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import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _a ( ) -> Dict:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(UpperCAmelCase ):
requests.request('''GET''' , '''https://huggingface.co''' )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request('''GET''' , '''https://huggingface.co''' , timeout=1.0 )
@pytest.mark.integration
def _a ( ) -> Dict:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request('''GET''' , '''https://huggingface.co''' )
def _a ( ) -> Optional[int]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(UpperCAmelCase ):
http_head('''https://huggingface.co''' )
| 315 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase : Any , UpperCamelCase : Optional[int]=2 , UpperCamelCase : str=True , UpperCamelCase : List[str]=False , UpperCamelCase : Tuple=10 , UpperCamelCase : Union[str, Any]=3 , UpperCamelCase : Tuple=32 * 4 , UpperCamelCase : Tuple=32 * 6 , UpperCamelCase : Optional[Any]=4 , UpperCamelCase : List[Any]=32 , ):
'''simple docstring'''
_snake_case : List[Any] = parent
_snake_case : Optional[Any] = batch_size
_snake_case : List[str] = is_training
_snake_case : Optional[int] = use_auxiliary_loss
_snake_case : Optional[Any] = num_queries
_snake_case : Any = num_channels
_snake_case : Union[str, Any] = min_size
_snake_case : Dict = max_size
_snake_case : str = num_labels
_snake_case : List[Any] = mask_feature_size
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_snake_case : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
UpperCamelCase )
_snake_case : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=UpperCamelCase )
_snake_case : List[str] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=UpperCamelCase ) > 0.5
).float()
_snake_case : Any = (torch.rand((self.batch_size, self.num_labels) , device=UpperCamelCase ) > 0.5).long()
_snake_case : List[Any] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case : Tuple = self.prepare_config_and_inputs()
_snake_case : Optional[Any] = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def UpperCamelCase_ ( self : Optional[Any] , UpperCamelCase : str , UpperCamelCase : Any ):
'''simple docstring'''
_snake_case : int = output.encoder_hidden_states
_snake_case : Tuple = output.pixel_decoder_hidden_states
_snake_case : Tuple = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(UpperCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(UpperCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(UpperCamelCase ) , config.decoder_config.decoder_layers )
def UpperCamelCase_ ( self : Any , UpperCamelCase : Tuple , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : str=False ):
'''simple docstring'''
with torch.no_grad():
_snake_case : str = MaskFormerModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
_snake_case : Tuple = model(pixel_values=UpperCamelCase , pixel_mask=UpperCamelCase )
_snake_case : str = model(UpperCamelCase , output_hidden_states=UpperCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(UpperCamelCase , UpperCamelCase )
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : str , UpperCamelCase : List[Any] , UpperCamelCase : Dict , UpperCamelCase : Tuple ):
'''simple docstring'''
_snake_case : str = MaskFormerForInstanceSegmentation(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
def comm_check_on_output(UpperCamelCase : Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_snake_case : Tuple = model(pixel_values=UpperCamelCase , pixel_mask=UpperCamelCase )
_snake_case : Optional[Any] = model(UpperCamelCase )
comm_check_on_output(UpperCamelCase )
_snake_case : Union[str, Any] = model(
pixel_values=UpperCamelCase , pixel_mask=UpperCamelCase , mask_labels=UpperCamelCase , class_labels=UpperCamelCase )
comm_check_on_output(UpperCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowerCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[Any] =(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
a_ : Tuple =(
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
a_ : Any =False
a_ : List[str] =False
a_ : List[str] =False
a_ : Optional[Any] =False
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : Tuple = MaskFormerModelTester(self )
_snake_case : Optional[int] = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(UpperCamelCase , **UpperCamelCase , output_hidden_states=UpperCamelCase )
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
_snake_case : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCamelCase )
@unittest.skip(reason='MaskFormer does not use inputs_embeds' )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method' )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip(reason='MaskFormer is not a generative model' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='MaskFormer does not use token embeddings' )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Optional[int] = model_class(UpperCamelCase )
_snake_case : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : Optional[Any] = [*signature.parameters.keys()]
_snake_case : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1] , UpperCamelCase )
@slow
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
for model_name in ["facebook/maskformer-swin-small-coco"]:
_snake_case : int = MaskFormerModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
_snake_case : Any = (self.model_tester.min_size,) * 2
_snake_case : Optional[int] = {
'pixel_values': torch.randn((2, 3, *size) , device=UpperCamelCase ),
'mask_labels': torch.randn((2, 10, *size) , device=UpperCamelCase ),
'class_labels': torch.zeros(2 , 10 , device=UpperCamelCase ).long(),
}
_snake_case : Optional[int] = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(UpperCamelCase )
_snake_case : Any = model(**UpperCamelCase )
self.assertTrue(outputs.loss is not None )
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
_snake_case , _snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(UpperCamelCase , **UpperCamelCase , output_hidden_states=UpperCamelCase )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case , _snake_case : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Union[str, Any] = model_class(UpperCamelCase ).to(UpperCamelCase )
_snake_case : Dict = model(**UpperCamelCase , output_attentions=UpperCamelCase )
self.assertTrue(outputs.attentions is not None )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_snake_case : Dict = self.all_model_classes[1]
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
_snake_case : int = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.train()
_snake_case : Optional[Any] = model(UpperCamelCase , mask_labels=UpperCamelCase , class_labels=UpperCamelCase ).loss
loss.backward()
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_snake_case : List[Any] = self.all_model_classes[1]
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case : str = self.model_tester.prepare_config_and_inputs()
_snake_case : List[str] = True
_snake_case : List[Any] = True
_snake_case : List[Any] = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.train()
_snake_case : Dict = model(UpperCamelCase , mask_labels=UpperCamelCase , class_labels=UpperCamelCase )
_snake_case : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_snake_case : Any = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_snake_case : List[str] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_snake_case : List[str] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=UpperCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
lowerCAmelCase_ = 1E-4
def lowerCamelCase_ ( )-> List[Any]:
_snake_case : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
return (
MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco' )
if is_vision_available()
else None
)
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Optional[Any] = MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco' ).to(UpperCamelCase )
_snake_case : Dict = self.default_image_processor
_snake_case : Tuple = prepare_img()
_snake_case : str = image_processor(UpperCamelCase , return_tensors='pt' ).to(UpperCamelCase )
_snake_case : int = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(UpperCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_snake_case : Union[str, Any] = model(**UpperCamelCase )
_snake_case : Tuple = torch.tensor(
[[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(UpperCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
_snake_case : Optional[int] = torch.tensor(
[[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(UpperCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
_snake_case : Optional[int] = torch.tensor(
[[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(UpperCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Optional[int] = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(UpperCamelCase )
.eval()
)
_snake_case : Any = self.default_image_processor
_snake_case : List[Any] = prepare_img()
_snake_case : List[str] = image_processor(UpperCamelCase , return_tensors='pt' ).to(UpperCamelCase )
_snake_case : List[Any] = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(UpperCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_snake_case : Optional[Any] = model(**UpperCamelCase )
# masks_queries_logits
_snake_case : Optional[int] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_snake_case : Any = [
[-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
[-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
[-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
]
_snake_case : str = torch.tensor(UpperCamelCase ).to(UpperCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
# class_queries_logits
_snake_case : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_snake_case : Tuple = torch.tensor(
[
[1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
[3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
[1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_snake_case : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff' )
.to(UpperCamelCase )
.eval()
)
_snake_case : int = self.default_image_processor
_snake_case : Optional[int] = prepare_img()
_snake_case : int = image_processor(UpperCamelCase , return_tensors='pt' ).to(UpperCamelCase )
_snake_case : Optional[Any] = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(UpperCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
_snake_case : List[Any] = model(**UpperCamelCase )
# masks_queries_logits
_snake_case : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_snake_case : List[Any] = [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
_snake_case : Union[str, Any] = torch.tensor(UpperCamelCase ).to(UpperCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
# class_queries_logits
_snake_case : Union[str, Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_snake_case : Union[str, Any] = torch.tensor(
[[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , UpperCamelCase , atol=UpperCamelCase ) )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_snake_case : Any = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(UpperCamelCase )
.eval()
)
_snake_case : Optional[int] = self.default_image_processor
_snake_case : Dict = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors='pt' , )
_snake_case : List[str] = inputs['pixel_values'].to(UpperCamelCase )
_snake_case : Tuple = [el.to(UpperCamelCase ) for el in inputs['mask_labels']]
_snake_case : Optional[int] = [el.to(UpperCamelCase ) for el in inputs['class_labels']]
with torch.no_grad():
_snake_case : List[str] = model(**UpperCamelCase )
self.assertTrue(outputs.loss is not None )
| 411 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_distilbert""": [
"""DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""DistilBertConfig""",
"""DistilBertOnnxConfig""",
],
"""tokenization_distilbert""": ["""DistilBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ["""DistilBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DistilBertForMaskedLM""",
"""DistilBertForMultipleChoice""",
"""DistilBertForQuestionAnswering""",
"""DistilBertForSequenceClassification""",
"""DistilBertForTokenClassification""",
"""DistilBertModel""",
"""DistilBertPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDistilBertForMaskedLM""",
"""TFDistilBertForMultipleChoice""",
"""TFDistilBertForQuestionAnswering""",
"""TFDistilBertForSequenceClassification""",
"""TFDistilBertForTokenClassification""",
"""TFDistilBertMainLayer""",
"""TFDistilBertModel""",
"""TFDistilBertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""FlaxDistilBertForMaskedLM""",
"""FlaxDistilBertForMultipleChoice""",
"""FlaxDistilBertForQuestionAnswering""",
"""FlaxDistilBertForSequenceClassification""",
"""FlaxDistilBertForTokenClassification""",
"""FlaxDistilBertModel""",
"""FlaxDistilBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 648 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowercase :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case=3 , __snake_case=32 , __snake_case=3 , __snake_case=10 , __snake_case=[8, 16, 32, 64] , __snake_case=[1, 1, 2, 1] , __snake_case=True , __snake_case=True , __snake_case="relu" , __snake_case=3 , __snake_case=None , __snake_case=["stage2", "stage3", "stage4"] , __snake_case=[2, 3, 4] , __snake_case=1 , ):
_UpperCamelCase : List[Any] = parent
_UpperCamelCase : Dict = batch_size
_UpperCamelCase : Optional[int] = image_size
_UpperCamelCase : str = num_channels
_UpperCamelCase : Optional[Any] = embeddings_size
_UpperCamelCase : Tuple = hidden_sizes
_UpperCamelCase : Dict = depths
_UpperCamelCase : str = is_training
_UpperCamelCase : Optional[int] = use_labels
_UpperCamelCase : str = hidden_act
_UpperCamelCase : Optional[int] = num_labels
_UpperCamelCase : Optional[int] = scope
_UpperCamelCase : Tuple = len(__snake_case)
_UpperCamelCase : Dict = out_features
_UpperCamelCase : Union[str, Any] = out_indices
_UpperCamelCase : int = num_groups
def A__ ( self):
_UpperCamelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase : str = None
if self.use_labels:
_UpperCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.num_labels)
_UpperCamelCase : str = self.get_config()
return config, pixel_values, labels
def A__ ( self):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def A__ ( self , __snake_case , __snake_case , __snake_case):
_UpperCamelCase : str = BitModel(config=__snake_case)
model.to(__snake_case)
model.eval()
_UpperCamelCase : Optional[Any] = model(__snake_case)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A__ ( self , __snake_case , __snake_case , __snake_case):
_UpperCamelCase : Dict = self.num_labels
_UpperCamelCase : Dict = BitForImageClassification(__snake_case)
model.to(__snake_case)
model.eval()
_UpperCamelCase : Dict = model(__snake_case , labels=__snake_case)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def A__ ( self , __snake_case , __snake_case , __snake_case):
_UpperCamelCase : Optional[Any] = BitBackbone(config=__snake_case)
model.to(__snake_case)
model.eval()
_UpperCamelCase : List[Any] = model(__snake_case)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])
# verify backbone works with out_features=None
_UpperCamelCase : Any = None
_UpperCamelCase : str = BitBackbone(config=__snake_case)
model.to(__snake_case)
model.eval()
_UpperCamelCase : Any = model(__snake_case)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def A__ ( self):
_UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : int = config_and_inputs
_UpperCamelCase : int = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( _lowercase , _lowercase , unittest.TestCase ):
"""simple docstring"""
a__ = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
a__ = (
{"feature-extraction": BitModel, "image-classification": BitForImageClassification}
if is_torch_available()
else {}
)
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def A__ ( self):
_UpperCamelCase : Dict = BitModelTester(self)
_UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case)
def A__ ( self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self):
return
@unittest.skip(reason='Bit does not output attentions')
def A__ ( self):
pass
@unittest.skip(reason='Bit does not use inputs_embeds')
def A__ ( self):
pass
@unittest.skip(reason='Bit does not support input and output embeddings')
def A__ ( self):
pass
def A__ ( self):
_UpperCamelCase , _UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : int = model_class(__snake_case)
_UpperCamelCase : List[Any] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase : Optional[int] = [*signature.parameters.keys()]
_UpperCamelCase : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , __snake_case)
def A__ ( self):
_UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case)
def A__ ( self):
_UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__snake_case)
def A__ ( self):
_UpperCamelCase , _UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : Union[str, Any] = model_class(config=__snake_case)
for name, module in model.named_modules():
if isinstance(__snake_case , (nn.BatchNormad, nn.GroupNorm)):
self.assertTrue(
torch.all(module.weight == 1) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def A__ ( self):
def check_hidden_states_output(__snake_case , __snake_case , __snake_case):
_UpperCamelCase : str = model_class(__snake_case)
model.to(__snake_case)
model.eval()
with torch.no_grad():
_UpperCamelCase : Union[str, Any] = model(**self._prepare_for_class(__snake_case , __snake_case))
_UpperCamelCase : List[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase : str = self.model_tester.num_stages
self.assertEqual(len(__snake_case) , expected_num_stages + 1)
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : List[str] = ['preactivation', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCamelCase : Any = layer_type
_UpperCamelCase : Tuple = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase : List[str] = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case)
@unittest.skip(reason='Bit does not use feedforward chunking')
def A__ ( self):
pass
def A__ ( self):
_UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case)
@slow
def A__ ( self):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase : Optional[Any] = BitModel.from_pretrained(__snake_case)
self.assertIsNotNone(__snake_case)
def lowerCamelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : List[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def A__ ( self):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
)
@slow
def A__ ( self):
_UpperCamelCase : Optional[Any] = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__snake_case)
_UpperCamelCase : str = self.default_image_processor
_UpperCamelCase : List[str] = prepare_img()
_UpperCamelCase : int = image_processor(images=__snake_case , return_tensors='pt').to(__snake_case)
# forward pass
with torch.no_grad():
_UpperCamelCase : Any = model(**__snake_case)
# verify the logits
_UpperCamelCase : Dict = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __snake_case)
_UpperCamelCase : Optional[int] = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(__snake_case)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __snake_case , atol=1e-4))
@require_torch
class lowercase ( _lowercase , unittest.TestCase ):
"""simple docstring"""
a__ = (BitBackbone,) if is_torch_available() else ()
a__ = BitConfig
a__ = False
def A__ ( self):
_UpperCamelCase : List[str] = BitModelTester(self)
| 648 | 1 |
def UpperCamelCase_ ( __a ) -> float:
return 10 - x * x
def UpperCamelCase_ ( __a , __a ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(__a ) * equation(__a ) >= 0:
raise ValueError("Wrong space!" )
a__ : Any = a
while (b - a) >= 0.01:
# Find middle point
a__ : str = (a + b) / 2
# Check if middle point is root
if equation(__a ) == 0.0:
break
# Decide the side to repeat the steps
if equation(__a ) * equation(__a ) < 0:
a__ : Dict = c
else:
a__ : Tuple = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 37 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
UpperCAmelCase =logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_ ) -> str:
super().__init__(*lowerCamelCase_ ,**lowerCamelCase_ )
self.check_model_type(lowerCamelCase_ )
def UpperCamelCase__ ( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_ ) -> List[Any]:
A , A = {}, {}
if padding is not None:
A = padding
if truncation is not None:
A = truncation
if top_k is not None:
A = top_k
return preprocess_params, {}, postprocess_params
def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_ ) -> Dict:
if isinstance(lowerCamelCase_ ,(Image.Image, str) ) and isinstance(lowerCamelCase_ ,lowerCamelCase_ ):
A = {"""image""": image, """question""": question}
else:
A = image
A = super().__call__(lowerCamelCase_ ,**lowerCamelCase_ )
return results
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=False ) -> List[Any]:
A = load_image(inputs["""image"""] )
A = self.tokenizer(
inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_ )
A = self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework )
model_inputs.update(lowerCamelCase_ )
return model_inputs
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> Union[str, Any]:
A = self.model(**lowerCamelCase_ )
return model_outputs
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_=5 ) -> int:
if top_k > self.model.config.num_labels:
A = self.model.config.num_labels
if self.framework == "pt":
A = model_outputs.logits.sigmoid()[0]
A , A = probs.topk(lowerCamelCase_ )
else:
raise ValueError(f'Unsupported framework: {self.framework}' )
A = scores.tolist()
A = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase_ ,lowerCamelCase_ )]
| 617 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Optional[Any] = {key: len(_lowercase ) for key, value in gen_kwargs.items() if isinstance(_lowercase , _lowercase )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"""Sharding is ambiguous for this dataset: """
+ """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
+ """\n""".join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items() )
+ """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
+ """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
) )
UpperCAmelCase : Dict = max(lists_lengths.values() , default=0 )
return max(1 , _lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[range]:
UpperCAmelCase : Dict = []
for group_idx in range(_lowercase ):
UpperCAmelCase : Tuple = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
UpperCAmelCase : int = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
UpperCAmelCase : List[str] = range(_lowercase , start + num_shards_to_add )
shards_indices_per_group.append(_lowercase )
return shards_indices_per_group
def __lowerCamelCase ( _lowercase , _lowercase ) -> List[dict]:
UpperCAmelCase : List[Any] = _number_of_shards_in_gen_kwargs(_lowercase )
if num_shards == 1:
return [dict(_lowercase )]
else:
UpperCAmelCase : int = _distribute_shards(num_shards=_lowercase , max_num_jobs=_lowercase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(_lowercase , _lowercase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(_lowercase ) )
]
def __lowerCamelCase ( _lowercase ) -> dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , _lowercase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def __lowerCamelCase ( _lowercase , _lowercase ) -> dict:
UpperCAmelCase : str = {len(_lowercase ) for value in gen_kwargs.values() if isinstance(_lowercase , _lowercase )}
UpperCAmelCase : int = {}
for size in list_sizes:
UpperCAmelCase : int = list(range(_lowercase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
UpperCAmelCase : List[Any] = dict(_lowercase )
for key, value in shuffled_kwargs.items():
if isinstance(_lowercase , _lowercase ):
UpperCAmelCase : Dict = [value[i] for i in indices_per_size[len(_lowercase )]]
return shuffled_kwargs
| 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase ( ) -> Dict:
UpperCAmelCase : str = Github(os.environ["""GITHUB_TOKEN"""] )
UpperCAmelCase : Dict = g.get_repo("""huggingface/transformers""" )
UpperCAmelCase : int = repo.get_issues(state="""open""" )
for issue in open_issues:
UpperCAmelCase : Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda _lowercase : i.created_at , reverse=_lowercase )
UpperCAmelCase : Any = comments[0] if len(_lowercase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 2_3
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 672 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class a :
def __init__( self , _snake_case , _snake_case=99 , _snake_case=13 , _snake_case=7 , _snake_case=9 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case=8 , _snake_case=0.1 , _snake_case=0.002 , _snake_case=1 , _snake_case=0 , _snake_case=0 , _snake_case=None , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = encoder_seq_length
lowerCAmelCase = decoder_seq_length
# For common tests
lowerCAmelCase = self.decoder_seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_attention_mask
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = d_ff
lowerCAmelCase = relative_attention_num_buckets
lowerCAmelCase = dropout_rate
lowerCAmelCase = initializer_factor
lowerCAmelCase = eos_token_id
lowerCAmelCase = pad_token_id
lowerCAmelCase = decoder_start_token_id
lowerCAmelCase = None
lowerCAmelCase = decoder_layers
def UpperCamelCase__ ( self ):
"""simple docstring"""
return TaConfig.from_pretrained('google/umt5-base' )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , ):
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_snake_case )
if decoder_head_mask is None:
lowerCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_snake_case )
if cross_attn_head_mask is None:
lowerCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_snake_case )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase = self.get_config()
lowerCAmelCase = config.num_attention_heads
lowerCAmelCase = self.prepare_inputs_dict(_snake_case , _snake_case , _snake_case )
return config, input_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase ,lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
return TaConfig(
vocab_size=1_66 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = UMTaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(
input_ids=_snake_case , decoder_input_ids=_snake_case , attention_mask=_snake_case , decoder_attention_mask=_snake_case , )
lowerCAmelCase = model(input_ids=_snake_case , decoder_input_ids=_snake_case )
lowerCAmelCase = result.last_hidden_state
lowerCAmelCase = result.past_key_values
lowerCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_snake_case ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = UMTaModel(config=_snake_case ).get_decoder().to(_snake_case ).eval()
# first forward pass
lowerCAmelCase = model(_snake_case , use_cache=_snake_case )
lowerCAmelCase = model(_snake_case )
lowerCAmelCase = model(_snake_case , use_cache=_snake_case )
self.parent.assertTrue(len(_snake_case ) == len(_snake_case ) )
self.parent.assertTrue(len(_snake_case ) == len(_snake_case ) + 1 )
lowerCAmelCase ,lowerCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase = model(_snake_case )['last_hidden_state']
lowerCAmelCase = model(_snake_case , past_key_values=_snake_case )['last_hidden_state']
# select random slice
lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
lowerCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_snake_case , _snake_case , atol=1E-3 ) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , ):
"""simple docstring"""
lowerCAmelCase = UMTaModel(config=_snake_case ).to(_snake_case ).half().eval()
lowerCAmelCase = model(**_snake_case )['last_hidden_state']
self.parent.assertFalse(torch.isnan(_snake_case ).any().item() )
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
snake_case__ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
snake_case__ = (
{
'''conversational''': UMTaForConditionalGeneration,
'''feature-extraction''': UMTaModel,
'''summarization''': UMTaForConditionalGeneration,
'''text2text-generation''': UMTaForConditionalGeneration,
'''translation''': UMTaForConditionalGeneration,
'''question-answering''': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
snake_case__ = True
snake_case__ = False
snake_case__ = False
snake_case__ = True
snake_case__ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
snake_case__ = [0.8, 0.9]
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = UMTaModelTester(self )
@unittest.skip('Test has a segmentation fault on torch 1.8.0' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase = UMTaModel(config_and_inputs[0] ).to(_snake_case )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_snake_case , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=_snake_case , opset_version=9 , input_names=['input_ids', 'decoder_input_ids'] , )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase = config_and_inputs[0]
lowerCAmelCase = UMTaForConditionalGeneration(_snake_case ).eval()
model.to(_snake_case )
lowerCAmelCase = {
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=_snake_case ),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_snake_case ),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_snake_case ),
}
for attn_name, (name, mask) in zip(_snake_case , head_masking.items() ):
lowerCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
lowerCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=_snake_case )
lowerCAmelCase = model.generate(
config_and_inputs[1]['input_ids'] , num_beams=1 , max_length=3 , output_attentions=_snake_case , return_dict_in_generate=_snake_case , **_snake_case , )
# We check the state of decoder_attentions and cross_attentions just from the last step
lowerCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
@slow
@unittest.skip(
'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = UMTaForConditionalGeneration.from_pretrained('google/umt5-small' , return_dict=_snake_case ).to(_snake_case )
lowerCAmelCase = AutoTokenizer.from_pretrained('google/umt5-small' , use_fast=_snake_case , legacy=_snake_case )
lowerCAmelCase = [
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
lowerCAmelCase = tokenizer(_snake_case , return_tensors='pt' , padding=_snake_case ).input_ids
# fmt: off
lowerCAmelCase = torch.tensor(
[
[ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
] )
# fmt: on
torch.testing.assert_allclose(_snake_case , _snake_case )
lowerCAmelCase = model.generate(input_ids.to(_snake_case ) )
lowerCAmelCase = [
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
lowerCAmelCase = tokenizer.batch_decode(_snake_case )
self.assertEqual(_snake_case , _snake_case )
| 4 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
# See all ViT models at https://huggingface.co/models?filter=vit
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''vit'''
def __init__( self : List[str] , _UpperCAmelCase : Optional[int]=768 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : Dict=12 , _UpperCAmelCase : int=3072 , _UpperCAmelCase : Optional[Any]="gelu" , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : int=1e-12 , _UpperCAmelCase : List[str]=224 , _UpperCAmelCase : Tuple=16 , _UpperCAmelCase : Optional[Any]=3 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Optional[int]=16 , **_UpperCAmelCase : List[str] , ) -> List[str]:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = qkv_bias
UpperCAmelCase_ = encoder_stride
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = version.parse('''1.11''' )
@property
def lowercase__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowercase__ ( self : Union[str, Any] ) -> float:
'''simple docstring'''
return 1e-4
| 82 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def lowercase_ ( lowercase__ = 8 , lowercase__ = None ) ->str:
_snake_case: Union[str, Any] = np.random.default_rng(seed=lowercase__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
_snake_case: Tuple = 6 * key_len
# Measurement basis for Alice's qubits.
_snake_case: List[str] = rng.integers(2 , size=lowercase__ )
# The set of states Alice will prepare.
_snake_case: List[Any] = rng.integers(2 , size=lowercase__ )
# Measurement basis for Bob's qubits.
_snake_case: str = rng.integers(2 , size=lowercase__ )
# Quantum Circuit to simulate BB84
_snake_case: List[Any] = qiskit.QuantumCircuit(lowercase__ , name='BB84' )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(lowercase__ ):
if alice_state[index] == 1:
bbaa_circ.x(lowercase__ )
if alice_basis[index] == 1:
bbaa_circ.h(lowercase__ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(lowercase__ ):
if bob_basis[index] == 1:
bbaa_circ.h(lowercase__ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
_snake_case: str = qiskit.Aer.get_backend('aer_simulator' )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
_snake_case: List[Any] = qiskit.execute(lowercase__ , lowercase__ , shots=1 , seed_simulator=lowercase__ )
# Returns the result of measurement.
_snake_case: str = job.result().get_counts(lowercase__ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
_snake_case: List[Any] = ''.join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
lowercase__ , lowercase__ , lowercase__ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
_snake_case: List[str] = gen_key[:key_len] if len(lowercase__ ) >= key_len else gen_key.ljust(lowercase__ , '0' )
return key
if __name__ == "__main__":
print(F'The generated key is : {bbaa(8, seed=0)}')
from doctest import testmod
testmod()
| 273 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A : Dict = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
A : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 273 | 1 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case = get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class __A ( snake_case__ ,unittest.TestCase ):
'''simple docstring'''
a_ = AlbertTokenizer
a_ = AlbertTokenizerFast
a_ = True
a_ = True
a_ = True
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase : str = AlbertTokenizer(_snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : Any = "this is a test"
_lowerCAmelCase : Tuple = "this is a test"
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Tuple = "<pad>"
_lowerCAmelCase : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case ) , _snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case ) , _snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "▁eloquent" )
self.assertEqual(len(_snake_case ) , 3_0000 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCAmelCase : Tuple = self.get_tokenizer()
_lowerCAmelCase : Optional[Any] = self.get_rust_tokenizer()
_lowerCAmelCase : List[Any] = "I was born in 92000, and this is falsé."
_lowerCAmelCase : Any = tokenizer.tokenize(_snake_case )
_lowerCAmelCase : str = rust_tokenizer.tokenize(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
_lowerCAmelCase : Any = tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
_lowerCAmelCase : Optional[int] = rust_tokenizer.encode(_snake_case , add_special_tokens=_snake_case )
self.assertListEqual(_snake_case , _snake_case )
_lowerCAmelCase : Dict = self.get_rust_tokenizer()
_lowerCAmelCase : List[str] = tokenizer.encode(_snake_case )
_lowerCAmelCase : Tuple = rust_tokenizer.encode(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Any = AlbertTokenizer(_snake_case , keep_accents=_snake_case )
_lowerCAmelCase : Any = tokenizer.tokenize("This is a test" )
self.assertListEqual(_snake_case , ["▁this", "▁is", "▁a", "▁test"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) , [48, 25, 21, 1289] )
_lowerCAmelCase : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
_snake_case , ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."] )
_lowerCAmelCase : Tuple = tokenizer.convert_tokens_to_ids(_snake_case )
self.assertListEqual(_snake_case , [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )
_lowerCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(_snake_case )
self.assertListEqual(
_snake_case , ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."] , )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Dict = AlbertTokenizer(_snake_case )
_lowerCAmelCase : Union[str, Any] = tokenizer.encode("sequence builders" )
_lowerCAmelCase : Optional[int] = tokenizer.encode("multi-sequence build" )
_lowerCAmelCase : Tuple = tokenizer.build_inputs_with_special_tokens(_snake_case )
_lowerCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(_snake_case , _snake_case )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# fmt: off
_lowerCAmelCase : Optional[int] = {"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_snake_case , model_name="albert-base-v2" , revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e" , )
| 424 | from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 424 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCAmelCase :
def __init__( self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=32 , _lowerCamelCase=3 , _lowerCamelCase=4 , _lowerCamelCase=[10, 20, 30, 40] , _lowerCamelCase=[2, 2, 3, 2] , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=37 , _lowerCamelCase="gelu" , _lowerCamelCase=10 , _lowerCamelCase=0.02 , _lowerCamelCase=["stage2", "stage3", "stage4"] , _lowerCamelCase=3 , _lowerCamelCase=None , ):
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = image_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = num_stages
lowerCAmelCase_ = hidden_sizes
lowerCAmelCase_ = depths
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = hidden_act
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = out_features
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = scope
lowerCAmelCase_ = num_stages
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ):
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase_ ( self ):
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=_lowerCamelCase , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=_lowerCamelCase , loss_ignore_index=255 , num_labels=self.num_labels , )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = UperNetForSemanticSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
lowerCAmelCase_ = model(_lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,
) = config_and_inputs
lowerCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( __a , __a , unittest.TestCase ):
__A : Dict = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
__A : List[Any] = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
__A : Tuple = False
__A : Union[str, Any] = False
__A : List[str] = False
__A : Dict = False
__A : Union[str, Any] = False
__A : Dict = False
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = UperNetModelTester(self )
lowerCAmelCase_ = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase_ ( self ):
return
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ ,lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(_lowerCamelCase )
lowerCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ = [*signature.parameters.keys()]
lowerCAmelCase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase )
@unittest.skip(reason='''UperNet does not use inputs_embeds''' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def UpperCAmelCase_ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def UpperCAmelCase_ ( self ):
pass
def UpperCAmelCase_ ( self ):
def check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase_ = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
lowerCAmelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCAmelCase_ ,lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ ,lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ = _config_zero_init(_lowerCamelCase )
lowerCAmelCase_ = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
lowerCAmelCase_ = model_class(config=_lowerCamelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''' )
def UpperCAmelCase_ ( self ):
pass
@slow
def UpperCAmelCase_ ( self ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = UperNetForSemanticSegmentation.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def snake_case_ ( ) -> Any:
lowerCAmelCase_ = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''' , repo_type='''dataset''' , filename='''ADE_val_00000001.jpg''')
lowerCAmelCase_ = Image.open(__snake_case).convert('''RGB''')
return image
@require_torch
@require_vision
@slow
class __UpperCAmelCase ( unittest.TestCase ):
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''' )
lowerCAmelCase_ = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''' ).to(_lowerCamelCase )
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
with torch.no_grad():
lowerCAmelCase_ = model(**_lowerCamelCase )
lowerCAmelCase_ = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
lowerCAmelCase_ = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''' )
lowerCAmelCase_ = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''' ).to(_lowerCamelCase )
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
with torch.no_grad():
lowerCAmelCase_ = model(**_lowerCamelCase )
lowerCAmelCase_ = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
lowerCAmelCase_ = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , _lowerCamelCase , atol=1E-4 ) )
| 606 | '''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def snake_case_ ( __snake_case : Optional[Any]) -> Union[str, Any]:
lowerCAmelCase_ = [False] * len(__snake_case)
lowerCAmelCase_ = [-1] * len(__snake_case)
def dfs(__snake_case : str , __snake_case : Any):
lowerCAmelCase_ = True
lowerCAmelCase_ = c
for u in graph[v]:
if not visited[u]:
dfs(__snake_case , 1 - c)
for i in range(len(__snake_case)):
if not visited[i]:
dfs(__snake_case , 0)
for i in range(len(__snake_case)):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
A_ : Optional[Any] ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 606 | 1 |
import string
def UpperCAmelCase ( a_ ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__A = ""
for symbol in message:
if symbol in string.ascii_uppercase:
__A = string.ascii_uppercase.find(a_ )
__A = num - key
if num < 0:
__A = num + len(string.ascii_uppercase )
__A = translated + string.ascii_uppercase[num]
else:
__A = translated + symbol
print(F'''Decryption using Key #{key}: {translated}''' )
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = input("Encrypted message: " )
__A = message.upper()
decrypt(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 55 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
lowerCAmelCase : List[Any] = ["""text""", """image""", """audio"""]
def _A ( A ) -> Dict:
lowercase : str = []
for input_type in input_types:
if input_type == "text":
inputs.append("Text input" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png" ).resize((5_1_2, 5_1_2) ) )
elif input_type == "audio":
inputs.append(torch.ones(3_0_0_0 ) )
elif isinstance(A ,A ):
inputs.append(create_inputs(A ) )
else:
raise ValueError(F'''Invalid type requested: {input_type}''' )
return inputs
def _A ( A ) -> str:
lowercase : Tuple = []
for output in outputs:
if isinstance(A ,(str, AgentText) ):
output_types.append("text" )
elif isinstance(A ,(Image.Image, AgentImage) ):
output_types.append("image" )
elif isinstance(A ,(torch.Tensor, AgentAudio) ):
output_types.append("audio" )
else:
raise ValueError(F'''Invalid output: {output}''' )
return output_types
@is_tool_test
class _UpperCamelCase :
'''simple docstring'''
def a__ ( self ) -> Optional[Any]:
self.assertTrue(hasattr(self.tool , "inputs" ) )
self.assertTrue(hasattr(self.tool , "outputs" ) )
lowercase : Optional[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input , a_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
lowercase : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def a__ ( self ) -> Any:
lowercase : Any = create_inputs(self.tool.inputs )
lowercase : Tuple = self.tool(*a_ )
# There is a single output
if len(self.tool.outputs ) == 1:
lowercase : Any = [outputs]
self.assertListEqual(output_types(a_ ) , self.tool.outputs )
def a__ ( self ) -> List[str]:
self.assertTrue(hasattr(self.tool , "description" ) )
self.assertTrue(hasattr(self.tool , "default_checkpoint" ) )
self.assertTrue(self.tool.description.startswith("This is a tool that" ) )
def a__ ( self ) -> int:
lowercase : str = create_inputs(self.tool.inputs )
lowercase : str = self.tool(*a_ )
if not isinstance(a_ , a_ ):
lowercase : Union[str, Any] = [outputs]
self.assertEqual(len(a_ ) , len(self.tool.outputs ) )
for output, output_type in zip(a_ , self.tool.outputs ):
lowercase : List[str] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(a_ , a_ ) )
def a__ ( self ) -> Optional[int]:
lowercase : int = create_inputs(self.tool.inputs )
lowercase : str = []
for _input, input_type in zip(a_ , self.tool.inputs ):
if isinstance(a_ , a_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
lowercase : Optional[int] = self.tool(*a_ )
if not isinstance(a_ , a_ ):
lowercase : str = [outputs]
self.assertEqual(len(a_ ) , len(self.tool.outputs ) )
| 372 | 0 |
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_A = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class __UpperCAmelCase :
"""simple docstring"""
_snake_case : str
_snake_case : Optional[str] = None
_snake_case : Optional[Union[str, int]] = None
_snake_case : Optional[Union[str, int]] = None
_snake_case : Optional[Union[str, int]] = None
def A ( self : Dict )-> Optional[int]:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = _str_to_version_tuple(self.version_str )
def __repr__( self : Optional[int] )-> int:
return f"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""
@property
def A ( self : Any )-> List[str]:
return self.major, self.minor, self.patch
def A ( self : str , A_ : List[str] )-> List[str]:
if isinstance(A_ , A_ ):
return Version(A_ )
elif isinstance(A_ , A_ ):
return other
raise TypeError(f"""{other} (type {type(A_ )}) cannot be compared to version.""" )
def __eq__( self : Optional[int] , A_ : List[str] )-> List[Any]:
try:
__UpperCamelCase = self._validate_operand(A_ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : str , A_ : Dict )-> Tuple:
__UpperCamelCase = self._validate_operand(A_ )
return self.tuple < other.tuple
def __hash__( self : Any )-> Optional[int]:
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def A ( cls : str , A_ : Dict )-> Tuple:
__UpperCamelCase = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def A ( self : Optional[Any] )-> str:
return self.version_str
def lowercase (_snake_case ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase = _VERSION_REG.match(_snake_case )
if not res:
raise ValueError(f"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits.""" )
return tuple(int(_snake_case ) for v in [res.group("major" ), res.group("minor" ), res.group("patch" )] )
def lowercase (_snake_case ) -> Optional[Any]:
'''simple docstring'''
return ".".join(str(_snake_case ) for v in version_tuple ) | 711 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : Union[List[PIL.Image.Image], np.ndarray]
_snake_case : Optional[List[bool]]
_snake_case : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker | 228 | 0 |
'''simple docstring'''
def _lowerCamelCase (__lowerCamelCase : Optional[Any] = 1000 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 489 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> str:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined for non-natural numbers""" )
_SCREAMING_SNAKE_CASE : List[Any] = precision
_SCREAMING_SNAKE_CASE : List[str] = ceil(precision / 14 )
_SCREAMING_SNAKE_CASE : List[str] = 426_880 * Decimal(10_005 ).sqrt()
_SCREAMING_SNAKE_CASE : str = 1
_SCREAMING_SNAKE_CASE : List[str] = 13_591_409
_SCREAMING_SNAKE_CASE : str = Decimal(__SCREAMING_SNAKE_CASE )
for k in range(1 , __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Dict = factorial(6 * k ) // (factorial(3 * k ) * factorial(__SCREAMING_SNAKE_CASE ) ** 3)
linear_term += 545_140_134
exponential_term *= -262_537_412_640_768_000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
lowerCAmelCase_ = 50
print(F"The first {n} digits of pi is: {pi(n)}")
| 338 | 0 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
__lowercase = {
'''b0''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1_280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1_408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1_536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1_792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2_048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2_304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2_560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def lowerCAmelCase (__UpperCamelCase : Any ):
"""simple docstring"""
__UpperCamelCase =EfficientNetConfig()
__UpperCamelCase =CONFIG_MAP[model_name]['''hidden_dim''']
__UpperCamelCase =CONFIG_MAP[model_name]['''width_coef''']
__UpperCamelCase =CONFIG_MAP[model_name]['''depth_coef''']
__UpperCamelCase =CONFIG_MAP[model_name]['''image_size''']
__UpperCamelCase =CONFIG_MAP[model_name]['''dropout_rate''']
__UpperCamelCase =CONFIG_MAP[model_name]['''dw_padding''']
__UpperCamelCase ='''huggingface/label-files'''
__UpperCamelCase ='''imagenet-1k-id2label.json'''
__UpperCamelCase =1_0_0_0
__UpperCamelCase =json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
__UpperCamelCase ={int(__UpperCamelCase ): v for k, v in idalabel.items()}
__UpperCamelCase =idalabel
__UpperCamelCase ={v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase ():
"""simple docstring"""
__UpperCamelCase ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCamelCase =Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
def lowerCAmelCase (__UpperCamelCase : str ):
"""simple docstring"""
__UpperCamelCase =CONFIG_MAP[model_name]['''image_size''']
__UpperCamelCase =EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3] , do_center_crop=__UpperCamelCase , )
return preprocessor
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =[v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
__UpperCamelCase =sorted(set(__UpperCamelCase ) )
__UpperCamelCase =len(__UpperCamelCase )
__UpperCamelCase ={b: str(__UpperCamelCase ) for b, i in zip(__UpperCamelCase , range(__UpperCamelCase ) )}
__UpperCamelCase =[]
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
__UpperCamelCase =block_name_mapping[b]
rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
rename_keys.append(
(F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
rename_keys.append(
(F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
rename_keys.append(
(F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )
rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
rename_keys.append(
(F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
rename_keys.append(
(F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
__UpperCamelCase ={}
for item in rename_keys:
if item[0] in original_param_names:
__UpperCamelCase ='''efficientnet.''' + item[1]
__UpperCamelCase ='''classifier.weight'''
__UpperCamelCase ='''classifier.bias'''
return key_mapping
def lowerCAmelCase (__UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : Dict ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
__UpperCamelCase =key_mapping[key]
if "_conv" in key and "kernel" in key:
__UpperCamelCase =torch.from_numpy(__UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__UpperCamelCase =torch.from_numpy(__UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__UpperCamelCase =torch.from_numpy(np.transpose(__UpperCamelCase ) )
else:
__UpperCamelCase =torch.from_numpy(__UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__UpperCamelCase )
@torch.no_grad()
def lowerCAmelCase (__UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : Any ):
"""simple docstring"""
__UpperCamelCase =model_classes[model_name](
include_top=__UpperCamelCase , weights='''imagenet''' , input_tensor=__UpperCamelCase , input_shape=__UpperCamelCase , pooling=__UpperCamelCase , classes=1_0_0_0 , classifier_activation='''softmax''' , )
__UpperCamelCase =original_model.trainable_variables
__UpperCamelCase =original_model.non_trainable_variables
__UpperCamelCase ={param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__UpperCamelCase =param.numpy()
__UpperCamelCase =list(tf_params.keys() )
# Load HuggingFace model
__UpperCamelCase =get_efficientnet_config(__UpperCamelCase )
__UpperCamelCase =EfficientNetForImageClassification(__UpperCamelCase ).eval()
__UpperCamelCase =hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
__UpperCamelCase =rename_keys(__UpperCamelCase )
replace_params(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Initialize preprocessor and preprocess input image
__UpperCamelCase =convert_image_processor(__UpperCamelCase )
__UpperCamelCase =preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
__UpperCamelCase =hf_model(**__UpperCamelCase )
__UpperCamelCase =outputs.logits.detach().numpy()
# Original model inference
__UpperCamelCase =False
__UpperCamelCase =CONFIG_MAP[model_name]['''image_size''']
__UpperCamelCase =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__UpperCamelCase =image.img_to_array(__UpperCamelCase )
__UpperCamelCase =np.expand_dims(__UpperCamelCase , axis=0 )
__UpperCamelCase =original_model.predict(__UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(__UpperCamelCase ):
os.mkdir(__UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(__UpperCamelCase )
preprocessor.save_pretrained(__UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(F"""Pushing converted {model_name} to the hub...""" )
__UpperCamelCase =F"""efficientnet-{model_name}"""
preprocessor.push_to_hub(__UpperCamelCase )
hf_model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
__lowercase = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 711 | """simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
__lowercase = logging.getLogger()
def lowerCAmelCase (__UpperCamelCase : Path , __UpperCamelCase : list ):
"""simple docstring"""
__UpperCamelCase ='''\n'''.join(__UpperCamelCase )
Path(__UpperCamelCase ).open('''w''' ).writelines(__UpperCamelCase )
__lowercase = '''patrickvonplaten/t5-tiny-random'''
__lowercase = '''sshleifer/bart-tiny-random'''
__lowercase = '''sshleifer/tiny-mbart'''
__lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class _lowercase ( __a ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Dict , UpperCamelCase__ : int ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
__UpperCamelCase =input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
__UpperCamelCase =[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
_dump_articles(UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =str(Path(self.get_auto_remove_tmp_dir() ) / '''scores.json''' )
__UpperCamelCase ='''translation_en_to_de''' if model == T5_TINY else '''summarization'''
__UpperCamelCase =f"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(UpperCamelCase__ , '''argv''' , UpperCamelCase__ ):
run_generate()
assert Path(UpperCamelCase__ ).exists()
# os.remove(Path(output_file_name))
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
self.run_eval_tester(UpperCamelCase__ )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] ) -> int:
'''simple docstring'''
self.run_eval_tester(UpperCamelCase__ )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : Any ) -> Any:
'''simple docstring'''
__UpperCamelCase =Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
__UpperCamelCase =input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
__UpperCamelCase ={
'''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
'''de''': [
'''Maschinelles Lernen ist großartig, oder?''',
'''Ich esse gerne Bananen''',
'''Morgen ist wieder ein toller Tag!''',
],
}
__UpperCamelCase =Path(self.get_auto_remove_tmp_dir() )
__UpperCamelCase =str(tmp_dir / '''scores.json''' )
__UpperCamelCase =str(tmp_dir / '''val.target''' )
_dump_articles(UpperCamelCase__ , text['''en'''] )
_dump_articles(UpperCamelCase__ , text['''de'''] )
__UpperCamelCase ='''translation_en_to_de''' if model == T5_TINY else '''summarization'''
__UpperCamelCase =f"""
run_eval_search.py
{model}
{str(UpperCamelCase__ )}
{str(UpperCamelCase__ )}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''] )
with patch.object(UpperCamelCase__ , '''argv''' , UpperCamelCase__ ):
with CaptureStdout() as cs:
run_search()
__UpperCamelCase =[''' num_beams | length_penalty''', model, '''Best score args''']
__UpperCamelCase =['''Info''']
if "translation" in task:
expected_strings.append('''bleu''' )
else:
expected_strings.extend(UpperCamelCase__ )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(UpperCamelCase__ ).exists()
os.remove(Path(UpperCamelCase__ ) )
| 296 | 0 |
_lowercase = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_lowercase = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
_lowercase = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
_lowercase = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
_lowercase = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
_lowercase = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
_lowercase = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
_lowercase = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 659 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 659 | 1 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
lowercase__ : str = [
"cross_validation.py",
"gradient_accumulation.py",
"local_sgd.py",
"multi_process_metrics.py",
"memory.py",
"automatic_gradient_accumulation.py",
"fsdp_with_peak_mem_tracking.py",
"deepspeed_with_config_support.py",
"megatron_lm_gpt_pretraining.py",
]
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : list = None ):
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : List[str] = os.path.abspath(os.path.join('examples' , 'by_feature' ) )
lowerCAmelCase_ : int = os.path.abspath('examples' )
for item in os.listdir(__UpperCamelCase ):
if item not in EXCLUDE_EXAMPLES:
lowerCAmelCase_ : int = os.path.join(__UpperCamelCase , __UpperCamelCase )
if os.path.isfile(__UpperCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=__UpperCamelCase , feature_script=__UpperCamelCase , tested_section='main()' if parser_only else 'training_function()' , ):
lowerCAmelCase_ : Dict = compare_against_test(
os.path.join(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
lowerCAmelCase_ : Any = '\n'.join(__UpperCamelCase )
if special_strings is not None:
for string in special_strings:
lowerCAmelCase_ : Tuple = diff.replace(__UpperCamelCase , '' )
self.assertEqual(__UpperCamelCase , '' )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
self.one_complete_example('complete_nlp_example.py' , __UpperCamelCase )
self.one_complete_example('complete_nlp_example.py' , __UpperCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : List[str] = os.path.abspath(os.path.join('examples' , 'cv_example.py' ) )
lowerCAmelCase_ : Union[str, Any] = [
' ' * 1_6 + '{\n\n',
' ' * 2_0 + '"accuracy": eval_metric["accuracy"],\n\n',
' ' * 2_0 + '"f1": eval_metric["f1"],\n\n',
' ' * 2_0 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
' ' * 2_0 + '"epoch": epoch,\n\n',
' ' * 1_6 + '},\n\n',
' ' * 1_6 + 'step=epoch,\n',
' ' * 1_2,
' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
]
self.one_complete_example('complete_cv_example.py' , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
self.one_complete_example('complete_cv_example.py' , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
@mock.patch.dict(os.environ, {"""TESTING_MOCKED_DATALOADERS""": """1"""} )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = False
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] ):
super().setUpClass()
lowerCAmelCase_ : Tuple = tempfile.mkdtemp()
lowerCAmelCase_ : str = os.path.join(cls._tmpdir , 'default_config.yml' )
write_basic_config(save_location=cls.configPath )
lowerCAmelCase_ : List[Any] = ['accelerate', 'launch', '--config_file', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : Any = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , 'epoch_0' ) ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : Optional[int] = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n ".split()
lowerCAmelCase_ : Dict = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , 'step_2' ) ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : Any = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}\n ".split()
lowerCAmelCase_ : Optional[Any] = run_command(self._launch_args + testargs , return_stdout=__UpperCamelCase )
self.assertNotIn('epoch 0:' , __UpperCamelCase )
self.assertIn('epoch 1:' , __UpperCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : Dict = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}\n ".split()
lowerCAmelCase_ : int = run_command(self._launch_args + testargs , return_stdout=__UpperCamelCase )
if torch.cuda.is_available():
lowerCAmelCase_ : Any = torch.cuda.device_count()
else:
lowerCAmelCase_ : str = 1
if num_processes > 1:
self.assertNotIn('epoch 0:' , __UpperCamelCase )
self.assertIn('epoch 1:' , __UpperCamelCase )
else:
self.assertIn('epoch 0:' , __UpperCamelCase )
self.assertIn('epoch 1:' , __UpperCamelCase )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
lowerCAmelCase_ : Dict = '\n examples/by_feature/cross_validation.py\n --num_folds 2\n '.split()
with mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '0'} ):
lowerCAmelCase_ : Tuple = run_command(self._launch_args + testargs , return_stdout=__UpperCamelCase )
lowerCAmelCase_ : Optional[Any] = re.findall('({.+})' , __UpperCamelCase )
lowerCAmelCase_ : Tuple = [r for r in results if 'accuracy' in r][-1]
lowerCAmelCase_ : Tuple = ast.literal_eval(__UpperCamelCase )
self.assertGreaterEqual(results['accuracy'] , 0.75 )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : Tuple = ['examples/by_feature/multi_process_metrics.py']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'WANDB_MODE': 'offline'} )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdir:
lowerCAmelCase_ : Any = F"\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase , 'tracking' ) ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
lowerCAmelCase_ : Optional[int] = ['examples/by_feature/gradient_accumulation.py']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : Any = ['examples/by_feature/local_sgd.py']
run_command(self._launch_args + testargs )
| 707 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
lowercase__ : Optional[Any] = get_logger(__name__)
lowercase__ : Tuple = Path(__file__).parent / """model_card_template.md"""
lowercase__ : Optional[Any] = uuida().hex
lowercase__ : Any = os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase__ : Tuple = os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase__ : Dict = HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def UpperCamelCase_ ( lowerCAmelCase__ : Union[Dict, str, None] = None ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f"; torch/{_torch_version}"
if is_flax_available():
ua += f"; jax/{_jax_version}"
ua += f"; flax/{_flax_version}"
if is_onnx_available():
ua += f"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get('DIFFUSERS_IS_CI' , '' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items() )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
ua += "; " + user_agent
return ua
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : Optional[str] = None ) -> Union[str, Any]:
"""simple docstring"""
if token is None:
lowerCAmelCase_ : Any = HfFolder.get_token()
if organization is None:
lowerCAmelCase_ : Union[str, Any] = whoami(lowerCAmelCase__ )['name']
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if not is_jinja_available():
raise ValueError(
'Modelcard rendering is based on Jinja templates.'
' Please make sure to have `jinja` installed before using `create_model_card`.'
' To install it, please run `pip install Jinja2`.' )
if hasattr(lowerCAmelCase__ , 'local_rank' ) and args.local_rank not in [-1, 0]:
return
lowerCAmelCase_ : List[str] = args.hub_token if hasattr(lowerCAmelCase__ , 'hub_token' ) else None
lowerCAmelCase_ : List[Any] = get_full_repo_name(lowerCAmelCase__ , token=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='en' , license='apache-2.0' , library_name='diffusers' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=lowerCAmelCase__ , model_name=lowerCAmelCase__ , repo_name=lowerCAmelCase__ , dataset_name=args.dataset_name if hasattr(lowerCAmelCase__ , 'dataset_name' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(lowerCAmelCase__ , 'gradient_accumulation_steps' ) else None
) , adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__ , 'adam_beta1' ) else None , adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__ , 'adam_beta2' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase__ , 'adam_weight_decay' ) else None , adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase__ , 'adam_epsilon' ) else None , lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase__ , 'lr_scheduler' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase__ , 'lr_warmup_steps' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase__ , 'ema_inv_gamma' ) else None , ema_power=args.ema_power if hasattr(lowerCAmelCase__ , 'ema_power' ) else None , ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase__ , 'ema_max_decay' ) else None , mixed_precision=args.mixed_precision , )
lowerCAmelCase_ : Tuple = os.path.join(args.output_dir , 'README.md' )
model_card.save(lowerCAmelCase__ )
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[str] , lowerCAmelCase__ : Optional[str] = None ) -> Tuple:
"""simple docstring"""
if resolved_file is None or commit_hash is not None:
return commit_hash
lowerCAmelCase_ : Tuple = str(Path(lowerCAmelCase__ ).as_posix() )
lowerCAmelCase_ : Any = re.search(R'snapshots/([^/]+)/' , lowerCAmelCase__ )
if search is None:
return None
lowerCAmelCase_ : Tuple = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase__ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowercase__ : int = os.path.expanduser(
os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
lowercase__ : Optional[int] = os.path.join(hf_cache_home, """diffusers""")
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : Optional[str] = None ) -> None:
"""simple docstring"""
if new_cache_dir is None:
lowerCAmelCase_ : Any = DIFFUSERS_CACHE
if old_cache_dir is None:
lowerCAmelCase_ : Optional[int] = old_diffusers_cache
lowerCAmelCase_ : Optional[int] = Path(lowerCAmelCase__ ).expanduser()
lowerCAmelCase_ : Dict = Path(lowerCAmelCase__ ).expanduser()
for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
lowerCAmelCase_ : List[str] = new_cache_dir / old_blob_path.relative_to(lowerCAmelCase__ )
new_blob_path.parent.mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
os.replace(lowerCAmelCase__ , lowerCAmelCase__ )
try:
os.symlink(lowerCAmelCase__ , lowerCAmelCase__ )
except OSError:
logger.warning(
'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
lowercase__ : Any = os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
lowercase__ : int = 0
else:
with open(cache_version_file) as f:
try:
lowercase__ : int = int(f.read())
except ValueError:
lowercase__ : Any = 0
if cache_version < 1:
lowercase__ : int = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"""The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
"""existing cached models. This is a one-time operation, you can interrupt it or run it """
"""later by calling `diffusers.utils.hub_utils.move_cache()`."""
)
try:
move_cache()
except Exception as e:
lowercase__ : int = """\n""".join(traceback.format_tb(e.__traceback__))
logger.error(
f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
"""file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
"""message and we will do our best to help."""
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, """w""") as f:
f.write("""1""")
except Exception:
logger.warning(
f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
"""the directory exists and can be written to."""
)
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> str:
"""simple docstring"""
if variant is not None:
lowerCAmelCase_ : Any = weights_name.split('.' )
lowerCAmelCase_ : List[Any] = splits[:-1] + [variant] + splits[-1:]
lowerCAmelCase_ : Any = '.'.join(lowerCAmelCase__ )
return weights_name
def UpperCamelCase_ ( lowerCAmelCase__ : List[str] , *,
lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int]=None , ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = str(lowerCAmelCase__ )
if os.path.isfile(lowerCAmelCase__ ):
return pretrained_model_name_or_path
elif os.path.isdir(lowerCAmelCase__ ):
if os.path.isfile(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) ):
# Load from a PyTorch checkpoint
lowerCAmelCase_ : Dict = os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) ):
lowerCAmelCase_ : List[str] = os.path.join(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return model_file
else:
raise EnvironmentError(
f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(lowerCAmelCase__ ).base_version ) >= version.parse('0.20.0' )
):
try:
lowerCAmelCase_ : Dict = hf_hub_download(
lowerCAmelCase__ , filename=_add_variant(lowerCAmelCase__ , lowerCAmelCase__ ) , cache_dir=lowerCAmelCase__ , force_download=lowerCAmelCase__ , proxies=lowerCAmelCase__ , resume_download=lowerCAmelCase__ , local_files_only=lowerCAmelCase__ , use_auth_token=lowerCAmelCase__ , user_agent=lowerCAmelCase__ , subfolder=lowerCAmelCase__ , revision=revision or commit_hash , )
warnings.warn(
f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead." , lowerCAmelCase__ , )
return model_file
except: # noqa: E722
warnings.warn(
f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase__ , lowerCAmelCase__ )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase__ , lowerCAmelCase__ )}' so that the correct variant file can be added." , lowerCAmelCase__ , )
try:
# 2. Load model file as usual
lowerCAmelCase_ : Optional[Any] = hf_hub_download(
lowerCAmelCase__ , filename=lowerCAmelCase__ , cache_dir=lowerCAmelCase__ , force_download=lowerCAmelCase__ , proxies=lowerCAmelCase__ , resume_download=lowerCAmelCase__ , local_files_only=lowerCAmelCase__ , use_auth_token=lowerCAmelCase__ , user_agent=lowerCAmelCase__ , subfolder=lowerCAmelCase__ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
'login`.' )
except RevisionNotFoundError:
raise EnvironmentError(
f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
'this model name. Check the model page at '
f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." )
except EntryNotFoundError:
raise EnvironmentError(
f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." )
except HTTPError as err:
raise EnvironmentError(
f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" )
except ValueError:
raise EnvironmentError(
f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
f" directory containing a file named {weights_name} or"
' \nCheckout your internet connection or see how to run the library in'
' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.' )
except EnvironmentError:
raise EnvironmentError(
f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
'\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
f"containing a file named {weights_name}" )
| 317 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCamelCase_ ( UpperCamelCase__ : jnp.ndarray , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 , UpperCamelCase__ : float = 1 , UpperCamelCase__ : float = 1.0E4 , UpperCamelCase__ : bool = False , UpperCamelCase__ : float = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
__lowerCamelCase = float(embedding_dim // 2 )
__lowerCamelCase = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
__lowerCamelCase = min_timescale * jnp.exp(jnp.arange(UpperCamelCase__ , dtype=jnp.floataa ) * -log_timescale_increment )
__lowerCamelCase = jnp.expand_dims(UpperCamelCase__ , 1 ) * jnp.expand_dims(UpperCamelCase__ , 0 )
# scale embeddings
__lowerCamelCase = scale * emb
if flip_sin_to_cos:
__lowerCamelCase = jnp.concatenate([jnp.cos(UpperCamelCase__ ), jnp.sin(UpperCamelCase__ )] , axis=1 )
else:
__lowerCamelCase = jnp.concatenate([jnp.sin(UpperCamelCase__ ), jnp.cos(UpperCamelCase__ )] , axis=1 )
__lowerCamelCase = jnp.reshape(UpperCamelCase__ , [jnp.shape(UpperCamelCase__ )[0], embedding_dim] )
return signal
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
snake_case_ = 32
snake_case_ = jnp.floataa
@nn.compact
def __call__( self , lowerCamelCase__ ) -> int:
'''simple docstring'''
__lowerCamelCase = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_1' )(lowerCamelCase__ )
__lowerCamelCase = nn.silu(lowerCamelCase__ )
__lowerCamelCase = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_2' )(lowerCamelCase__ )
return temb
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
snake_case_ = 32
snake_case_ = False
snake_case_ = 1
@nn.compact
def __call__( self , lowerCamelCase__ ) -> List[Any]:
'''simple docstring'''
return get_sinusoidal_embeddings(
lowerCamelCase__ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 469 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''vit_mae'''
def __init__( self , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__="gelu" , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-12 , lowerCamelCase__=224 , lowerCamelCase__=16 , lowerCamelCase__=3 , lowerCamelCase__=True , lowerCamelCase__=16 , lowerCamelCase__=512 , lowerCamelCase__=8 , lowerCamelCase__=2_048 , lowerCamelCase__=0.75 , lowerCamelCase__=False , **lowerCamelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = image_size
__lowerCamelCase = patch_size
__lowerCamelCase = num_channels
__lowerCamelCase = qkv_bias
__lowerCamelCase = decoder_num_attention_heads
__lowerCamelCase = decoder_hidden_size
__lowerCamelCase = decoder_num_hidden_layers
__lowerCamelCase = decoder_intermediate_size
__lowerCamelCase = mask_ratio
__lowerCamelCase = norm_pix_loss
| 469 | 1 |
from __future__ import annotations
_snake_case = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_snake_case = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
_lowerCAmelCase : Tuple = len(_lowerCamelCase )
for i in range(_lowerCamelCase ):
_lowerCAmelCase : float = -1
for j in range(i + 1 , _lowerCamelCase ):
if arr[i] < arr[j]:
_lowerCAmelCase : Union[str, Any] = arr[j]
break
result.append(_lowerCamelCase )
return result
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = []
for i, outer in enumerate(_lowerCamelCase ):
_lowerCAmelCase : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
_lowerCAmelCase : str = inner
break
result.append(_lowerCamelCase )
return result
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = len(_lowerCamelCase )
_lowerCAmelCase : list[float] = []
_lowerCAmelCase : list[float] = [-1] * arr_size
for index in reversed(range(_lowerCamelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
_lowerCAmelCase : List[str] = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
_snake_case = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| 658 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
_snake_case = "https://openaipublic.azureedge.net/jukebox/models/"
_snake_case = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def A ( _lowerCamelCase ):
'''simple docstring'''
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
_lowerCAmelCase : int = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
_lowerCAmelCase : Optional[int] = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
_lowerCAmelCase : Union[str, Any] = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
_lowerCAmelCase : int = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
_lowerCAmelCase : List[str] = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
_lowerCAmelCase : int = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
_lowerCAmelCase : int = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
_lowerCAmelCase : Tuple = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = {}
import re
_lowerCAmelCase : Union[str, Any] = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
_lowerCAmelCase : List[str] = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
_lowerCAmelCase : List[Any] = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
_lowerCAmelCase : List[Any] = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
_lowerCAmelCase : List[str] = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
_lowerCAmelCase : int = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
_lowerCAmelCase : List[Any] = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
_lowerCAmelCase : List[Any] = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
_lowerCAmelCase : Optional[int] = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : Any = re_encoder_block_conv_in.match(_lowerCamelCase )
_lowerCAmelCase : List[str] = regex_match.groups()
_lowerCAmelCase : List[Any] = int(groups[2] ) * 2 + int(groups[3] )
_lowerCAmelCase : str = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
_lowerCAmelCase : Tuple = re_encoder_block_conv_in.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_resnet.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : List[Any] = re_encoder_block_resnet.match(_lowerCamelCase )
_lowerCAmelCase : str = regex_match.groups()
_lowerCAmelCase : Optional[int] = int(groups[2] ) * 2 + int(groups[3] )
_lowerCAmelCase : str = {"1": 1, "3": 2}[groups[-2]]
_lowerCAmelCase : Union[str, Any] = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
_lowerCAmelCase : Optional[Any] = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_lowerCAmelCase : int = prefix + resnet_block
_lowerCAmelCase : int = re_encoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = re_encoder_block_proj_out.match(_lowerCamelCase )
_lowerCAmelCase : List[Any] = regex_match.groups()
_lowerCAmelCase : Optional[Any] = F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
_lowerCAmelCase : str = re_encoder_block_proj_out.sub(_lowerCamelCase , _lowerCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : List[str] = re_decoder_block_conv_out.match(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = regex_match.groups()
_lowerCAmelCase : Any = int(groups[2] ) * 2 + int(groups[3] ) - 2
_lowerCAmelCase : Optional[int] = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
_lowerCAmelCase : str = re_decoder_block_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_resnet.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : List[str] = re_decoder_block_resnet.match(_lowerCamelCase )
_lowerCAmelCase : List[str] = regex_match.groups()
_lowerCAmelCase : Optional[Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
_lowerCAmelCase : Union[str, Any] = {"1": 1, "3": 2}[groups[-2]]
_lowerCAmelCase : Optional[Any] = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
_lowerCAmelCase : Optional[int] = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_lowerCAmelCase : Dict = prefix + resnet_block
_lowerCAmelCase : Dict = re_decoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : Optional[int] = re_decoder_block_proj_in.match(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = regex_match.groups()
_lowerCAmelCase : Optional[Any] = F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
_lowerCAmelCase : Any = re_decoder_block_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : Optional[int] = re_prior_cond_conv_out.match(_lowerCamelCase )
_lowerCAmelCase : List[Any] = regex_match.groups()
_lowerCAmelCase : Optional[int] = int(groups[1] ) * 2 + int(groups[2] ) - 2
_lowerCAmelCase : Tuple = F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
_lowerCAmelCase : Optional[int] = re_prior_cond_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_resnet.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : List[str] = re_prior_cond_resnet.match(_lowerCamelCase )
_lowerCAmelCase : List[str] = regex_match.groups()
_lowerCAmelCase : Union[str, Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
_lowerCAmelCase : List[str] = {"1": 1, "3": 2}[groups[-2]]
_lowerCAmelCase : Optional[Any] = F"conditioner_blocks.upsampler.upsample_block.{block_index}."
_lowerCAmelCase : Tuple = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
_lowerCAmelCase : List[Any] = prefix + resnet_block
_lowerCAmelCase : Optional[Any] = re_prior_cond_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase ):
_lowerCAmelCase : int = re_prior_cond_proj_in.match(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = regex_match.groups()
_lowerCAmelCase : Optional[int] = F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
_lowerCAmelCase : List[str] = re_prior_cond_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# keep original key
else:
_lowerCAmelCase : Optional[int] = original_key
_lowerCAmelCase : Tuple = replace_key(_lowerCamelCase )
if F"{key_prefix}.{key}" not in model_state_dict or key is None:
print(F"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
_lowerCAmelCase : Any = model_state_dict[F"{key_prefix}.{key}"]
print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
_lowerCAmelCase : Tuple = original_key
_lowerCAmelCase : List[Any] = original_key
_lowerCAmelCase : Optional[int] = value
return new_dict
@torch.no_grad()
def A ( _lowerCamelCase=None , _lowerCamelCase=None ):
'''simple docstring'''
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
_lowerCAmelCase : List[Any] = requests.get(F"{PREFIX}{file}" , allow_redirects=_lowerCamelCase )
os.makedirs(F"{pytorch_dump_folder_path}/" , exist_ok=_lowerCamelCase )
open(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , "wb" ).write(r.content )
_lowerCAmelCase : Optional[Any] = MODEL_MAPPING[model_name.split("/" )[-1]]
_lowerCAmelCase : Tuple = JukeboxConfig.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = JukeboxModel(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = []
_lowerCAmelCase : List[Any] = {}
for i, dict_name in enumerate(_lowerCamelCase ):
_lowerCAmelCase : Any = torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )["model"]
_lowerCAmelCase : Union[str, Any] = {}
for k in old_dic.keys():
if k.endswith(".b" ):
_lowerCAmelCase : Dict = old_dic[k]
elif k.endswith(".w" ):
_lowerCAmelCase : Tuple = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
_lowerCAmelCase : str = old_dic[k]
else:
_lowerCAmelCase : Union[str, Any] = old_dic[k]
_lowerCAmelCase : Union[str, Any] = "vqvae" if i == 0 else F"priors.{3 - i}"
_lowerCAmelCase : Union[str, Any] = fix_jukebox_keys(_lowerCamelCase , model.state_dict() , _lowerCamelCase , _lowerCamelCase )
weight_dict.append(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = weight_dict.pop(0 )
model.vqvae.load_state_dict(_lowerCamelCase )
for i in range(len(_lowerCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
with open(F"{pytorch_dump_folder_path}/mapping.json" , "w" ) as txtfile:
json.dump(_lowerCamelCase , _lowerCamelCase )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
return weight_dict
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
_snake_case = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 658 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=3 , lowerCamelCase=2_24 , lowerCamelCase=30 , lowerCamelCase=4_00 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=[0.5, 0.5, 0.5] , lowerCamelCase=[0.5, 0.5, 0.5] , ) -> int:
'''simple docstring'''
UpperCamelCase : Dict = size if size is not None else {"height": 18, "width": 18}
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : Union[str, Any] = num_channels
UpperCamelCase : List[str] = image_size
UpperCamelCase : Dict = min_resolution
UpperCamelCase : int = max_resolution
UpperCamelCase : str = do_resize
UpperCamelCase : int = size
UpperCamelCase : str = do_normalize
UpperCamelCase : str = image_mean
UpperCamelCase : Union[str, Any] = image_std
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = ViTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : Optional[int] = EfficientFormerImageProcessorTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
return self.image_proc_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , "image_mean" ) )
self.assertTrue(hasattr(lowerCamelCase , "image_std" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(lowerCamelCase , "size" ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
UpperCamelCase : List[str] = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
UpperCamelCase : Any = image_processor(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
UpperCamelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : Optional[int] = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
UpperCamelCase : List[Any] = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
UpperCamelCase : str = image_processor(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
UpperCamelCase : Any = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
UpperCamelCase : Tuple = image_processor(lowerCamelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 173 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = None , **lowerCamelCase , ) -> Dict:
'''simple docstring'''
UpperCamelCase : Optional[Any] = path_or_paths
UpperCamelCase : List[str] = split if split or isinstance(lowerCamelCase , lowerCamelCase ) else "train"
UpperCamelCase : Any = features
UpperCamelCase : Optional[int] = cache_dir
UpperCamelCase : str = keep_in_memory
UpperCamelCase : str = streaming
UpperCamelCase : List[Any] = num_proc
UpperCamelCase : Optional[Any] = kwargs
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
'''simple docstring'''
pass
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = None , **lowerCamelCase , ) -> str:
'''simple docstring'''
UpperCamelCase : Tuple = features
UpperCamelCase : str = cache_dir
UpperCamelCase : List[Any] = keep_in_memory
UpperCamelCase : int = streaming
UpperCamelCase : int = num_proc
UpperCamelCase : Tuple = kwargs
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[Dataset, IterableDataset]:
'''simple docstring'''
pass
| 173 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
a_ : Optional[int] = 16
a_ : int = 32
def _SCREAMING_SNAKE_CASE ( snake_case_ : Accelerator , snake_case_ : int = 16 , snake_case_ : str = "bert-base-cased" ):
__magic_name__ = AutoTokenizer.from_pretrained(snake_case_ )
__magic_name__ = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(snake_case_ : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
__magic_name__ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case_ , max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__magic_name__ = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=snake_case_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__magic_name__ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case_ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(snake_case_ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
__magic_name__ = DataLoader(
tokenized_datasets['''train'''] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
__magic_name__ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : Dict , snake_case_ : List[Any] , snake_case_ : str ):
model.eval()
__magic_name__ = 0
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__magic_name__ = model(**snake_case_ )
__magic_name__ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
__magic_name__ , __magic_name__ = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(snake_case_ ) - 1:
__magic_name__ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__magic_name__ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=snake_case_ , references=snake_case_ , )
__magic_name__ = metric.compute()
return eval_metric["accuracy"]
def _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] , snake_case_ : Tuple ):
# Initialize accelerator
__magic_name__ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__magic_name__ = config['''lr''']
__magic_name__ = int(config['''num_epochs'''] )
__magic_name__ = int(config['''seed'''] )
__magic_name__ = int(config['''batch_size'''] )
__magic_name__ = args.model_name_or_path
set_seed(snake_case_ )
__magic_name__ , __magic_name__ = get_dataloaders(snake_case_ , snake_case_ , snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__magic_name__ = AutoModelForSequenceClassification.from_pretrained(snake_case_ , return_dict=snake_case_ )
# Instantiate optimizer
__magic_name__ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__magic_name__ = optimizer_cls(params=model.parameters() , lr=snake_case_ )
if accelerator.state.deepspeed_plugin is not None:
__magic_name__ = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
__magic_name__ = 1
__magic_name__ = (len(snake_case_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__magic_name__ = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=0 , num_training_steps=snake_case_ , )
else:
__magic_name__ = DummyScheduler(snake_case_ , total_num_steps=snake_case_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# We need to keep track of how many total steps we have iterated over
__magic_name__ = 0
# We also need to keep track of the stating epoch so files are named properly
__magic_name__ = 0
__magic_name__ = evaluate.load('''glue''' , '''mrpc''' )
__magic_name__ = num_epochs
if args.partial_train_epoch is not None:
__magic_name__ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
__magic_name__ = args.resume_from_checkpoint.split('''epoch_''' )[1]
__magic_name__ = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
__magic_name__ = int(snake_case_ ) + 1
__magic_name__ = evaluation_loop(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
accelerator.print('''resumed checkpoint performance:''' , snake_case_ )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' , lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' , optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir , f'state_{starting_epoch-1}.json' ) , '''r''' ) as f:
__magic_name__ = json.load(snake_case_ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
__magic_name__ = {}
for epoch in range(snake_case_ , snake_case_ ):
model.train()
for step, batch in enumerate(snake_case_ ):
__magic_name__ = model(**snake_case_ )
__magic_name__ = outputs.loss
__magic_name__ = loss / gradient_accumulation_steps
accelerator.backward(snake_case_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
__magic_name__ = f'epoch_{epoch}'
__magic_name__ = os.path.join(args.output_dir , snake_case_ )
accelerator.save_state(snake_case_ )
__magic_name__ = evaluation_loop(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
__magic_name__ = accuracy
__magic_name__ = lr_scheduler.get_lr()[0]
__magic_name__ = optimizer.param_groups[0]['''lr''']
__magic_name__ = epoch
__magic_name__ = overall_step
accelerator.print(f'epoch {epoch}:' , snake_case_ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'state_{epoch}.json' ) , '''w''' ) as f:
json.dump(snake_case_ , snake_case_ )
def _SCREAMING_SNAKE_CASE ( ):
__magic_name__ = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=snake_case_ , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=snake_case_ , )
parser.add_argument(
'''--output_dir''' , type=snake_case_ , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=snake_case_ , default=snake_case_ , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--partial_train_epoch''' , type=snake_case_ , default=snake_case_ , help='''If passed, the training will stop after this number of epochs.''' , )
parser.add_argument(
'''--num_epochs''' , type=snake_case_ , default=2 , help='''Number of train epochs.''' , )
__magic_name__ = parser.parse_args()
__magic_name__ = {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main() | 678 |
import re
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
__magic_name__ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(snake_case_ , snake_case_ ) )
if __name__ == "__main__":
a_ : Optional[int] = '0094702343221'
print(is_sri_lankan_phone_number(phone)) | 678 | 1 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __UpperCamelCase( ):
'''simple docstring'''
UpperCAmelCase__ : str = [randint(-10_00 , 10_00 ) for i in range(10 )]
UpperCAmelCase__ : Optional[Any] = randint(-50_00 , 50_00 )
return (arr, r)
UpperCamelCase__ : Any = make_dataset()
def __UpperCamelCase( _A : list[int] , _A : int ):
'''simple docstring'''
for triplet in permutations(_A , 3 ):
if sum(_A ) == target:
return tuple(sorted(_A ) )
return (0, 0, 0)
def __UpperCamelCase( _A : list[int] , _A : int ):
'''simple docstring'''
arr.sort()
UpperCAmelCase__ : List[Any] = len(_A )
for i in range(n - 1 ):
UpperCAmelCase__ , UpperCAmelCase__ : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __UpperCamelCase( ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
UpperCAmelCase__ : List[Any] = '''
triplet_sum1(*dataset)
'''
UpperCAmelCase__ : Any = '''
triplet_sum2(*dataset)
'''
UpperCAmelCase__ : Dict = repeat(setup=_A , stmt=_A , repeat=5 , number=1_00_00 )
UpperCAmelCase__ : Union[str, Any] = repeat(setup=_A , stmt=_A , repeat=5 , number=1_00_00 )
return (min(_A ), min(_A ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCamelCase__ : Optional[Any] = solution_times()
print(f"""The time for naive implementation is {times[0]}.""")
print(f"""The time for optimized implementation is {times[1]}.""")
| 614 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__ : Dict = logging.getLogger(__name__)
def __UpperCamelCase( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = argparse.ArgumentParser(
description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' )
parser.add_argument('''--file_path''' , type=_A , default='''data/dump.txt''' , help='''The path to the data.''' )
parser.add_argument('''--tokenizer_type''' , type=_A , default='''bert''' , choices=['''bert''', '''roberta''', '''gpt2'''] )
parser.add_argument('''--tokenizer_name''' , type=_A , default='''bert-base-uncased''' , help='''The tokenizer to use.''' )
parser.add_argument('''--dump_file''' , type=_A , default='''data/dump''' , help='''The dump file prefix.''' )
UpperCAmelCase__ : Optional[int] = parser.parse_args()
logger.info(F'''Loading Tokenizer ({args.tokenizer_name})''' )
if args.tokenizer_type == "bert":
UpperCAmelCase__ : List[str] = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : Optional[Any] = tokenizer.special_tokens_map['''cls_token'''] # `[CLS]`
UpperCAmelCase__ : Union[str, Any] = tokenizer.special_tokens_map['''sep_token'''] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase__ : Union[str, Any] = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : List[Any] = tokenizer.special_tokens_map['''cls_token'''] # `<s>`
UpperCAmelCase__ : Dict = tokenizer.special_tokens_map['''sep_token'''] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase__ : List[str] = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase__ : List[Any] = tokenizer.special_tokens_map['''bos_token'''] # `<|endoftext|>`
UpperCAmelCase__ : Optional[Any] = tokenizer.special_tokens_map['''eos_token'''] # `<|endoftext|>`
logger.info(F'''Loading text from {args.file_path}''' )
with open(args.file_path , '''r''' , encoding='''utf8''' ) as fp:
UpperCAmelCase__ : List[Any] = fp.readlines()
logger.info('''Start encoding''' )
logger.info(F'''{len(_A )} examples to process.''' )
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Union[str, Any] = 0
UpperCAmelCase__ : Optional[int] = 1_00_00
UpperCAmelCase__ : Tuple = time.time()
for text in data:
UpperCAmelCase__ : Any = F'''{bos} {text.strip()} {sep}'''
UpperCAmelCase__ : int = tokenizer.encode(_A , add_special_tokens=_A )
rslt.append(_A )
iter += 1
if iter % interval == 0:
UpperCAmelCase__ : int = time.time()
logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl''' )
UpperCAmelCase__ : Optional[Any] = time.time()
logger.info('''Finished binarization''' )
logger.info(F'''{len(_A )} examples processed.''' )
UpperCAmelCase__ : Dict = F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
UpperCAmelCase__ : Dict = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase__ : Any = [np.uintaa(_A ) for d in rslt]
else:
UpperCAmelCase__ : str = [np.intaa(_A ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F'''Dump to {dp_file}''' )
with open(_A , '''wb''' ) as handle:
pickle.dump(rslt_ , _A , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 614 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Dict = KandinskyImgaImgPipeline
__snake_case : List[str] = ["prompt", "image_embeds", "negative_image_embeds", "image"]
__snake_case : int = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
__snake_case : Any = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
__snake_case : List[str] = False
@property
def UpperCamelCase ( self: str ):
'''simple docstring'''
return 32
@property
def UpperCamelCase ( self: Any ):
'''simple docstring'''
return 32
@property
def UpperCamelCase ( self: str ):
'''simple docstring'''
return self.time_input_dim
@property
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCamelCase ( self: int ):
'''simple docstring'''
return 100
@property
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , )
_SCREAMING_SNAKE_CASE = MultilingualCLIP(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = text_encoder.eval()
return text_encoder
@property
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(**UpperCAmelCase_ )
return model
@property
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.dummy_text_encoder
_SCREAMING_SNAKE_CASE = self.dummy_tokenizer
_SCREAMING_SNAKE_CASE = self.dummy_unet
_SCREAMING_SNAKE_CASE = self.dummy_movq
_SCREAMING_SNAKE_CASE = {
"""num_train_timesteps""": 1_000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
_SCREAMING_SNAKE_CASE = DDIMScheduler(**UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def UpperCamelCase ( self: Any , UpperCAmelCase_: List[Any] , UpperCAmelCase_: str=0 ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCAmelCase_ )
# create init_image
_SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(UpperCAmelCase_ ) ).convert("""RGB""" ).resize((256, 256) )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(UpperCAmelCase_ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = """cpu"""
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = output.images
_SCREAMING_SNAKE_CASE = pipe(
**self.get_dummy_inputs(UpperCAmelCase_ ) , return_dict=UpperCAmelCase_ , )[0]
_SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
_SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_SCREAMING_SNAKE_CASE = np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class __UpperCAmelCase (unittest.TestCase ):
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self: List[str] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
_SCREAMING_SNAKE_CASE = """A red cartoon frog, 4k"""
_SCREAMING_SNAKE_CASE = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa )
_SCREAMING_SNAKE_CASE = pipeline.to(UpperCAmelCase_ )
pipeline.set_progress_bar_config(disable=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = pipe_prior(
UpperCAmelCase_ , generator=UpperCAmelCase_ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
_SCREAMING_SNAKE_CASE = pipeline(
UpperCAmelCase_ , image=UpperCAmelCase_ , image_embeds=UpperCAmelCase_ , negative_image_embeds=UpperCAmelCase_ , generator=UpperCAmelCase_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
_SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCAmelCase_ , UpperCAmelCase_ )
| 569 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCamelCase ( snake_case__ ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(snake_case__ ,0 ,snake_case__ ,args=(snake_case__) )[0]
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> float:
"""simple docstring"""
return math.pow(snake_case__ ,z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 569 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A_ : Tuple ="""%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
A_ : Dict =F'''https://www.google.com/search?q={query}&num=100'''
A_ : Dict =requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
A_ : Tuple =(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
A_ : str =parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link) | 483 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase : int = logging.getLogger(__name__)
class _a (a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = """masked_bert"""
def __init__( self ,__a=30_522 ,__a=768 ,__a=12 ,__a=12 ,__a=3_072 ,__a="gelu" ,__a=0.1 ,__a=0.1 ,__a=512 ,__a=2 ,__a=0.02 ,__a=1E-12 ,__a=0 ,__a="topK" ,__a="constant" ,__a=0.0 ,**__a ,) -> List[str]:
super().__init__(pad_token_id=__a ,**__a )
snake_case : Dict = vocab_size
snake_case : Optional[Any] = hidden_size
snake_case : Dict = num_hidden_layers
snake_case : List[Any] = num_attention_heads
snake_case : Dict = hidden_act
snake_case : Any = intermediate_size
snake_case : Optional[int] = hidden_dropout_prob
snake_case : Optional[Any] = attention_probs_dropout_prob
snake_case : List[Any] = max_position_embeddings
snake_case : int = type_vocab_size
snake_case : int = initializer_range
snake_case : int = layer_norm_eps
snake_case : Any = pruning_method
snake_case : Union[str, Any] = mask_init
snake_case : int = mask_scale
| 116 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class A__ ( A__ ):
"""simple docstring"""
_lowercase = 'deta'
_lowercase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : List[Any] , lowerCamelCase__ : List[str]=None , lowerCamelCase__ : str=900 , lowerCamelCase__ : Union[str, Any]=2_048 , lowerCamelCase__ : Dict=6 , lowerCamelCase__ : str=2_048 , lowerCamelCase__ : Tuple=8 , lowerCamelCase__ : List[Any]=6 , lowerCamelCase__ : Dict=1_024 , lowerCamelCase__ : int=8 , lowerCamelCase__ : Any=0.0 , lowerCamelCase__ : List[Any]=True , lowerCamelCase__ : Tuple="relu" , lowerCamelCase__ : Tuple=256 , lowerCamelCase__ : List[str]=0.1 , lowerCamelCase__ : Union[str, Any]=0.0 , lowerCamelCase__ : Dict=0.0 , lowerCamelCase__ : Union[str, Any]=0.02 , lowerCamelCase__ : int=1.0 , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Optional[Any]=False , lowerCamelCase__ : Tuple="sine" , lowerCamelCase__ : Any=5 , lowerCamelCase__ : str=4 , lowerCamelCase__ : List[Any]=4 , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Union[str, Any]=300 , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Optional[int]=True , lowerCamelCase__ : Dict=1 , lowerCamelCase__ : List[str]=5 , lowerCamelCase__ : Tuple=2 , lowerCamelCase__ : Optional[int]=1 , lowerCamelCase__ : List[Any]=1 , lowerCamelCase__ : Dict=5 , lowerCamelCase__ : str=2 , lowerCamelCase__ : str=0.1 , lowerCamelCase__ : Union[str, Any]=0.25 , **lowerCamelCase__ : int , ):
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
a__ : List[Any] = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
else:
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
a__ : Tuple = backbone_config.pop("model_type" )
a__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
a__ : Tuple = config_class.from_dict(lowerCamelCase__ )
a__ : int = backbone_config
a__ : int = num_queries
a__ : Dict = max_position_embeddings
a__ : Union[str, Any] = d_model
a__ : List[Any] = encoder_ffn_dim
a__ : int = encoder_layers
a__ : Dict = encoder_attention_heads
a__ : Dict = decoder_ffn_dim
a__ : List[str] = decoder_layers
a__ : str = decoder_attention_heads
a__ : Optional[int] = dropout
a__ : str = attention_dropout
a__ : Optional[Any] = activation_dropout
a__ : Any = activation_function
a__ : Dict = init_std
a__ : Tuple = init_xavier_std
a__ : str = encoder_layerdrop
a__ : Dict = auxiliary_loss
a__ : List[Any] = position_embedding_type
# deformable attributes
a__ : Dict = num_feature_levels
a__ : str = encoder_n_points
a__ : Tuple = decoder_n_points
a__ : List[Any] = two_stage
a__ : Dict = two_stage_num_proposals
a__ : int = with_box_refine
a__ : int = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("If two_stage is True, with_box_refine must be True." )
# Hungarian matcher
a__ : List[str] = class_cost
a__ : Optional[Any] = bbox_cost
a__ : List[str] = giou_cost
# Loss coefficients
a__ : Any = mask_loss_coefficient
a__ : Union[str, Any] = dice_loss_coefficient
a__ : List[str] = bbox_loss_coefficient
a__ : Any = giou_loss_coefficient
a__ : Optional[int] = eos_coefficient
a__ : Tuple = focal_alpha
super().__init__(is_encoder_decoder=lowerCamelCase__ , **lowerCamelCase__ )
@property
def _UpperCamelCase( self : List[str] ):
return self.encoder_attention_heads
@property
def _UpperCamelCase( self : Tuple ):
return self.d_model
def _UpperCamelCase( self : List[Any] ):
a__ : Dict = copy.deepcopy(self.__dict__ )
a__ : List[Any] = self.backbone_config.to_dict()
a__ : Optional[Any] = self.__class__.model_type
return output
| 151 |
from copy import deepcopy
class A__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase__ : list[int] | None = None , lowerCamelCase__ : int | None = None ):
if arr is None and size is not None:
a__ : Union[str, Any] = size
a__ : Optional[Any] = [0] * size
elif arr is not None:
self.init(lowerCamelCase__ )
else:
raise ValueError("Either arr or size must be specified" )
def _UpperCamelCase( self : Optional[int] , lowerCamelCase__ : list[int] ):
a__ : Any = len(lowerCamelCase__ )
a__ : List[Any] = deepcopy(lowerCamelCase__ )
for i in range(1 , self.size ):
a__ : Union[str, Any] = self.next_(lowerCamelCase__ )
if j < self.size:
self.tree[j] += self.tree[i]
def _UpperCamelCase( self : Tuple ):
a__ : List[str] = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
a__ : Optional[Any] = self.next_(lowerCamelCase__ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def _UpperCamelCase( lowerCamelCase__ : int ):
return index + (index & (-index))
@staticmethod
def _UpperCamelCase( lowerCamelCase__ : int ):
return index - (index & (-index))
def _UpperCamelCase( self : str , lowerCamelCase__ : int , lowerCamelCase__ : int ):
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
a__ : Optional[int] = self.next_(lowerCamelCase__ )
def _UpperCamelCase( self : List[str] , lowerCamelCase__ : int , lowerCamelCase__ : int ):
self.add(lowerCamelCase__ , value - self.get(lowerCamelCase__ ) )
def _UpperCamelCase( self : Union[str, Any] , lowerCamelCase__ : int ):
if right == 0:
return 0
a__ : Tuple = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
a__ : List[Any] = self.prev(lowerCamelCase__ )
return result
def _UpperCamelCase( self : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int ):
return self.prefix(lowerCamelCase__ ) - self.prefix(lowerCamelCase__ )
def _UpperCamelCase( self : Union[str, Any] , lowerCamelCase__ : int ):
return self.query(lowerCamelCase__ , index + 1 )
def _UpperCamelCase( self : int , lowerCamelCase__ : int ):
value -= self.tree[0]
if value < 0:
return -1
a__ : Union[str, Any] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
a__ : Tuple = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 | 1 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : int = 1_0**1_2 ):
__a : Union[str, Any] = 1
__a : Union[str, Any] = 0
__a : int = 1
__a : Tuple = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f'{solution() = }')
| 581 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
"bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
"bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
"bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
"bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
"bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = "bloom"
_lowerCAmelCase = ["past_key_values"]
_lowerCAmelCase = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__(self , _lowercase=250880 , _lowercase=64 , _lowercase=2 , _lowercase=8 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=1 , _lowercase=2 , _lowercase=False , _lowercase=0.0 , _lowercase=0.0 , _lowercase=1 , _lowercase=False , **_lowercase , ):
'''simple docstring'''
__a : Tuple = vocab_size
# Backward compatibility with n_embed kwarg
__a : Tuple = kwargs.pop("""n_embed""" , _lowercase )
__a : Optional[Any] = hidden_size if n_embed is None else n_embed
__a : Optional[int] = n_layer
__a : Optional[int] = n_head
__a : Union[str, Any] = layer_norm_epsilon
__a : Optional[Any] = initializer_range
__a : List[str] = use_cache
__a : List[str] = pretraining_tp
__a : Optional[Any] = apply_residual_connection_post_layernorm
__a : Optional[int] = hidden_dropout
__a : List[str] = attention_dropout
__a : Tuple = bos_token_id
__a : Tuple = eos_token_id
__a : List[str] = slow_but_exact
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
class SCREAMING_SNAKE_CASE__ ( __snake_case ):
_lowerCAmelCase = version.parse("1.12" )
def __init__(self , _lowercase , _lowercase = "default" , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
super().__init__(_lowercase , task=_lowercase , patching_specs=_lowercase , use_past=_lowercase )
if not getattr(self._config , """pad_token_id""" , _lowercase ):
# TODO: how to do that better?
__a : Any = 0
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(_lowercase , direction="""inputs""" , inverted_values_shape=_lowercase )
__a : int = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__a : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return self._config.n_head
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 1e-3
def lowerCAmelCase__(self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
'''simple docstring'''
__a : Union[str, Any] = super(_lowercase , self ).generate_dummy_inputs(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
# We need to order the input in the way they appears in the forward()
__a : List[str] = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__a , __a : Optional[int] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__a : str = seqlen + 2
__a : List[Any] = self._config.hidden_size // self.num_attention_heads
__a : List[Any] = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__a : Optional[int] = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__a : int = [
(torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers )
]
__a : int = common_inputs["""attention_mask"""]
if self.use_past:
__a : int = ordered_inputs["""attention_mask"""].dtype
__a : Union[str, Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 )
return ordered_inputs
@property
def lowerCAmelCase__(self ):
'''simple docstring'''
return 13
| 581 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> str:
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(SCREAMING_SNAKE_CASE__ )
if n > 1:
factors.append(SCREAMING_SNAKE_CASE__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 704 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Any , lowerCamelCase : int = 128 , lowerCamelCase : int = 256 , lowerCamelCase : float = 2000.0 , lowerCamelCase : int = 768 , lowerCamelCase : int = 12 , lowerCamelCase : int = 12 , lowerCamelCase : int = 64 , lowerCamelCase : int = 2048 , lowerCamelCase : float = 0.1 , ) -> str:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Sequential(
nn.Linear(lowerCamelCase , d_model * 4 , bias=lowerCamelCase ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=lowerCamelCase ) , nn.SiLU() , )
_UpperCAmelCase = nn.Embedding(lowerCamelCase , lowerCamelCase )
_UpperCAmelCase = False
_UpperCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
_UpperCAmelCase = nn.Dropout(p=lowerCamelCase )
_UpperCAmelCase = nn.ModuleList()
for lyr_num in range(lowerCamelCase ):
# FiLM conditional T5 decoder
_UpperCAmelCase = DecoderLayer(d_model=lowerCamelCase , d_kv=lowerCamelCase , num_heads=lowerCamelCase , d_ff=lowerCamelCase , dropout_rate=lowerCamelCase )
self.decoders.append(lowerCamelCase )
_UpperCAmelCase = TaLayerNorm(lowerCamelCase )
_UpperCAmelCase = nn.Dropout(p=lowerCamelCase )
_UpperCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
def lowerCamelCase ( self : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def lowerCamelCase ( self : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : List[Any] , lowerCamelCase : str ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_UpperCAmelCase = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_UpperCAmelCase = self.conditioning_emb(lowerCamelCase ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_UpperCAmelCase = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_UpperCAmelCase = torch.broadcast_to(
torch.arange(lowerCamelCase , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_UpperCAmelCase = self.position_encoding(lowerCamelCase )
_UpperCAmelCase = self.continuous_inputs_projection(lowerCamelCase )
inputs += position_encodings
_UpperCAmelCase = self.dropout(lowerCamelCase )
# decoder: No padding present.
_UpperCAmelCase = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_UpperCAmelCase = [(x, self.encoder_decoder_mask(lowerCamelCase , lowerCamelCase )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_UpperCAmelCase = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_UpperCAmelCase = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_UpperCAmelCase = lyr(
lowerCamelCase , conditioning_emb=lowerCamelCase , encoder_hidden_states=lowerCamelCase , encoder_attention_mask=lowerCamelCase , )[0]
_UpperCAmelCase = self.decoder_norm(lowerCamelCase )
_UpperCAmelCase = self.post_dropout(lowerCamelCase )
_UpperCAmelCase = self.spec_out(lowerCamelCase )
return spec_out
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] , lowerCamelCase : Any=1E-6 ) -> int:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=lowerCamelCase , d_kv=lowerCamelCase , num_heads=lowerCamelCase , dropout_rate=lowerCamelCase ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=lowerCamelCase , d_kv=lowerCamelCase , num_heads=lowerCamelCase , dropout_rate=lowerCamelCase , layer_norm_epsilon=lowerCamelCase , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=lowerCamelCase , d_ff=lowerCamelCase , dropout_rate=lowerCamelCase , layer_norm_epsilon=lowerCamelCase ) )
def lowerCamelCase ( self : str , lowerCamelCase : Tuple , lowerCamelCase : Tuple=None , lowerCamelCase : Optional[Any]=None , lowerCamelCase : Optional[int]=None , lowerCamelCase : Any=None , lowerCamelCase : Optional[int]=None , ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.layer[0](
lowerCamelCase , conditioning_emb=lowerCamelCase , attention_mask=lowerCamelCase , )
if encoder_hidden_states is not None:
_UpperCAmelCase = torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_UpperCAmelCase = self.layer[1](
lowerCamelCase , key_value_states=lowerCamelCase , attention_mask=lowerCamelCase , )
# Apply Film Conditional Feed Forward layer
_UpperCAmelCase = self.layer[-1](lowerCamelCase , lowerCamelCase )
return (hidden_states,)
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : List[str] , lowerCamelCase : int ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = TaLayerNorm(lowerCamelCase )
_UpperCAmelCase = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCamelCase )
_UpperCAmelCase = Attention(query_dim=lowerCamelCase , heads=lowerCamelCase , dim_head=lowerCamelCase , out_bias=lowerCamelCase , scale_qk=lowerCamelCase )
_UpperCAmelCase = nn.Dropout(lowerCamelCase )
def lowerCamelCase ( self : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , lowerCamelCase : Dict=None , ) -> List[Any]:
"""simple docstring"""
# pre_self_attention_layer_norm
_UpperCAmelCase = self.layer_norm(lowerCamelCase )
if conditioning_emb is not None:
_UpperCAmelCase = self.FiLMLayer(lowerCamelCase , lowerCamelCase )
# Self-attention block
_UpperCAmelCase = self.attention(lowerCamelCase )
_UpperCAmelCase = hidden_states + self.dropout(lowerCamelCase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : List[str] , lowerCamelCase : List[str] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = Attention(query_dim=lowerCamelCase , heads=lowerCamelCase , dim_head=lowerCamelCase , out_bias=lowerCamelCase , scale_qk=lowerCamelCase )
_UpperCAmelCase = TaLayerNorm(lowerCamelCase , eps=lowerCamelCase )
_UpperCAmelCase = nn.Dropout(lowerCamelCase )
def lowerCamelCase ( self : Optional[int] , lowerCamelCase : List[str] , lowerCamelCase : int=None , lowerCamelCase : Optional[Any]=None , ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.layer_norm(lowerCamelCase )
_UpperCAmelCase = self.attention(
lowerCamelCase , encoder_hidden_states=lowerCamelCase , attention_mask=attention_mask.squeeze(1 ) , )
_UpperCAmelCase = hidden_states + self.dropout(lowerCamelCase )
return layer_output
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : Any , lowerCamelCase : int ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = TaDenseGatedActDense(d_model=lowerCamelCase , d_ff=lowerCamelCase , dropout_rate=lowerCamelCase )
_UpperCAmelCase = TaFiLMLayer(in_features=d_model * 4 , out_features=lowerCamelCase )
_UpperCAmelCase = TaLayerNorm(lowerCamelCase , eps=lowerCamelCase )
_UpperCAmelCase = nn.Dropout(lowerCamelCase )
def lowerCamelCase ( self : List[Any] , lowerCamelCase : Any , lowerCamelCase : str=None ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.layer_norm(lowerCamelCase )
if conditioning_emb is not None:
_UpperCAmelCase = self.film(lowerCamelCase , lowerCamelCase )
_UpperCAmelCase = self.DenseReluDense(lowerCamelCase )
_UpperCAmelCase = hidden_states + self.dropout(lowerCamelCase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[str] ) -> Any:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
_UpperCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
_UpperCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
_UpperCAmelCase = nn.Dropout(lowerCamelCase )
_UpperCAmelCase = NewGELUActivation()
def lowerCamelCase ( self : Tuple , lowerCamelCase : Dict ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.act(self.wi_a(lowerCamelCase ) )
_UpperCAmelCase = self.wi_a(lowerCamelCase )
_UpperCAmelCase = hidden_gelu * hidden_linear
_UpperCAmelCase = self.dropout(lowerCamelCase )
_UpperCAmelCase = self.wo(lowerCamelCase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , lowerCamelCase : Tuple , lowerCamelCase : str=1E-6 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Parameter(torch.ones(lowerCamelCase ) )
_UpperCAmelCase = eps
def lowerCamelCase ( self : List[str] , lowerCamelCase : int ) -> Union[str, Any]:
"""simple docstring"""
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_UpperCAmelCase = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=lowerCamelCase )
_UpperCAmelCase = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_UpperCAmelCase = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def lowerCamelCase ( self : Union[str, Any] , lowerCamelCase : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_4715 * torch.pow(lowerCamelCase , 3.0 )) ))
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , lowerCamelCase : Any , lowerCamelCase : Any ) -> Optional[int]:
"""simple docstring"""
super().__init__()
_UpperCAmelCase = nn.Linear(lowerCamelCase , out_features * 2 , bias=lowerCamelCase )
def lowerCamelCase ( self : int , lowerCamelCase : str , lowerCamelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = self.scale_bias(lowerCamelCase )
_UpperCAmelCase , _UpperCAmelCase = torch.chunk(lowerCamelCase , 2 , -1 )
_UpperCAmelCase = x * (1 + scale) + shift
return x | 402 | 0 |
lowerCAmelCase__ = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase__ = [{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__ = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 503 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCAmelCase__ :
'''simple docstring'''
@property
def _lowerCamelCase ( self) -> Tuple:
return self.get_dummy_input()
@property
def _lowerCamelCase ( self) -> List[Any]:
if self.block_type == "down":
return (4, 3_2, 1_6, 1_6)
elif self.block_type == "mid":
return (4, 3_2, 3_2, 3_2)
elif self.block_type == "up":
return (4, 3_2, 6_4, 6_4)
raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.")
def _lowerCamelCase ( self , __lowerCamelCase=True , __lowerCamelCase=False , __lowerCamelCase=False , __lowerCamelCase=False , ) -> Dict:
_A : Tuple = 4
_A : Optional[Any] = 3_2
_A : Optional[int] = (3_2, 3_2)
_A : Dict = torch.manual_seed(0)
_A : List[Any] = torch.device(__lowerCamelCase)
_A : Union[str, Any] = (batch_size, num_channels) + sizes
_A : int = randn_tensor(__lowerCamelCase , generator=__lowerCamelCase , device=__lowerCamelCase)
_A : Optional[int] = {"hidden_states": hidden_states}
if include_temb:
_A : Dict = 1_2_8
_A : Any = randn_tensor((batch_size, temb_channels) , generator=__lowerCamelCase , device=__lowerCamelCase)
if include_res_hidden_states_tuple:
_A : str = torch.manual_seed(1)
_A : str = (randn_tensor(__lowerCamelCase , generator=__lowerCamelCase , device=__lowerCamelCase),)
if include_encoder_hidden_states:
_A : Any = floats_tensor((batch_size, 3_2, 3_2)).to(__lowerCamelCase)
if include_skip_sample:
_A : List[Any] = randn_tensor(((batch_size, 3) + sizes) , generator=__lowerCamelCase , device=__lowerCamelCase)
return dummy_input
def _lowerCamelCase ( self) -> Optional[Any]:
_A : int = {
"in_channels": 3_2,
"out_channels": 3_2,
"temb_channels": 1_2_8,
}
if self.block_type == "up":
_A : Optional[Any] = 3_2
if self.block_type == "mid":
init_dict.pop("out_channels")
_A : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def _lowerCamelCase ( self , __lowerCamelCase) -> Dict:
_A , _A : Optional[int] = self.prepare_init_args_and_inputs_for_common()
_A : int = self.block_class(**__lowerCamelCase)
unet_block.to(__lowerCamelCase)
unet_block.eval()
with torch.no_grad():
_A : Any = unet_block(**__lowerCamelCase)
if isinstance(__lowerCamelCase , __lowerCamelCase):
_A : Optional[Any] = output[0]
self.assertEqual(output.shape , self.output_shape)
_A : Optional[int] = output[0, -1, -3:, -3:]
_A : Dict = torch.tensor(__lowerCamelCase).to(__lowerCamelCase)
assert torch_all_close(output_slice.flatten() , __lowerCamelCase , atol=5e-3)
@unittest.skipIf(torch_device == "mps" , "Training is not supported in mps")
def _lowerCamelCase ( self) -> Dict:
_A , _A : Optional[int] = self.prepare_init_args_and_inputs_for_common()
_A : Optional[int] = self.block_class(**__lowerCamelCase)
model.to(__lowerCamelCase)
model.train()
_A : Tuple = model(**__lowerCamelCase)
if isinstance(__lowerCamelCase , __lowerCamelCase):
_A : List[Any] = output[0]
_A : Any = torch.device(__lowerCamelCase)
_A : Any = randn_tensor(output.shape , device=__lowerCamelCase)
_A : Optional[int] = torch.nn.functional.mse_loss(__lowerCamelCase , __lowerCamelCase)
loss.backward()
| 503 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase : List[str] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
"processing_speech_to_text": ["Speech2TextProcessor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : int = ["Speech2TextTokenizer"]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[int] = ["Speech2TextFeatureExtractor"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Any = [
"TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSpeech2TextPreTrainedModel",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Optional[int] = [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Speech2TextForConditionalGeneration",
"Speech2TextModel",
"Speech2TextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowercase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 707 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a__ :
_A = 42
_A = 42
class a__ :
def __init__( self : Optional[Any] , A_ : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_: list[list[Edge]] = [[] for _ in range(A_ )]
lowerCamelCase_: Dict = size
def __getitem__( self : Any , A_ : int ) -> Iterator[Edge]:
"""simple docstring"""
return iter(self._graph[vertex] )
@property
def lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
return self._size
def lowerCAmelCase ( self : Union[str, Any] , A_ : int , A_ : int , A_ : int ) -> Union[str, Any]:
"""simple docstring"""
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(A_ , A_ ) )
def lowerCAmelCase ( self : Any , A_ : int , A_ : int ) -> int | None:
"""simple docstring"""
lowerCamelCase_: str = deque([start_vertex] )
lowerCamelCase_: list[int | None] = [None] * self.size
lowerCamelCase_: int = 0
while queue:
lowerCamelCase_: List[Any] = queue.popleft()
lowerCamelCase_: Any = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
lowerCamelCase_: Dict = current_distance + edge.weight
lowerCamelCase_: Dict = distances[edge.destination_vertex]
if (
isinstance(A_ , A_ )
and new_distance >= dest_vertex_distance
):
continue
lowerCamelCase_: Dict = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 584 | 0 |
from __future__ import annotations
def a_ ( __magic_name__ , __magic_name__ ) -> tuple[int, int]:
"""simple docstring"""
if b == 0:
return (1, 0)
((snake_case) , (snake_case)) : Any = extended_euclid(__magic_name__ , a % b )
snake_case : Dict = a // b
return (y, x - k * y)
def a_ ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
"""simple docstring"""
((snake_case) , (snake_case)) : str = extended_euclid(__magic_name__ , __magic_name__ )
snake_case : Tuple = na * na
snake_case : str = ra * x * na + ra * y * na
return (n % m + m) % m
def a_ ( __magic_name__ , __magic_name__ ) -> int:
"""simple docstring"""
((snake_case) , (snake_case)) : Any = extended_euclid(__magic_name__ , __magic_name__ )
if b < 0:
snake_case : int = (b % n + n) % n
return b
def a_ ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
"""simple docstring"""
snake_case , snake_case : Dict = invert_modulo(__magic_name__ , __magic_name__ ), invert_modulo(__magic_name__ , __magic_name__ )
snake_case : Union[str, Any] = na * na
snake_case : int = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='chinese_remainder_theorem', verbose=True)
testmod(name='chinese_remainder_theorem2', verbose=True)
testmod(name='invert_modulo', verbose=True)
testmod(name='extended_euclid', verbose=True)
| 598 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class a_ ( a ):
A__ : Optional[Any] = 'openai/whisper-base'
A__ : Optional[Any] = (
'This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '
'transcribed text.'
)
A__ : Union[str, Any] = 'transcriber'
A__ : Optional[int] = WhisperProcessor
A__ : List[str] = WhisperForConditionalGeneration
A__ : List[Any] = ['audio']
A__ : Optional[int] = ['text']
def lowerCAmelCase( self : List[Any] , UpperCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
return self.pre_processor(UpperCAmelCase__ , return_tensors='''pt''' ).input_features
def lowerCAmelCase( self : Dict , UpperCAmelCase__ : Any ):
"""simple docstring"""
return self.model.generate(inputs=UpperCAmelCase__ )
def lowerCAmelCase( self : Dict , UpperCAmelCase__ : List[Any] ):
"""simple docstring"""
return self.pre_processor.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )[0]
| 598 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCAmelCase_ = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class lowerCamelCase__( _lowercase):
UpperCAmelCase__ : Tuple = '''albert'''
def __init__( self: Any , UpperCamelCase_: List[str]=3_00_00 , UpperCamelCase_: Tuple=1_28 , UpperCamelCase_: Tuple=40_96 , UpperCamelCase_: Optional[int]=12 , UpperCamelCase_: Dict=1 , UpperCamelCase_: Optional[int]=64 , UpperCamelCase_: str=1_63_84 , UpperCamelCase_: Union[str, Any]=1 , UpperCamelCase_: List[Any]="gelu_new" , UpperCamelCase_: Optional[Any]=0 , UpperCamelCase_: Any=0 , UpperCamelCase_: Dict=5_12 , UpperCamelCase_: Dict=2 , UpperCamelCase_: Optional[Any]=0.02 , UpperCamelCase_: Optional[int]=1E-12 , UpperCamelCase_: Dict=0.1 , UpperCamelCase_: List[Any]="absolute" , UpperCamelCase_: Optional[int]=0 , UpperCamelCase_: Optional[Any]=2 , UpperCamelCase_: List[str]=3 , **UpperCamelCase_: List[Any] , ):
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
__lowerCamelCase = vocab_size
__lowerCamelCase = embedding_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_hidden_groups
__lowerCamelCase = num_attention_heads
__lowerCamelCase = inner_group_num
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = classifier_dropout_prob
__lowerCamelCase = position_embedding_type
class lowerCamelCase__( _lowercase):
@property
def lowerCAmelCase__ ( self: List[str] ):
if self.task == "multiple-choice":
__lowerCamelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__lowerCamelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 700 |
from ... import PretrainedConfig
UpperCAmelCase_ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : Dict = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
UpperCAmelCase__ : Dict = 'nezha'
def __init__( self: Dict , UpperCamelCase_: Any=2_11_28 , UpperCamelCase_: Optional[int]=7_68 , UpperCamelCase_: Optional[int]=12 , UpperCamelCase_: List[str]=12 , UpperCamelCase_: Optional[int]=30_72 , UpperCamelCase_: Optional[int]="gelu" , UpperCamelCase_: Optional[Any]=0.1 , UpperCamelCase_: str=0.1 , UpperCamelCase_: Union[str, Any]=5_12 , UpperCamelCase_: Any=64 , UpperCamelCase_: Dict=2 , UpperCamelCase_: int=0.02 , UpperCamelCase_: Optional[Any]=1E-12 , UpperCamelCase_: Tuple=0.1 , UpperCamelCase_: Any=0 , UpperCamelCase_: str=2 , UpperCamelCase_: Optional[int]=3 , UpperCamelCase_: str=True , **UpperCamelCase_: Any , ):
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_act
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = max_relative_position
__lowerCamelCase = type_vocab_size
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = classifier_dropout
__lowerCamelCase = use_cache
| 80 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowercase_ = _symbol_database.Default()
lowercase_ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowercase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowercase_ = None
lowercase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowercase_ = 45
lowercase_ = 15_81
lowercase_ = 15_17
lowercase_ = 15_70
lowercase_ = 15_84
lowercase_ = 17_93
lowercase_ = 17_95
lowercase_ = 19_16
lowercase_ = 18_64
lowercase_ = 19_05
lowercase_ = 19_19
lowercase_ = 24_29
lowercase_ = 22_08
lowercase_ = 24_18
lowercase_ = 23_23
lowercase_ = 24_07
# @@protoc_insertion_point(module_scope)
| 562 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class A :
'''simple docstring'''
def __init__( self : List[str] , _UpperCamelCase : Tuple , _UpperCamelCase : List[str]=13 , _UpperCamelCase : Union[str, Any]=7 , _UpperCamelCase : List[str]=True , _UpperCamelCase : Tuple=True , _UpperCamelCase : Dict=True , _UpperCamelCase : Any=True , _UpperCamelCase : Union[str, Any]=99 , _UpperCamelCase : Tuple=32 , _UpperCamelCase : List[Any]=5 , _UpperCamelCase : Tuple=4 , _UpperCamelCase : List[Any]=4 , _UpperCamelCase : Tuple="gelu" , _UpperCamelCase : Any=0.0 , _UpperCamelCase : List[Any]=0.1 , _UpperCamelCase : Optional[int]=True , _UpperCamelCase : Tuple=512 , _UpperCamelCase : str=16 , _UpperCamelCase : str=2 , _UpperCamelCase : Tuple=0.0_2 , _UpperCamelCase : List[str]=3 , _UpperCamelCase : int=4 , _UpperCamelCase : Tuple=None , ):
_lowercase: Any = parent
_lowercase: int = batch_size
_lowercase: Tuple = seq_length
_lowercase: Any = is_training
_lowercase: Any = use_input_mask
_lowercase: Union[str, Any] = use_token_type_ids
_lowercase: int = use_labels
_lowercase: int = vocab_size
_lowercase: int = hidden_size
_lowercase: Any = num_hidden_layers
_lowercase: Tuple = num_attention_heads
_lowercase: List[str] = intermediate_multiple_size
_lowercase: Dict = hidden_act
_lowercase: Optional[int] = hidden_dropout
_lowercase: Optional[int] = attention_dropout
_lowercase: Dict = weight_tying
_lowercase: Union[str, Any] = max_position_embeddings
_lowercase: str = type_vocab_size
_lowercase: str = type_sequence_label_size
_lowercase: Optional[int] = initializer_range
_lowercase: List[Any] = num_labels
_lowercase: Any = num_choices
_lowercase: Optional[Any] = scope
def UpperCAmelCase__ ( self : Optional[Any]):
_lowercase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowercase: Optional[Any] = None
if self.use_input_mask:
_lowercase: List[str] = random_attention_mask([self.batch_size, self.seq_length])
_lowercase: Dict = None
if self.use_labels:
_lowercase: Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_lowercase: Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase__ ( self : Optional[int]):
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_UpperCamelCase , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self : int):
_lowercase , _lowercase , _lowercase , _lowercase: int = self.prepare_config_and_inputs()
_lowercase: str = True
return config, input_ids, input_mask, token_labels
def UpperCAmelCase__ ( self : Optional[int] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : Dict):
_lowercase: Dict = GPTNeoXJapaneseModel(config=_UpperCamelCase)
model.to(_UpperCamelCase)
model.eval()
_lowercase: Optional[Any] = model(_UpperCamelCase , attention_mask=_UpperCamelCase)
_lowercase: Optional[int] = model(_UpperCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : Any):
_lowercase: Tuple = True
_lowercase: Optional[int] = GPTNeoXJapaneseModel(_UpperCamelCase)
model.to(_UpperCamelCase)
model.eval()
_lowercase: int = model(_UpperCamelCase , attention_mask=_UpperCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : List[str] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : str):
_lowercase: Union[str, Any] = GPTNeoXJapaneseForCausalLM(config=_UpperCamelCase)
model.to(_UpperCamelCase)
model.eval()
_lowercase: Optional[int] = model(_UpperCamelCase , attention_mask=_UpperCamelCase , labels=_UpperCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCAmelCase__ ( self : List[str] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any):
_lowercase: Tuple = True
_lowercase: Optional[Any] = GPTNeoXJapaneseForCausalLM(config=_UpperCamelCase)
model.to(_UpperCamelCase)
model.eval()
# first forward pass
_lowercase: int = model(_UpperCamelCase , attention_mask=_UpperCamelCase , use_cache=_UpperCamelCase)
_lowercase: str = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowercase: List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size)
_lowercase: Union[str, Any] = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
_lowercase: List[Any] = torch.cat([input_ids, next_tokens] , dim=-1)
_lowercase: List[Any] = torch.cat([input_mask, next_mask] , dim=-1)
_lowercase: Union[str, Any] = model(_UpperCamelCase , attention_mask=_UpperCamelCase , output_hidden_states=_UpperCamelCase)
_lowercase: Optional[int] = output_from_no_past["hidden_states"][0]
_lowercase: Tuple = model(
_UpperCamelCase , attention_mask=_UpperCamelCase , past_key_values=_UpperCamelCase , output_hidden_states=_UpperCamelCase , )["hidden_states"][0]
# select random slice
_lowercase: Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1]).item()
_lowercase: Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowercase: Optional[int] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_UpperCamelCase , _UpperCamelCase , atol=1e-3))
def UpperCAmelCase__ ( self : Dict):
_lowercase: Union[str, Any] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase , _lowercase: Tuple = config_and_inputs
_lowercase: Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase : List[str] = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
lowerCamelCase : Optional[int] = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
lowerCamelCase : List[Any] = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
lowerCamelCase : int = False
lowerCamelCase : Optional[int] = False
lowerCamelCase : int = False
lowerCamelCase : List[str] = False
def UpperCAmelCase__ ( self : Tuple):
_lowercase: Optional[int] = GPTNeoXJapaneseModelTester(self)
_lowercase: List[str] = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37)
def UpperCAmelCase__ ( self : Optional[Any]):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : List[str]):
_lowercase , _lowercase , _lowercase , _lowercase: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : Optional[int]):
_lowercase , _lowercase , _lowercase , _lowercase: int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : Optional[Any]):
# This regression test was failing with PyTorch < 1.3
_lowercase , _lowercase , _lowercase , _lowercase: Any = self.model_tester.prepare_config_and_inputs_for_decoder()
_lowercase: int = None
self.model_tester.create_and_check_model_as_decoder(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : Optional[Any]):
_lowercase , _lowercase , _lowercase , _lowercase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : str):
_lowercase: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*_UpperCamelCase)
@slow
def UpperCAmelCase__ ( self : Any):
_lowercase: List[str] = "abeja/gpt-neox-japanese-2.7b"
_lowercase: Dict = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
_lowercase: Union[str, Any] = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
_lowercase: str = GPTNeoXJapaneseTokenizer.from_pretrained(_UpperCamelCase)
_lowercase: Dict = GPTNeoXJapaneseForCausalLM.from_pretrained(_UpperCamelCase)
_lowercase: List[Any] = []
for prompt in prompts:
_lowercase: List[str] = tokenizer(_UpperCamelCase , return_tensors="pt").input_ids
_lowercase: List[Any] = model.generate(_UpperCamelCase , max_length=50)
_lowercase: str = tokenizer.batch_decode(_UpperCamelCase , skip_special_tokens=_UpperCamelCase)
predicted_outputs += generated_string
self.assertListEqual(_UpperCamelCase , _UpperCamelCase)
| 226 | 0 |
class A_ :
def __init__( self : List[str] , snake_case__ : Optional[int] ):
lowercase = set_counts
lowercase = max(__lowerCamelCase )
lowercase = len(__lowerCamelCase )
lowercase = [1] * num_sets
lowercase = list(range(__lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self : int , snake_case__ : Tuple , snake_case__ : Any ):
lowercase = self.get_parent(__lowerCamelCase )
lowercase = self.get_parent(__lowerCamelCase )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
lowercase = 0
lowercase = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
lowercase = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
lowercase = 0
lowercase = src_parent
lowercase = self.set_counts[src_parent]
lowercase = max(self.max_set , __lowerCamelCase )
return True
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , snake_case__ : Optional[int] ):
if self.parents[disj_set] == disj_set:
return disj_set
lowercase = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 701 |
from numpy import exp, pi, sqrt
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ = 0.0 ,lowerCAmelCase__ = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 | 0 |
'''simple docstring'''
_lowerCAmelCase :List[str] = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def __lowerCAmelCase ( a_ ) -> bytes:
'''simple docstring'''
if not isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(a_ )
SCREAMING_SNAKE_CASE : Dict = ''.join(bin(a_ )[2:].zfill(8 ) for byte in data )
SCREAMING_SNAKE_CASE : Union[str, Any] = len(a_ ) % 6 != 0
if padding_needed:
# The padding that will be added later
SCREAMING_SNAKE_CASE : Optional[int] = B'=' * ((6 - len(a_ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(a_ ) % 6)
else:
SCREAMING_SNAKE_CASE : int = B''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(a_ ) , 6 ) ).encode()
+ padding
)
def __lowerCAmelCase ( a_ ) -> bytes:
'''simple docstring'''
if not isinstance(a_ , a_ ) and not isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE : int = (
'argument should be a bytes-like object or ASCII string, '
f"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(a_ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(a_ , a_ ):
try:
SCREAMING_SNAKE_CASE : List[Any] = encoded_data.decode('utf-8' )
except UnicodeDecodeError:
raise ValueError('base64 encoded data should only contain ASCII characters' )
SCREAMING_SNAKE_CASE : int = encoded_data.count('=' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(a_ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
SCREAMING_SNAKE_CASE : Optional[Any] = encoded_data[:-padding]
SCREAMING_SNAKE_CASE : Optional[int] = ''.join(
bin(B64_CHARSET.index(a_ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
SCREAMING_SNAKE_CASE : Any = ''.join(
bin(B64_CHARSET.index(a_ ) )[2:].zfill(6 ) for char in encoded_data )
SCREAMING_SNAKE_CASE : Optional[int] = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(a_ ) , 8 )
]
return bytes(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 251 | '''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
_lowerCAmelCase :Optional[Any] = """Create a default config file for Accelerate with only a few flags set."""
def __lowerCAmelCase ( a_="no" , a_ = default_json_config_file , a_ = False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = Path(a_ )
path.parent.mkdir(parents=a_ , exist_ok=a_ )
if path.exists():
print(
f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
SCREAMING_SNAKE_CASE : List[str] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cuda.device_count()
SCREAMING_SNAKE_CASE : str = num_gpus
SCREAMING_SNAKE_CASE : Dict = False
if num_gpus > 1:
SCREAMING_SNAKE_CASE : List[str] = 'MULTI_GPU'
else:
SCREAMING_SNAKE_CASE : Optional[int] = 'NO'
elif is_xpu_available() and use_xpu:
SCREAMING_SNAKE_CASE : List[str] = torch.xpu.device_count()
SCREAMING_SNAKE_CASE : List[Any] = num_xpus
SCREAMING_SNAKE_CASE : Optional[int] = False
if num_xpus > 1:
SCREAMING_SNAKE_CASE : List[Any] = 'MULTI_XPU'
else:
SCREAMING_SNAKE_CASE : List[Any] = 'NO'
elif is_npu_available():
SCREAMING_SNAKE_CASE : List[str] = torch.npu.device_count()
SCREAMING_SNAKE_CASE : Any = num_npus
SCREAMING_SNAKE_CASE : Dict = False
if num_npus > 1:
SCREAMING_SNAKE_CASE : Any = 'MULTI_NPU'
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = 'NO'
else:
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : List[Any] = 'NO'
SCREAMING_SNAKE_CASE : Any = ClusterConfig(**a_ )
config.to_json_file(a_ )
return path
def __lowerCAmelCase ( a_ , a_ ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = parser.add_parser('default' , parents=a_ , help=a_ , formatter_class=a_ )
parser.add_argument(
'--config_file' , default=a_ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=a_ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=a_ )
return parser
def __lowerCAmelCase ( a_ ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"""accelerate configuration saved at {config_file}""" )
| 251 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowerCAmelCase ( ):
lowercase__ = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
lowercase__ = parser.parse_args_into_dataclasses()[0]
lowercase__ = TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_ )
try:
lowercase__ = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase__ = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowercase__ = " ".join(str(SCREAMING_SNAKE_CASE_ ).split(" " )[:-1] )
lowercase__ = ""
lowercase__ = eval(str(SCREAMING_SNAKE_CASE_ ).split(" " )[-1] )
lowercase__ = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowercase__ = full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_ )
raise ValueError(SCREAMING_SNAKE_CASE_ )
benchmark.run()
if __name__ == "__main__":
main()
| 37 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_xmod""": [
"""XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XmodConfig""",
"""XmodOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XmodForCausalLM""",
"""XmodForMaskedLM""",
"""XmodForMultipleChoice""",
"""XmodForQuestionAnswering""",
"""XmodForSequenceClassification""",
"""XmodForTokenClassification""",
"""XmodModel""",
"""XmodPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 37 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 505 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_SCREAMING_SNAKE_CASE : Union[str, Any] = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class A :
'''simple docstring'''
lowerCamelCase : Union[str, Any] = PegasusConfig
lowerCamelCase : Any = {}
lowerCamelCase : int = """gelu"""
def __init__( self : Union[str, Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[int]=13 , _UpperCamelCase : List[Any]=7 , _UpperCamelCase : Dict=True , _UpperCamelCase : Dict=False , _UpperCamelCase : Dict=99 , _UpperCamelCase : str=32 , _UpperCamelCase : Dict=5 , _UpperCamelCase : str=4 , _UpperCamelCase : Any=37 , _UpperCamelCase : List[str]=0.1 , _UpperCamelCase : Optional[Any]=0.1 , _UpperCamelCase : Any=20 , _UpperCamelCase : List[str]=2 , _UpperCamelCase : int=1 , _UpperCamelCase : List[str]=0 , ):
_lowercase: str = parent
_lowercase: int = batch_size
_lowercase: Optional[Any] = seq_length
_lowercase: Dict = is_training
_lowercase: List[str] = use_labels
_lowercase: Dict = vocab_size
_lowercase: Optional[Any] = hidden_size
_lowercase: Optional[int] = num_hidden_layers
_lowercase: int = num_attention_heads
_lowercase: Tuple = intermediate_size
_lowercase: Optional[int] = hidden_dropout_prob
_lowercase: Union[str, Any] = attention_probs_dropout_prob
_lowercase: Tuple = max_position_embeddings
_lowercase: Optional[Any] = eos_token_id
_lowercase: List[Any] = pad_token_id
_lowercase: int = bos_token_id
def UpperCAmelCase__ ( self : Tuple):
_lowercase: int = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_lowercase: str = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_lowercase: List[str] = np.concatenate([input_ids, eos_tensor] , axis=1)
_lowercase: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowercase: Tuple = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_lowercase: Any = prepare_pegasus_inputs_dict(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
return config, inputs_dict
def UpperCAmelCase__ ( self : int , _UpperCamelCase : Any , _UpperCamelCase : List[Any] , _UpperCamelCase : str):
_lowercase: List[Any] = 20
_lowercase: Optional[int] = model_class_name(_UpperCamelCase)
_lowercase: Union[str, Any] = model.encode(inputs_dict["input_ids"])
_lowercase , _lowercase: List[str] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_lowercase: Optional[Any] = model.init_cache(decoder_input_ids.shape[0] , _UpperCamelCase , _UpperCamelCase)
_lowercase: Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4")
_lowercase: Optional[int] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowercase: int = model.decode(
decoder_input_ids[:, :-1] , _UpperCamelCase , decoder_attention_mask=_UpperCamelCase , past_key_values=_UpperCamelCase , decoder_position_ids=_UpperCamelCase , )
_lowercase: List[str] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4")
_lowercase: str = model.decode(
decoder_input_ids[:, -1:] , _UpperCamelCase , decoder_attention_mask=_UpperCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_UpperCamelCase , )
_lowercase: Optional[Any] = model.decode(_UpperCamelCase , _UpperCamelCase)
_lowercase: List[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=f"Max diff is {diff}")
def UpperCAmelCase__ ( self : Optional[int] , _UpperCamelCase : Dict , _UpperCamelCase : Any , _UpperCamelCase : List[Any]):
_lowercase: Optional[Any] = 20
_lowercase: List[Any] = model_class_name(_UpperCamelCase)
_lowercase: str = model.encode(inputs_dict["input_ids"])
_lowercase , _lowercase: Tuple = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
_lowercase: int = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_lowercase: Any = model.init_cache(decoder_input_ids.shape[0] , _UpperCamelCase , _UpperCamelCase)
_lowercase: Optional[int] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowercase: List[str] = model.decode(
decoder_input_ids[:, :-1] , _UpperCamelCase , decoder_attention_mask=_UpperCamelCase , past_key_values=_UpperCamelCase , decoder_position_ids=_UpperCamelCase , )
_lowercase: int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4")
_lowercase: Dict = model.decode(
decoder_input_ids[:, -1:] , _UpperCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_UpperCamelCase , decoder_position_ids=_UpperCamelCase , )
_lowercase: Tuple = model.decode(_UpperCamelCase , _UpperCamelCase , decoder_attention_mask=_UpperCamelCase)
_lowercase: int = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=f"Max diff is {diff}")
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None , ):
if attention_mask is None:
_lowercase: int = np.not_equal(__magic_name__ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_lowercase: Dict = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class A ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase : Dict = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowerCamelCase : Any = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowerCamelCase : Tuple = True
lowerCamelCase : List[str] = False
lowerCamelCase : int = False
lowerCamelCase : Dict = False
def UpperCAmelCase__ ( self : List[Any]):
_lowercase: Any = FlaxPegasusModelTester(self)
_lowercase: Optional[int] = ConfigTester(self , config_class=_UpperCamelCase)
def UpperCAmelCase__ ( self : str):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Tuple):
_lowercase , _lowercase: List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : Tuple):
_lowercase , _lowercase: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase)
def UpperCAmelCase__ ( self : Optional[int]):
_lowercase , _lowercase: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_lowercase: Union[str, Any] = self._prepare_for_class(_UpperCamelCase , _UpperCamelCase)
_lowercase: Union[str, Any] = model_class(_UpperCamelCase)
@jax.jit
def encode_jitted(_UpperCamelCase : List[str] , _UpperCamelCase : Optional[int]=None , **_UpperCamelCase : int):
return model.encode(input_ids=_UpperCamelCase , attention_mask=_UpperCamelCase)
with self.subTest("JIT Enabled"):
_lowercase: Tuple = encode_jitted(**_UpperCamelCase).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
_lowercase: Tuple = encode_jitted(**_UpperCamelCase).to_tuple()
self.assertEqual(len(_UpperCamelCase) , len(_UpperCamelCase))
for jitted_output, output in zip(_UpperCamelCase , _UpperCamelCase):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase__ ( self : Any):
_lowercase , _lowercase: Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_lowercase: Union[str, Any] = model_class(_UpperCamelCase)
_lowercase: Any = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"])
_lowercase: int = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(_UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Dict):
return model.decode(
decoder_input_ids=_UpperCamelCase , decoder_attention_mask=_UpperCamelCase , encoder_outputs=_UpperCamelCase , )
with self.subTest("JIT Enabled"):
_lowercase: Dict = decode_jitted(**_UpperCamelCase).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
_lowercase: Union[str, Any] = decode_jitted(**_UpperCamelCase).to_tuple()
self.assertEqual(len(_UpperCamelCase) , len(_UpperCamelCase))
for jitted_output, output in zip(_UpperCamelCase , _UpperCamelCase):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase__ ( self : str):
for model_class_name in self.all_model_classes:
_lowercase: Tuple = model_class_name.from_pretrained("google/pegasus-large" , from_pt=_UpperCamelCase)
_lowercase: Union[str, Any] = np.ones((1, 1))
_lowercase: List[str] = model(_UpperCamelCase)
self.assertIsNotNone(_UpperCamelCase)
@slow
def UpperCAmelCase__ ( self : Optional[Any]):
_lowercase: Union[str, Any] = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
_lowercase: int = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
_lowercase: int = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
_lowercase: str = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
_lowercase: Any = tokenizer(_UpperCamelCase , return_tensors="np" , truncation=_UpperCamelCase , max_length=512 , padding=_UpperCamelCase)
_lowercase: Union[str, Any] = model.generate(**_UpperCamelCase , num_beams=2).sequences
_lowercase: Optional[int] = tokenizer.batch_decode(_UpperCamelCase , skip_special_tokens=_UpperCamelCase)
assert tgt_text == decoded
| 226 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase (lowerCAmelCase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : str = ConsistencyModelPipeline
_SCREAMING_SNAKE_CASE : List[Any] = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_SCREAMING_SNAKE_CASE : Union[str, Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
_SCREAMING_SNAKE_CASE : str = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
@property
def __snake_case ( self :Dict ) ->Any:
lowercase : Optional[Any] = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet""" , )
return unet
@property
def __snake_case ( self :List[Any] ) ->Dict:
lowercase : Union[str, Any] = UNetaDModel.from_pretrained(
"""diffusers/consistency-models-test""" , subfolder="""test_unet_class_cond""" , )
return unet
def __snake_case ( self :Optional[Any] , __magic_name__ :int=False ) ->Union[str, Any]:
if class_cond:
lowercase : str = self.dummy_cond_unet
else:
lowercase : Tuple = self.dummy_uncond_unet
# Default to CM multistep sampler
lowercase : List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
lowercase : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
}
return components
def __snake_case ( self :int , __magic_name__ :int , __magic_name__ :List[str]=0 ) ->int:
if str(_SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
lowercase : str = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
lowercase : Optional[Any] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
lowercase : Optional[Any] = {
"""batch_size""": 1,
"""num_inference_steps""": None,
"""timesteps""": [22, 0],
"""generator""": generator,
"""output_type""": """np""",
}
return inputs
def __snake_case ( self :List[Any] ) ->List[Any]:
lowercase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase : Dict = self.get_dummy_components()
lowercase : int = ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE )
lowercase : int = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 32, 32, 3)
lowercase : Optional[int] = image[0, -3:, -3:, -1]
lowercase : Union[str, Any] = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __snake_case ( self :str ) ->str:
lowercase : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase : str = self.get_dummy_components(class_cond=_SCREAMING_SNAKE_CASE )
lowercase : Optional[Any] = ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Tuple = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowercase : Dict = 0
lowercase : Union[str, Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 32, 32, 3)
lowercase : Dict = image[0, -3:, -3:, -1]
lowercase : Any = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __snake_case ( self :List[str] ) ->Optional[int]:
lowercase : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase : List[str] = self.get_dummy_components()
lowercase : Tuple = ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE )
lowercase : Tuple = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowercase : Optional[Any] = 1
lowercase : List[str] = None
lowercase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 32, 32, 3)
lowercase : Dict = image[0, -3:, -3:, -1]
lowercase : int = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __snake_case ( self :str ) ->Optional[Any]:
lowercase : Optional[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase : List[Any] = self.get_dummy_components(class_cond=_SCREAMING_SNAKE_CASE )
lowercase : Any = ConsistencyModelPipeline(**_SCREAMING_SNAKE_CASE )
lowercase : Any = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = 1
lowercase : List[str] = None
lowercase : List[Any] = 0
lowercase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 32, 32, 3)
lowercase : Optional[Any] = image[0, -3:, -3:, -1]
lowercase : str = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class UpperCamelCase (unittest.TestCase ):
def __snake_case ( self :Any ) ->str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self :List[Any] , __magic_name__ :List[str]=0 , __magic_name__ :Dict=False , __magic_name__ :Tuple="cpu" , __magic_name__ :Optional[Any]=torch.floataa , __magic_name__ :str=(1, 3, 64, 64) ) ->Tuple:
lowercase : int = torch.manual_seed(_SCREAMING_SNAKE_CASE )
lowercase : List[Any] = {
"""num_inference_steps""": None,
"""timesteps""": [22, 0],
"""class_labels""": 0,
"""generator""": generator,
"""output_type""": """np""",
}
if get_fixed_latents:
lowercase : str = self.get_fixed_latents(seed=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE , shape=_SCREAMING_SNAKE_CASE )
lowercase : Any = latents
return inputs
def __snake_case ( self :int , __magic_name__ :List[Any]=0 , __magic_name__ :Tuple="cpu" , __magic_name__ :Any=torch.floataa , __magic_name__ :Dict=(1, 3, 64, 64) ) ->Optional[int]:
if type(_SCREAMING_SNAKE_CASE ) == str:
lowercase : Union[str, Any] = torch.device(_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
lowercase : Any = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
return latents
def __snake_case ( self :Optional[int] ) ->Any:
lowercase : Any = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowercase : Union[str, Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
lowercase : Optional[int] = ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
pipe.to(torch_device=_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = self.get_inputs()
lowercase : Union[str, Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 64, 64, 3)
lowercase : str = image[0, -3:, -3:, -1]
lowercase : Tuple = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __snake_case ( self :str ) ->str:
lowercase : Union[str, Any] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowercase : str = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
lowercase : Union[str, Any] = ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
pipe.to(torch_device=_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Tuple = self.get_inputs()
lowercase : Tuple = 1
lowercase : Any = None
lowercase : Tuple = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 64, 64, 3)
lowercase : str = image[0, -3:, -3:, -1]
lowercase : Union[str, Any] = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
@require_torch_a
def __snake_case ( self :Dict ) ->Any:
lowercase : List[str] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowercase : Union[str, Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
lowercase : List[str] = ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
pipe.to(torch_device=_SCREAMING_SNAKE_CASE , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = self.get_inputs(get_fixed_latents=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=_SCREAMING_SNAKE_CASE , enable_math=_SCREAMING_SNAKE_CASE , enable_mem_efficient=_SCREAMING_SNAKE_CASE ):
lowercase : List[Any] = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 64, 64, 3)
lowercase : str = image[0, -3:, -3:, -1]
lowercase : str = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@require_torch_a
def __snake_case ( self :str ) ->List[Any]:
lowercase : Union[str, Any] = UNetaDModel.from_pretrained("""diffusers/consistency_models""" , subfolder="""diffusers_cd_imagenet64_l2""" )
lowercase : Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
lowercase : Dict = ConsistencyModelPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
pipe.to(torch_device=_SCREAMING_SNAKE_CASE , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowercase : Tuple = self.get_inputs(get_fixed_latents=_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
lowercase : Optional[int] = 1
lowercase : Dict = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=_SCREAMING_SNAKE_CASE , enable_math=_SCREAMING_SNAKE_CASE , enable_mem_efficient=_SCREAMING_SNAKE_CASE ):
lowercase : Any = pipe(**_SCREAMING_SNAKE_CASE ).images
assert image.shape == (1, 64, 64, 3)
lowercase : Any = image[0, -3:, -3:, -1]
lowercase : Optional[Any] = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 709 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_lowerCAmelCase = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""" )
@require_torch
@require_tf
@slow
class UpperCamelCase (unittest.TestCase ):
def __snake_case ( self :str , __magic_name__ :Path , __magic_name__ :Union[str, None] = None , __magic_name__ :Union[List[str], None] = None , __magic_name__ :Union[str, List[str], None] = None , __magic_name__ :bool = True , ) ->Optional[Any]:
lowercase : Dict = [file for file in os.listdir(__magic_name__ ) if os.path.isfile(os.path.join(__magic_name__ , __magic_name__ ) )]
if identifier is not None:
lowercase : Tuple = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(__magic_name__ , __magic_name__ ):
for n_ in n_identifier:
lowercase : List[str] = [file for file in files if n_ not in file]
else:
lowercase : str = [file for file in files if n_identifier not in file]
lowercase : List[str] = ignore_files or []
ignore_files.append("""__init__.py""" )
lowercase : Tuple = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("""Testing""" , __magic_name__ )
if only_modules:
lowercase : List[Any] = file.split(""".""" )[0]
try:
lowercase : Dict = getattr(__magic_name__ , __magic_name__ )
lowercase : Dict = doctest.DocTestSuite(__magic_name__ )
lowercase : Optional[int] = unittest.TextTestRunner().run(__magic_name__ )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(f"""{module_identifier} is not a module.""" )
else:
lowercase : List[str] = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def __snake_case ( self :Optional[Any] ) ->Dict:
lowercase : int = Path("""src/transformers""" )
lowercase : Tuple = """modeling"""
lowercase : List[str] = [
"""modeling_ctrl.py""",
"""modeling_tf_ctrl.py""",
]
self.analyze_directory(__magic_name__ , identifier=__magic_name__ , ignore_files=__magic_name__ )
def __snake_case ( self :str ) ->str:
lowercase : Optional[int] = Path("""src/transformers""" )
lowercase : Tuple = """tokenization"""
self.analyze_directory(__magic_name__ , identifier=__magic_name__ )
def __snake_case ( self :Optional[int] ) ->str:
lowercase : Tuple = Path("""src/transformers""" )
lowercase : List[Any] = """configuration"""
self.analyze_directory(__magic_name__ , identifier=__magic_name__ )
def __snake_case ( self :Tuple ) ->Any:
lowercase : str = Path("""src/transformers""" )
lowercase : Optional[int] = ["""configuration""", """modeling""", """tokenization"""]
self.analyze_directory(__magic_name__ , n_identifier=__magic_name__ )
def __snake_case ( self :List[str] ) ->Tuple:
lowercase : List[str] = Path("""docs/source""" )
lowercase : int = ["""favicon.ico"""]
self.analyze_directory(__magic_name__ , ignore_files=__magic_name__ , only_modules=__magic_name__ )
| 348 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_a: Dict = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def __lowerCAmelCase ( A , A , A , A , A , A , A , A=False , ):
output_path.parent.mkdir(parents=A , exist_ok=A )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
A , A , f=output_path.as_posix() , input_names=A , output_names=A , dynamic_axes=A , do_constant_folding=A , use_external_data_format=A , enable_onnx_checker=A , opset_version=A , )
else:
export(
A , A , f=output_path.as_posix() , input_names=A , output_names=A , dynamic_axes=A , do_constant_folding=A , opset_version=A , )
@torch.no_grad()
def __lowerCAmelCase ( A , A , A , A = False ):
UpperCAmelCase_ = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCAmelCase_ = "cuda"
elif fpaa and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
else:
UpperCAmelCase_ = "cpu"
UpperCAmelCase_ = StableDiffusionPipeline.from_pretrained(A , torch_dtype=A ).to(A )
UpperCAmelCase_ = Path(A )
# TEXT ENCODER
UpperCAmelCase_ = pipeline.text_encoder.config.max_position_embeddings
UpperCAmelCase_ = pipeline.text_encoder.config.hidden_size
UpperCAmelCase_ = pipeline.tokenizer(
"A sample prompt" , padding="max_length" , max_length=pipeline.tokenizer.model_max_length , truncation=A , return_tensors="pt" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=A , dtype=torch.intaa )) , output_path=output_path / "text_encoder" / "model.onnx" , ordered_input_names=["input_ids"] , output_names=["last_hidden_state", "pooler_output"] , dynamic_axes={
"input_ids": {0: "batch", 1: "sequence"},
} , opset=A , )
del pipeline.text_encoder
# UNET
UpperCAmelCase_ = pipeline.unet.config.in_channels
UpperCAmelCase_ = pipeline.unet.config.sample_size
UpperCAmelCase_ = output_path / "unet" / "model.onnx"
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , A , A , A ).to(device=A , dtype=A ),
torch.randn(2 ).to(device=A , dtype=A ),
torch.randn(2 , A , A ).to(device=A , dtype=A ),
False,
) , output_path=A , ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"] , output_names=["out_sample"] , dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"timestep": {0: "batch"},
"encoder_hidden_states": {0: "batch", 1: "sequence"},
} , opset=A , use_external_data_format=A , )
UpperCAmelCase_ = str(unet_path.absolute().as_posix() )
UpperCAmelCase_ = os.path.dirname(A )
UpperCAmelCase_ = onnx.load(A )
# clean up existing tensor files
shutil.rmtree(A )
os.mkdir(A )
# collate external tensor files into one
onnx.save_model(
A , A , save_as_external_data=A , all_tensors_to_one_file=A , location="weights.pb" , convert_attribute=A , )
del pipeline.unet
# VAE ENCODER
UpperCAmelCase_ = pipeline.vae
UpperCAmelCase_ = vae_encoder.config.in_channels
UpperCAmelCase_ = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
UpperCAmelCase_ = lambda A , A : vae_encoder.encode(A , A )[0].sample()
onnx_export(
A , model_args=(
torch.randn(1 , A , A , A ).to(device=A , dtype=A ),
False,
) , output_path=output_path / "vae_encoder" / "model.onnx" , ordered_input_names=["sample", "return_dict"] , output_names=["latent_sample"] , dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=A , )
# VAE DECODER
UpperCAmelCase_ = pipeline.vae
UpperCAmelCase_ = vae_decoder.config.latent_channels
UpperCAmelCase_ = vae_decoder.config.out_channels
# forward only through the decoder part
UpperCAmelCase_ = vae_encoder.decode
onnx_export(
A , model_args=(
torch.randn(1 , A , A , A ).to(device=A , dtype=A ),
False,
) , output_path=output_path / "vae_decoder" / "model.onnx" , ordered_input_names=["latent_sample", "return_dict"] , output_names=["sample"] , dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=A , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
UpperCAmelCase_ = pipeline.safety_checker
UpperCAmelCase_ = safety_checker.config.vision_config.num_channels
UpperCAmelCase_ = safety_checker.config.vision_config.image_size
UpperCAmelCase_ = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , A , A , A , ).to(device=A , dtype=A ),
torch.randn(1 , A , A , A ).to(device=A , dtype=A ),
) , output_path=output_path / "safety_checker" / "model.onnx" , ordered_input_names=["clip_input", "images"] , output_names=["out_images", "has_nsfw_concepts"] , dynamic_axes={
"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
"images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
} , opset=A , )
del pipeline.safety_checker
UpperCAmelCase_ = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker" )
UpperCAmelCase_ = pipeline.feature_extractor
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / "unet" ) , scheduler=pipeline.scheduler , safety_checker=A , feature_extractor=A , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(A )
print("ONNX pipeline saved to" , A )
del pipeline
del onnx_pipeline
UpperCAmelCase_ = OnnxStableDiffusionPipeline.from_pretrained(A , provider="CPUExecutionProvider" )
print("ONNX pipeline is loadable" )
if __name__ == "__main__":
_a: Any = argparse.ArgumentParser()
parser.add_argument(
"""--model_path""",
type=str,
required=True,
help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
)
parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--opset""",
default=14,
type=int,
help="""The version of the ONNX operator set to use.""",
)
parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")
_a: Any = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa) | 162 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
_a: List[Any] = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def __lowerCAmelCase ( A ):
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
UpperCAmelCase_ = list(s_dict.keys() )
for key in keys:
UpperCAmelCase_ = r".*/layers_(\d+)"
UpperCAmelCase_ = key
if re.match(A , A ):
UpperCAmelCase_ = re.sub(r"layers_(\d+)" , r"block/\1/layer" , A )
UpperCAmelCase_ = r"(encoder|decoder)\/"
if re.match(A , A ):
UpperCAmelCase_ = re.match(A , A ).groups()
if groups[0] == "encoder":
UpperCAmelCase_ = re.sub(r"/mlp/" , r"/1/mlp/" , A )
UpperCAmelCase_ = re.sub(r"/pre_mlp_layer_norm/" , r"/1/layer_norm/" , A )
elif groups[0] == "decoder":
UpperCAmelCase_ = re.sub(r"/mlp/" , r"/2/mlp/" , A )
UpperCAmelCase_ = re.sub(r"/pre_mlp_layer_norm/" , r"/2/layer_norm/" , A )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
UpperCAmelCase_ = new_key.replace(A , A )
print(F"{key} -> {new_key}" )
UpperCAmelCase_ = s_dict.pop(A )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase_ = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase_ = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
UpperCAmelCase_ = s_dict[key].shape[0]
UpperCAmelCase_ = s_dict[key]
for idx in range(A ):
UpperCAmelCase_ = expert_weihts[idx]
print(F"{key} -> {key.replace('expert/' , 'nested fstring' )}" )
s_dict.pop(A )
return s_dict
_a: Any = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def __lowerCAmelCase ( A , A ):
# Convert a google style config to the hugging face fromat
import regex as re
with open(A , "r" ) as f:
UpperCAmelCase_ = f.read()
UpperCAmelCase_ = re.findall(r"(.*) = ([0-9.]*)" , A )
UpperCAmelCase_ = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
UpperCAmelCase_ = float(A ) if "." in value else int(A )
UpperCAmelCase_ = re.findall(r"(.*activations) = \(\'(.*)\',\)" , A )[0]
UpperCAmelCase_ = str(activation[1] )
UpperCAmelCase_ = num_experts
UpperCAmelCase_ = SwitchTransformersConfig(**A )
return config
def __lowerCAmelCase ( A , A , A=None , A="./" , A=8 ):
# Initialise PyTorch model
print(F"Loading flax weights from : {flax_checkpoint_path}" )
UpperCAmelCase_ = checkpoints.load_tax_checkpoint(A )
if gin_file is not None:
UpperCAmelCase_ = convert_gin_to_config(A , A )
else:
UpperCAmelCase_ = SwitchTransformersConfig.from_pretrained(A )
UpperCAmelCase_ = SwitchTransformersForConditionalGeneration(A )
UpperCAmelCase_ = flax_params["target"]
UpperCAmelCase_ = flatten_dict(A , sep="/" )
UpperCAmelCase_ = rename_keys(A )
UpperCAmelCase_ = unflatten_dict(A , sep="/" )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(A , A )
print(F"Save PyTorch model to {pytorch_dump_path}" )
pt_model.save_pretrained(A )
if __name__ == "__main__":
_a: Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
""" model architecture. If not provided, a `gin_file` has to be provided."""
),
)
parser.add_argument(
"""--gin_file""",
default=None,
type=str,
required=False,
help="""Path to the gin config file. If not provided, a `config_file` has to be passed """,
)
parser.add_argument(
"""--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
)
parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
_a: List[Any] = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
) | 162 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = "ZinengTang/tvlt-base"
lowercase__ = tempfile.mkdtemp()
def UpperCAmelCase ( self :int , **_lowercase :Optional[int] ):
'''simple docstring'''
return TvltImageProcessor.from_pretrained(self.checkpoint , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , **_lowercase :Optional[int] ):
'''simple docstring'''
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_feature_extractor()
lowercase__ = TvltProcessor(image_processor=_lowercase , feature_extractor=_lowercase )
processor.save_pretrained(self.tmpdirname )
lowercase__ = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , _lowercase )
self.assertIsInstance(processor.image_processor , _lowercase )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_feature_extractor()
lowercase__ = TvltProcessor(image_processor=_lowercase , feature_extractor=_lowercase )
lowercase__ = np.ones([1_20_00] )
lowercase__ = feature_extractor(_lowercase , return_tensors="np" )
lowercase__ = processor(audio=_lowercase , return_tensors="np" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_feature_extractor()
lowercase__ = TvltProcessor(image_processor=_lowercase , feature_extractor=_lowercase )
lowercase__ = np.ones([3, 2_24, 2_24] )
lowercase__ = image_processor(_lowercase , return_tensors="np" )
lowercase__ = processor(images=_lowercase , return_tensors="np" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_feature_extractor()
lowercase__ = TvltProcessor(image_processor=_lowercase , feature_extractor=_lowercase )
lowercase__ = np.ones([1_20_00] )
lowercase__ = np.ones([3, 2_24, 2_24] )
lowercase__ = processor(audio=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , ["audio_values", "audio_mask", "pixel_values", "pixel_mask"] )
# test if it raises when no input is passed
with pytest.raises(_lowercase ):
processor()
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_feature_extractor()
lowercase__ = TvltProcessor(image_processor=_lowercase , feature_extractor=_lowercase )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="`processor` and `image_processor`+`feature_extractor` model input names do not match" , )
| 611 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = CycleDiffusionPipeline
__lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'negative_prompt',
'height',
'width',
'negative_prompt_embeds',
}
__lowerCamelCase = PipelineTesterMixin.required_optional_params - {'latents'}
__lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'} )
__lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
lowercase__ = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , num_train_timesteps=10_00 , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowercase__ = CLIPTextModel(_lowercase )
lowercase__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase__ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase ( self :Optional[Any] , _lowercase :List[Any] , _lowercase :Union[str, Any]=0 ):
'''simple docstring'''
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowercase ) ).to(_lowercase )
lowercase__ = image / 2 + 0.5
if str(_lowercase ).startswith("mps" ):
lowercase__ = torch.manual_seed(_lowercase )
else:
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
lowercase__ = {
"prompt": "An astronaut riding an elephant",
"source_prompt": "An astronaut riding a horse",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"eta": 0.1,
"strength": 0.8,
"guidance_scale": 3,
"source_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase__ = self.get_dummy_components()
lowercase__ = CycleDiffusionPipeline(**_lowercase )
lowercase__ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )
lowercase__ = output.images
lowercase__ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase__ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
for name, module in components.items():
if hasattr(_lowercase , "half" ):
lowercase__ = module.half()
lowercase__ = CycleDiffusionPipeline(**_lowercase )
lowercase__ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )
lowercase__ = output.images
lowercase__ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
lowercase__ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return super().test_save_load_local()
@unittest.skip("non-deterministic pipeline" )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return super().test_inference_batch_single_identical()
@skip_mps
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png" )
lowercase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy" )
lowercase__ = init_image.resize((5_12, 5_12) )
lowercase__ = "CompVis/stable-diffusion-v1-4"
lowercase__ = DDIMScheduler.from_pretrained(_lowercase , subfolder="scheduler" )
lowercase__ = CycleDiffusionPipeline.from_pretrained(
_lowercase , scheduler=_lowercase , safety_checker=_lowercase , torch_dtype=torch.floataa , revision="fp16" )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
lowercase__ = "A black colored car"
lowercase__ = "A blue colored car"
lowercase__ = torch.manual_seed(0 )
lowercase__ = pipe(
prompt=_lowercase , source_prompt=_lowercase , image=_lowercase , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=_lowercase , output_type="np" , )
lowercase__ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png" )
lowercase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy" )
lowercase__ = init_image.resize((5_12, 5_12) )
lowercase__ = "CompVis/stable-diffusion-v1-4"
lowercase__ = DDIMScheduler.from_pretrained(_lowercase , subfolder="scheduler" )
lowercase__ = CycleDiffusionPipeline.from_pretrained(_lowercase , scheduler=_lowercase , safety_checker=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
lowercase__ = "A black colored car"
lowercase__ = "A blue colored car"
lowercase__ = torch.manual_seed(0 )
lowercase__ = pipe(
prompt=_lowercase , source_prompt=_lowercase , image=_lowercase , num_inference_steps=1_00 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=_lowercase , output_type="np" , )
lowercase__ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 611 | 1 |
from collections import deque
def UpperCamelCase ( _a ) -> Optional[int]:
'''simple docstring'''
lowercase_ :Tuple = len(_a )
lowercase_ :Dict = deque()
lowercase_ :Optional[Any] = [False for _ in range(_a )]
lowercase_ :Any = [-1 for _ in range(_a )]
lowercase_ :int = index_of[:]
def strong_connect(_a , _a , _a ):
lowercase_ :List[str] = index # the number when this node is seen
lowercase_ :Tuple = index # lowest rank node reachable from here
index += 1
stack.append(_a )
lowercase_ :Optional[Any] = True
for w in g[v]:
if index_of[w] == -1:
lowercase_ :List[str] = strong_connect(_a , _a , _a )
lowercase_ :Optional[Any] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
lowercase_ :str = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
lowercase_ :Dict = []
lowercase_ :List[str] = stack.pop()
lowercase_ :Optional[Any] = False
component.append(_a )
while w != v:
lowercase_ :str = stack.pop()
lowercase_ :List[str] = False
component.append(_a )
components.append(_a )
return index
lowercase_ :Any = []
for v in range(_a ):
if index_of[v] == -1:
strong_connect(_a , 0 , _a )
return components
def UpperCamelCase ( _a , _a ) -> Any:
'''simple docstring'''
lowercase_ :Optional[int] = [[] for _ in range(_a )]
for u, v in edges:
g[u].append(_a )
return g
if __name__ == "__main__":
# Test
SCREAMING_SNAKE_CASE : Optional[int] = 7
SCREAMING_SNAKE_CASE : List[Any] = [0, 0, 1, 2, 3, 3, 4, 4, 6]
SCREAMING_SNAKE_CASE : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
SCREAMING_SNAKE_CASE : int = [(u, v) for u, v in zip(source, target)]
SCREAMING_SNAKE_CASE : Any = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 257 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
SCREAMING_SNAKE_CASE : str = {
"facebook/mbart-large-en-ro": 1_024,
"facebook/mbart-large-cc25": 1_024,
}
# fmt: off
SCREAMING_SNAKE_CASE : Optional[Any] = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
lowercase : List[str] =VOCAB_FILES_NAMES
lowercase : Any =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Optional[int] =PRETRAINED_VOCAB_FILES_MAP
lowercase : Union[str, Any] =["""input_ids""", """attention_mask"""]
lowercase : Optional[int] =MBartTokenizer
lowercase : List[int] =[]
lowercase : List[int] =[]
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="<s>" , UpperCamelCase_="</s>" , UpperCamelCase_="</s>" , UpperCamelCase_="<s>" , UpperCamelCase_="<unk>" , UpperCamelCase_="<pad>" , UpperCamelCase_="<mask>" , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , **UpperCamelCase_ , ):
# Mask token behave like a normal word, i.e. include the space before it
lowercase_ :Optional[int] = AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else mask_token
super().__init__(
vocab_file=UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , bos_token=UpperCamelCase_ , eos_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , unk_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , src_lang=UpperCamelCase_ , tgt_lang=UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , **UpperCamelCase_ , )
lowercase_ :Optional[int] = vocab_file
lowercase_ :Any = False if not self.vocab_file else True
lowercase_ :int = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
lowercase_ :Optional[int] = {
lang_code: self.convert_tokens_to_ids(UpperCamelCase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowercase_ :Dict = src_lang if src_lang is not None else '''en_XX'''
lowercase_ :Any = self.convert_tokens_to_ids(self._src_lang )
lowercase_ :Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase ( self ):
return self._src_lang
@src_lang.setter
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
lowercase_ :Optional[Any] = [self.sep_token_id]
lowercase_ :Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ ):
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowercase_ :str = src_lang
lowercase_ :List[Any] = self(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_tensors=UpperCamelCase_ , **UpperCamelCase_ )
lowercase_ :Union[str, Any] = self.convert_tokens_to_ids(UpperCamelCase_ )
lowercase_ :Any = tgt_lang_id
return inputs
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = "en_XX" , UpperCamelCase_ = None , UpperCamelCase_ = "ro_RO" , **UpperCamelCase_ , ):
lowercase_ :List[str] = src_lang
lowercase_ :Any = tgt_lang
return super().prepare_seqaseq_batch(UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ )
def UpperCamelCase ( self ):
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase ( self ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :Union[str, Any] = self.convert_tokens_to_ids(UpperCamelCase_ )
lowercase_ :Tuple = []
lowercase_ :Tuple = [self.eos_token_id, self.cur_lang_code]
lowercase_ :Union[str, Any] = self.convert_ids_to_tokens(self.prefix_tokens )
lowercase_ :int = self.convert_ids_to_tokens(self.suffix_tokens )
lowercase_ :Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , UpperCamelCase_ ):
lowercase_ :List[str] = self.convert_tokens_to_ids(UpperCamelCase_ )
lowercase_ :Union[str, Any] = []
lowercase_ :Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
lowercase_ :Dict = self.convert_ids_to_tokens(self.prefix_tokens )
lowercase_ :List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowercase_ :int = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(UpperCamelCase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory." )
return
lowercase_ :Dict = os.path.join(
UpperCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
copyfile(self.vocab_file , UpperCamelCase_ )
return (out_vocab_file,)
| 257 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCamelCase__ ( _A , _A , _A , _A=None , _A=None , _A=None , _A=None , _A=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
snake_case_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
snake_case_ = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_A )
if decoder_head_mask is None:
snake_case_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_A )
if cross_attn_head_mask is None:
snake_case_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_A )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Dict , __lowercase : str , __lowercase : Optional[int]=13 , __lowercase : Any=7 , __lowercase : Any=True , __lowercase : Any=False , __lowercase : int=99 , __lowercase : int=16 , __lowercase : int=2 , __lowercase : str=4 , __lowercase : List[Any]=4 , __lowercase : Any="relu" , __lowercase : Tuple=0.1 , __lowercase : Optional[Any]=0.1 , __lowercase : Any=0.0 , __lowercase : int=0.0 , __lowercase : Union[str, Any]=20 , __lowercase : str=2 , __lowercase : List[Any]=1 , __lowercase : Union[str, Any]=0 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = max_position_embeddings
snake_case_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = self.eos_token_id # Eos Token
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
snake_case_ = input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
snake_case_ = self.get_config()
snake_case_ = prepare_mam_aaa_inputs_dict(__lowercase , __lowercase , __lowercase )
return config, inputs_dict
def snake_case__ ( self : int ):
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def snake_case__ ( self : Any ):
"""simple docstring"""
snake_case_ , snake_case_ = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case__ ( self : Any , __lowercase : List[Any] , __lowercase : int ):
"""simple docstring"""
snake_case_ = MaMaaaModel(config=__lowercase ).get_decoder().to(__lowercase ).eval()
snake_case_ = inputs_dict["input_ids"]
snake_case_ = inputs_dict["attention_mask"]
snake_case_ = inputs_dict["head_mask"]
# first forward pass
snake_case_ = model(__lowercase , attention_mask=__lowercase , head_mask=__lowercase , use_cache=__lowercase )
snake_case_ , snake_case_ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
snake_case_ = model(__lowercase , attention_mask=__lowercase )["last_hidden_state"]
snake_case_ = model(__lowercase , attention_mask=__lowercase , past_key_values=__lowercase )[
"last_hidden_state"
]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1E-2 ) )
def snake_case__ ( self : List[Any] , __lowercase : Tuple , __lowercase : Tuple ):
"""simple docstring"""
snake_case_ = MaMaaaModel(config=__lowercase ).to(__lowercase ).eval()
snake_case_ = model(**__lowercase )
snake_case_ = outputs.encoder_last_hidden_state
snake_case_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = model.get_encoder()
encoder.save_pretrained(__lowercase )
snake_case_ = MaMaaaEncoder.from_pretrained(__lowercase ).to(__lowercase )
snake_case_ = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = model.get_decoder()
decoder.save_pretrained(__lowercase )
snake_case_ = MaMaaaDecoder.from_pretrained(__lowercase ).to(__lowercase )
snake_case_ = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=__lowercase , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowerCAmelCase_ = (
{
'''conversational''': MaMaaaForConditionalGeneration,
'''feature-extraction''': MaMaaaModel,
'''summarization''': MaMaaaForConditionalGeneration,
'''text2text-generation''': MaMaaaForConditionalGeneration,
'''translation''': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowerCAmelCase_ = True
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def snake_case__ ( self : Tuple , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : int , __lowercase : str , __lowercase : Any ):
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
snake_case_ = MaMaaaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__lowercase )
def snake_case__ ( self : int ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ = model_class(__lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowercase )
snake_case_ , snake_case_ = model_class.from_pretrained(__lowercase , output_loading_info=__lowercase )
self.assertEqual(info["missing_keys"] , [] )
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__lowercase )
def snake_case__ ( self : List[str] ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
snake_case_ = model_class(__lowercase )
model.to(__lowercase )
model.eval()
snake_case_ = copy.deepcopy(self._prepare_for_class(__lowercase , __lowercase ) )
if not self.is_encoder_decoder:
snake_case_ = inputs["input_ids"]
del inputs["input_ids"]
else:
snake_case_ = inputs["input_ids"]
snake_case_ = inputs.get("decoder_input_ids" , __lowercase )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , __lowercase )
snake_case_ = model.get_input_embeddings()
if not self.is_encoder_decoder:
snake_case_ = wte(__lowercase )
else:
snake_case_ = wte(__lowercase )
snake_case_ = wte(__lowercase )
with torch.no_grad():
model(**__lowercase )[0]
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
snake_case_ = input_dict["input_ids"]
snake_case_ = input_ids.ne(1 ).to(__lowercase )
snake_case_ = MaMaaaForConditionalGeneration(__lowercase ).eval().to(__lowercase )
if torch_device == "cuda":
model.half()
model.generate(__lowercase , attention_mask=__lowercase )
model.generate(num_beams=4 , do_sample=__lowercase , early_stopping=__lowercase , num_return_sequences=3 )
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return torch.tensor(_A , dtype=torch.long , device=_A )
lowercase__ : Optional[Any] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
snake_case_ = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(__lowercase )
snake_case_ = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
snake_case_ = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
snake_case_ = prepare_mam_aaa_inputs_dict(model.config , __lowercase , __lowercase )
with torch.no_grad():
snake_case_ = model(**__lowercase )[0]
snake_case_ = torch.Size((1, 11, 10_24) )
self.assertEqual(output.shape , __lowercase )
# change to expected output here
snake_case_ = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=__lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowercase , atol=__lowercase ) )
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
snake_case_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__lowercase )
# change to intended input
snake_case_ = _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
snake_case_ = _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
snake_case_ = prepare_mam_aaa_inputs_dict(model.config , __lowercase , __lowercase )
with torch.no_grad():
snake_case_ = model(**__lowercase )[0]
snake_case_ = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , __lowercase )
# change to expected output here
snake_case_ = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=__lowercase )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowercase , atol=__lowercase ) )
def snake_case__ ( self : Any ):
"""simple docstring"""
snake_case_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__lowercase )
snake_case_ = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
snake_case_ = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
snake_case_ = tokenizer(__lowercase , padding=__lowercase , return_tensors="pt" )
snake_case_ = model.generate(
input_ids=dct["input_ids"].to(__lowercase ) , attention_mask=dct["attention_mask"].to(__lowercase ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
snake_case_ = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
snake_case_ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=__lowercase , skip_special_tokens=__lowercase )
assert generated == expected_en
| 139 |
lowercase__ : Optional[int] = range(2, 20 + 1)
lowercase__ : List[str] = [10**k for k in range(ks[-1] + 1)]
lowercase__ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
snake_case_ = sum(a_i[j] for j in range(_A , len(_A ) ) )
snake_case_ = sum(a_i[j] * base[j] for j in range(min(len(_A ) , _A ) ) )
snake_case_ , snake_case_ = 0, 0
snake_case_ = n - i
snake_case_ = memo.get(_A )
if sub_memo is not None:
snake_case_ = sub_memo.get(_A )
if jumps is not None and len(_A ) > 0:
# find and make the largest jump without going over
snake_case_ = -1
for _k in range(len(_A ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
snake_case_ = _k
break
if max_jump >= 0:
snake_case_ , snake_case_ , snake_case_ = jumps[max_jump]
# since the difference between jumps is cached, add c
snake_case_ = diff + c
for j in range(min(_A , len(_A ) ) ):
snake_case_ , snake_case_ = divmod(_A , 10 )
if new_c > 0:
add(_A , _A , _A )
else:
snake_case_ = []
else:
snake_case_ = {c: []}
snake_case_ = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
snake_case_ , snake_case_ = next_term(_A , k - 1 , i + dn , _A )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
snake_case_ , snake_case_ = compute(_A , _A , i + dn , _A )
diff += _diff
dn += terms_jumped
snake_case_ = sub_memo[c]
# keep jumps sorted by # of terms skipped
snake_case_ = 0
while j < len(_A ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_A , (diff, dn, k) )
return (diff, dn)
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring'''
if i >= n:
return 0, i
if k > len(_A ):
a_i.extend([0 for _ in range(k - len(_A ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
snake_case_ = i
snake_case_ , snake_case_ , snake_case_ = 0, 0, 0
for j in range(len(_A ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
snake_case_ = ds_c + ds_b
diff += addend
snake_case_ = 0
for j in range(_A ):
snake_case_ = a_i[j] + addend
snake_case_ , snake_case_ = divmod(_A , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_A , _A , _A )
return diff, i - start_i
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
for j in range(_A , len(_A ) ):
snake_case_ = digits[j] + addend
if s >= 10:
snake_case_ , snake_case_ = divmod(_A , 10 )
snake_case_ = addend // 10 + quotient
else:
snake_case_ = s
snake_case_ = addend // 10
if addend == 0:
break
while addend > 0:
snake_case_ , snake_case_ = divmod(_A , 10 )
digits.append(_A )
def lowerCamelCase__ ( _A = 10**15 ):
'''simple docstring'''
snake_case_ = [1]
snake_case_ = 1
snake_case_ = 0
while True:
snake_case_ , snake_case_ = next_term(_A , 20 , i + dn , _A )
dn += terms_jumped
if dn == n - i:
break
snake_case_ = 0
for j in range(len(_A ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f'''{solution() = }''')
| 139 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
_UpperCamelCase = field(default="""language-modeling""" ,metadata={"""include_in_asdict_even_if_is_default""": True} )
_UpperCamelCase = Features({"""text""": Value("""string""" )} )
_UpperCamelCase = Features({} )
_UpperCamelCase = "text"
@property
def __snake_case ( self : str) -> List[str]:
return {self.text_column: "text"}
| 366 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ (lowercase__ : int , lowercase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_ (lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCAmelCase__ = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ , keep_in_memory=lowercase__ ).read()
_check_parquet_dataset(lowercase__ , lowercase__ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_ (lowercase__ : Any , lowercase__ : Union[str, Any] , lowercase__ : Optional[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase__ = features.copy() if features else default_expected_features
lowerCAmelCase__ = (
Features({feature: Value(lowercase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase__ = ParquetDatasetReader(lowercase__ , features=lowercase__ , cache_dir=lowercase__ ).read()
_check_parquet_dataset(lowercase__ , lowercase__ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_ (lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase__ = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ , split=lowercase__ ).read()
_check_parquet_dataset(lowercase__ , lowercase__ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_ (lowercase__ : List[str] , lowercase__ : Union[str, Any] , lowercase__ : str ) -> Any:
'''simple docstring'''
if issubclass(lowercase__ , lowercase__ ):
lowerCAmelCase__ = parquet_path
elif issubclass(lowercase__ , lowercase__ ):
lowerCAmelCase__ = [parquet_path]
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase__ = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ ).read()
_check_parquet_dataset(lowercase__ , lowercase__ )
def lowerCAmelCase_ (lowercase__ : List[str] , lowercase__ : str , lowercase__ : Optional[Any]=("train",) ) -> Union[str, Any]:
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ )
for split in splits:
lowerCAmelCase__ = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_ (lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCAmelCase__ = ParquetDatasetReader(
{'''train''': parquet_path} , cache_dir=lowercase__ , keep_in_memory=lowercase__ ).read()
_check_parquet_datasetdict(lowercase__ , lowercase__ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_ (lowercase__ : int , lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase__ = features.copy() if features else default_expected_features
lowerCAmelCase__ = (
Features({feature: Value(lowercase__ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase__ = ParquetDatasetReader({'''train''': parquet_path} , features=lowercase__ , cache_dir=lowercase__ ).read()
_check_parquet_datasetdict(lowercase__ , lowercase__ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_ (lowercase__ : str , lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any] ) -> int:
'''simple docstring'''
if split:
lowerCAmelCase__ = {split: parquet_path}
else:
lowerCAmelCase__ = '''train'''
lowerCAmelCase__ = {'''train''': parquet_path, '''test''': parquet_path}
lowerCAmelCase__ = tmp_path / '''cache'''
lowerCAmelCase__ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
lowerCAmelCase__ = ParquetDatasetReader(lowercase__ , cache_dir=lowercase__ ).read()
_check_parquet_datasetdict(lowercase__ , lowercase__ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ (lowercase__ : Optional[int] , lowercase__ : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = ParquetDatasetWriter(lowercase__ , tmp_path / '''foo.parquet''' )
assert writer.write() > 0
lowerCAmelCase__ = pq.ParquetFile(tmp_path / '''foo.parquet''' )
lowerCAmelCase__ = pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ (lowercase__ : Dict , lowercase__ : List[str] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = str(shared_datadir / '''test_image_rgb.jpg''' )
lowerCAmelCase__ = {'''image''': [image_path]}
lowerCAmelCase__ = Features({'''image''': Image()} )
lowerCAmelCase__ = Dataset.from_dict(lowercase__ , features=lowercase__ )
lowerCAmelCase__ = ParquetDatasetWriter(lowercase__ , tmp_path / '''foo.parquet''' )
assert writer.write() > 0
lowerCAmelCase__ = Dataset.from_parquet(str(tmp_path / '''foo.parquet''' ) )
assert dataset.features == reloaded_dataset.features
lowerCAmelCase__ = ParquetDatasetReader(str(tmp_path / '''foo.parquet''' ) , streaming=lowercase__ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
'''feature, expected''' , [
(Features({'''foo''': Value('''int32''' )} ), None),
(Features({'''image''': Image(), '''foo''': Value('''int32''' )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({'''nested''': Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ (lowercase__ : Optional[int] , lowercase__ : str ) -> Tuple:
'''simple docstring'''
assert get_writer_batch_size(lowercase__ ) == expected
| 668 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
return (data["data"], data["target"])
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = XGBClassifier()
classifier.fit(_lowercase , _lowercase )
return classifier
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = load_iris()
UpperCAmelCase_, UpperCAmelCase_ : Any = data_handling(_lowercase )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = train_test_split(
_lowercase , _lowercase , test_size=0.25 )
UpperCAmelCase_ : Dict = iris['''target_names''']
# Create an XGBoost Classifier from the training data
UpperCAmelCase_ : int = xgboost(_lowercase , _lowercase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_lowercase , _lowercase , _lowercase , display_labels=_lowercase , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main() | 300 |
from __future__ import annotations
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_, UpperCAmelCase_ : List[str] = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
UpperCAmelCase_ : Optional[Any] = result + left + right
return input_list
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if len(_lowercase ) <= 1:
return input_list
UpperCAmelCase_ : List[Any] = list(_lowercase )
# iteration for two-way merging
UpperCAmelCase_ : Dict = 2
while p <= len(_lowercase ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(_lowercase ) , _lowercase ):
UpperCAmelCase_ : Union[str, Any] = i
UpperCAmelCase_ : Union[str, Any] = i + p - 1
UpperCAmelCase_ : Optional[int] = (low + high + 1) // 2
UpperCAmelCase_ : Tuple = merge(_lowercase , _lowercase , _lowercase , _lowercase )
# final merge of last two parts
if p * 2 >= len(_lowercase ):
UpperCAmelCase_ : List[Any] = i
UpperCAmelCase_ : Tuple = merge(_lowercase , 0 , _lowercase , len(_lowercase ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
__a = []
else:
__a = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted)) | 300 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( lowercase_ , lowercase_ , lowercase_ ) -> Any:
def get_masked_lm_array(lowercase_ ):
_snake_case : Optional[int] = f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case : str = tf.train.load_variable(lowercase_ , lowercase_ )
if "kernel" in name:
_snake_case : Any = array.transpose()
return torch.from_numpy(lowercase_ )
def get_encoder_array(lowercase_ ):
_snake_case : List[Any] = f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case : Optional[int] = tf.train.load_variable(lowercase_ , lowercase_ )
if "kernel" in name:
_snake_case : List[Any] = array.transpose()
return torch.from_numpy(lowercase_ )
def get_encoder_layer_array(lowercase_ , lowercase_ ):
_snake_case : Optional[Any] = f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case : List[str] = tf.train.load_variable(lowercase_ , lowercase_ )
if "kernel" in name:
_snake_case : List[str] = array.transpose()
return torch.from_numpy(lowercase_ )
def get_encoder_attention_layer_array(lowercase_ , lowercase_ , lowercase_ ):
_snake_case : int = f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
_snake_case : Tuple = tf.train.load_variable(lowercase_ , lowercase_ )
_snake_case : Dict = array.reshape(lowercase_ )
if "kernel" in name:
_snake_case : Optional[Any] = array.transpose()
return torch.from_numpy(lowercase_ )
print(f'''Loading model based on config from {config_path}...''' )
_snake_case : List[Any] = BertConfig.from_json_file(lowercase_ )
_snake_case : Union[str, Any] = BertForMaskedLM(lowercase_ )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
_snake_case : BertLayer = model.bert.encoder.layer[layer_index]
# Self-attention
_snake_case : BertSelfAttention = layer.attention.self
_snake_case : str = get_encoder_attention_layer_array(
lowercase_ , '''_query_dense/kernel''' , self_attn.query.weight.data.shape )
_snake_case : str = get_encoder_attention_layer_array(
lowercase_ , '''_query_dense/bias''' , self_attn.query.bias.data.shape )
_snake_case : Any = get_encoder_attention_layer_array(
lowercase_ , '''_key_dense/kernel''' , self_attn.key.weight.data.shape )
_snake_case : Optional[Any] = get_encoder_attention_layer_array(
lowercase_ , '''_key_dense/bias''' , self_attn.key.bias.data.shape )
_snake_case : int = get_encoder_attention_layer_array(
lowercase_ , '''_value_dense/kernel''' , self_attn.value.weight.data.shape )
_snake_case : List[Any] = get_encoder_attention_layer_array(
lowercase_ , '''_value_dense/bias''' , self_attn.value.bias.data.shape )
# Self-attention Output
_snake_case : BertSelfOutput = layer.attention.output
_snake_case : List[str] = get_encoder_attention_layer_array(
lowercase_ , '''_output_dense/kernel''' , self_output.dense.weight.data.shape )
_snake_case : Union[str, Any] = get_encoder_attention_layer_array(
lowercase_ , '''_output_dense/bias''' , self_output.dense.bias.data.shape )
_snake_case : Dict = get_encoder_layer_array(lowercase_ , '''_attention_layer_norm/gamma''' )
_snake_case : Union[str, Any] = get_encoder_layer_array(lowercase_ , '''_attention_layer_norm/beta''' )
# Intermediate
_snake_case : BertIntermediate = layer.intermediate
_snake_case : Any = get_encoder_layer_array(lowercase_ , '''_intermediate_dense/kernel''' )
_snake_case : Any = get_encoder_layer_array(lowercase_ , '''_intermediate_dense/bias''' )
# Output
_snake_case : BertOutput = layer.output
_snake_case : Tuple = get_encoder_layer_array(lowercase_ , '''_output_dense/kernel''' )
_snake_case : Union[str, Any] = get_encoder_layer_array(lowercase_ , '''_output_dense/bias''' )
_snake_case : Any = get_encoder_layer_array(lowercase_ , '''_output_layer_norm/gamma''' )
_snake_case : Tuple = get_encoder_layer_array(lowercase_ , '''_output_layer_norm/beta''' )
# Embeddings
_snake_case : str = get_encoder_array('''_position_embedding_layer/embeddings''' )
_snake_case : Tuple = get_encoder_array('''_type_embedding_layer/embeddings''' )
_snake_case : Optional[Any] = get_encoder_array('''_embedding_norm_layer/gamma''' )
_snake_case : Union[str, Any] = get_encoder_array('''_embedding_norm_layer/beta''' )
# LM Head
_snake_case : str = model.cls.predictions.transform
_snake_case : int = get_masked_lm_array('''dense/kernel''' )
_snake_case : Optional[Any] = get_masked_lm_array('''dense/bias''' )
_snake_case : Optional[Any] = get_masked_lm_array('''layer_norm/gamma''' )
_snake_case : int = get_masked_lm_array('''layer_norm/beta''' )
_snake_case : List[str] = get_masked_lm_array('''embedding_table''' )
# Pooling
_snake_case : int = BertPooler(config=lowercase_ )
_snake_case : BertPooler = get_encoder_array('''_pooler_layer/kernel''' )
_snake_case : BertPooler = get_encoder_array('''_pooler_layer/bias''' )
# Export final model
model.save_pretrained(lowercase_ )
# Integration test - should load without any errors ;)
_snake_case : Tuple = BertForMaskedLM.from_pretrained(lowercase_ )
print(new_model.eval() )
print('''Model conversion was done sucessfully!''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
)
parser.add_argument(
"--bert_config_file",
type=str,
required=True,
help="The config json file corresponding to the BERT model. This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path",
type=str,
required=True,
help="Path to the output PyTorch model.",
)
lowerCAmelCase_ = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 326 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"""The RoBERTa Model transformer with early exiting (DeeRoBERTa). """ ,__UpperCAmelCase ,)
class A (__UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = RobertaConfig
_SCREAMING_SNAKE_CASE = """roberta"""
def __init__( self , lowercase_ ) -> Any:
'''simple docstring'''
super().__init__(lowercase_ )
_snake_case : str = RobertaEmbeddings(lowercase_ )
self.init_weights()
@add_start_docstrings(
"""RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. """ ,__UpperCAmelCase ,)
class A (__UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = RobertaConfig
_SCREAMING_SNAKE_CASE = """roberta"""
def __init__( self , lowercase_ ) -> str:
'''simple docstring'''
super().__init__(lowercase_ )
_snake_case : List[str] = config.num_labels
_snake_case : List[str] = config.num_hidden_layers
_snake_case : Dict = DeeRobertaModel(lowercase_ )
_snake_case : Dict = nn.Dropout(config.hidden_dropout_prob )
_snake_case : Optional[Any] = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(lowercase_ )
def __a ( self , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=-1 , lowercase_=False , ) -> Optional[Any]:
'''simple docstring'''
_snake_case : Dict = self.num_layers
try:
_snake_case : Any = self.roberta(
lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , position_ids=lowercase_ , head_mask=lowercase_ , inputs_embeds=lowercase_ , )
_snake_case : List[Any] = outputs[1]
_snake_case : Optional[Any] = self.dropout(lowercase_ )
_snake_case : Any = self.classifier(lowercase_ )
_snake_case : Union[str, Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_snake_case : Dict = e.message
_snake_case : Any = e.exit_layer
_snake_case : Tuple = outputs[0]
if not self.training:
_snake_case : Tuple = entropy(lowercase_ )
_snake_case : str = []
_snake_case : List[Any] = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_snake_case : List[str] = MSELoss()
_snake_case : int = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_snake_case : Tuple = CrossEntropyLoss()
_snake_case : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_snake_case : int = []
for highway_exit in outputs[-1]:
_snake_case : List[str] = highway_exit[0]
if not self.training:
highway_logits_all.append(lowercase_ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_snake_case : Optional[Any] = MSELoss()
_snake_case : List[Any] = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_snake_case : Optional[Any] = CrossEntropyLoss()
_snake_case : str = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowercase_ )
if train_highway:
_snake_case : Tuple = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_snake_case : List[str] = (loss,) + outputs
if not self.training:
_snake_case : Tuple = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_snake_case : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 326 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar("""T""")
lowerCAmelCase_ = TypeVar("""U""")
class _lowerCAmelCase ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase : T | None , UpperCamelCase : U | None ):
'''simple docstring'''
_snake_case : Optional[Any] = key
_snake_case : List[Any] = val
_snake_case : DoubleLinkedListNode[T, U] | None = None
_snake_case : DoubleLinkedListNode[T, U] | None = None
def __repr__( self : Union[str, Any] ):
'''simple docstring'''
return (
f"""Node: key: {self.key}, val: {self.val}, """
f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class _lowerCAmelCase ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : str ):
'''simple docstring'''
_snake_case : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(UpperCamelCase , UpperCamelCase )
_snake_case : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(UpperCamelCase , UpperCamelCase )
_snake_case : List[str] = self.rear, self.head
def __repr__( self : Optional[int] ):
'''simple docstring'''
_snake_case : List[str] = ['DoubleLinkedList']
_snake_case : Dict = self.head
while node.next is not None:
rep.append(str(UpperCamelCase ) )
_snake_case : Dict = node.next
rep.append(str(self.rear ) )
return ",\n ".join(UpperCamelCase )
def UpperCamelCase_ ( self : Dict , UpperCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
_snake_case : str = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_snake_case : Tuple = node
_snake_case : int = previous
_snake_case : int = node
_snake_case : int = self.rear
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
_snake_case : str = node.next
_snake_case : List[str] = node.prev
_snake_case : Union[str, Any] = None
_snake_case : Optional[int] = None
return node
class _lowerCAmelCase ( Generic[T, U] ):
'''simple docstring'''
a_ : dict[Callable[[T], U], LRUCache[T, U]] ={}
def __init__( self : List[Any] , UpperCamelCase : int ):
'''simple docstring'''
_snake_case : DoubleLinkedList[T, U] = DoubleLinkedList()
_snake_case : Dict = capacity
_snake_case : Tuple = 0
_snake_case : Optional[Any] = 0
_snake_case : Optional[Any] = 0
_snake_case : dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__( self : Dict ):
'''simple docstring'''
return (
f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
f"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : List[str] , UpperCamelCase : T ):
'''simple docstring'''
return key in self.cache
def UpperCamelCase_ ( self : Any , UpperCamelCase : T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
_snake_case : DoubleLinkedListNode[T, U] = self.cache[key]
_snake_case : Dict = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(UpperCamelCase )
return node.val
self.miss += 1
return None
def UpperCamelCase_ ( self : int , UpperCamelCase : T , UpperCamelCase : U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_snake_case : Dict = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(UpperCamelCase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_snake_case : Any = DoubleLinkedListNode(UpperCamelCase , UpperCamelCase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_snake_case : Optional[Any] = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_snake_case : Optional[Any] = value
self.list.add(UpperCamelCase )
@classmethod
def UpperCamelCase_ ( cls : str , UpperCamelCase : int = 1_28 ):
'''simple docstring'''
def cache_decorator_inner(UpperCamelCase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*UpperCamelCase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
_snake_case : Union[str, Any] = LRUCache(UpperCamelCase )
_snake_case : Union[str, Any] = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_snake_case : Any = func(*UpperCamelCase )
cls.decorator_function_to_instance_map[func].put(args[0] , UpperCamelCase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(UpperCamelCase , 'cache_info' , UpperCamelCase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 |
from __future__ import annotations
from random import random
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase : int | None = None ):
'''simple docstring'''
_snake_case : str = value
_snake_case : List[Any] = random()
_snake_case : Node | None = None
_snake_case : Node | None = None
def __repr__( self : Optional[Any] ):
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return f"""'{self.value}: {self.prior:.5}'"""
else:
return pformat(
{f"""{self.value}: {self.prior:.5}""": (self.left, self.right)} , indent=1 )
def __str__( self : Dict ):
'''simple docstring'''
_snake_case : List[str] = str(self.value ) + ' '
_snake_case : List[Any] = str(self.left or '' )
_snake_case : int = str(self.right or '' )
return value + left + right
def lowerCamelCase_ ( lowerCAmelCase: Node | None , lowerCAmelCase: int )-> tuple[Node | None, Node | None]:
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
_snake_case , _snake_case : Optional[Any] = split(root.left , lowerCAmelCase )
return left, root
else:
_snake_case , _snake_case : List[str] = split(root.right , lowerCAmelCase )
return root, right
def lowerCamelCase_ ( lowerCAmelCase: Node | None , lowerCAmelCase: Node | None )-> Node | None:
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
_snake_case : str = merge(left.right , lowerCAmelCase )
return left
else:
_snake_case : Union[str, Any] = merge(lowerCAmelCase , right.left )
return right
def lowerCamelCase_ ( lowerCAmelCase: Node | None , lowerCAmelCase: int )-> Node | None:
_snake_case : Tuple = Node(lowerCAmelCase )
_snake_case , _snake_case : Optional[int] = split(lowerCAmelCase , lowerCAmelCase )
return merge(merge(lowerCAmelCase , lowerCAmelCase ) , lowerCAmelCase )
def lowerCamelCase_ ( lowerCAmelCase: Node | None , lowerCAmelCase: int )-> Node | None:
_snake_case , _snake_case : Optional[int] = split(lowerCAmelCase , value - 1 )
_snake_case , _snake_case : List[str] = split(lowerCAmelCase , lowerCAmelCase )
return merge(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase_ ( lowerCAmelCase: Node | None )-> None:
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=',' )
inorder(root.right )
def lowerCamelCase_ ( lowerCAmelCase: Node | None , lowerCAmelCase: str )-> Node | None:
for arg in args.split():
if arg[0] == "+":
_snake_case : List[str] = insert(lowerCAmelCase , int(arg[1:] ) )
elif arg[0] == "-":
_snake_case : Any = erase(lowerCAmelCase , int(arg[1:] ) )
else:
print('Unknown command' )
return root
def lowerCamelCase_ ( )-> None:
_snake_case : Tuple = None
print(
'enter numbers to create a tree, + value to add value into treap, '
'- value to erase all nodes with value. \'q\' to quit. ' )
_snake_case : List[Any] = input()
while args != "q":
_snake_case : int = interact_treap(lowerCAmelCase , lowerCAmelCase )
print(lowerCAmelCase )
_snake_case : Tuple = input()
print('good by!' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 669 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class A ( lowerCamelCase__ ):
def __init__( self: List[str] , _lowerCAmelCase: Union[str, "sqlalchemy.sql.Selectable"] , _lowerCAmelCase: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , _lowerCAmelCase: Optional[Features] = None , _lowerCAmelCase: str = None , _lowerCAmelCase: bool = False , **_lowerCAmelCase: Optional[int] , ) -> str:
'''simple docstring'''
super().__init__(features=lowerCAmelCase__ , cache_dir=lowerCAmelCase__ , keep_in_memory=lowerCAmelCase__ , **lowerCAmelCase__ )
UpperCAmelCase_ =Sql(
cache_dir=lowerCAmelCase__ , features=lowerCAmelCase__ , sql=lowerCAmelCase__ , con=lowerCAmelCase__ , **lowerCAmelCase__ , )
def lowerCAmelCase__ ( self: Union[str, Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ =None
UpperCAmelCase_ =None
UpperCAmelCase_ =None
UpperCAmelCase_ =None
self.builder.download_and_prepare(
download_config=lowerCAmelCase__ , download_mode=lowerCAmelCase__ , verification_mode=lowerCAmelCase__ , base_path=lowerCAmelCase__ , )
# Build dataset for splits
UpperCAmelCase_ =self.builder.as_dataset(
split="train" , verification_mode=lowerCAmelCase__ , in_memory=self.keep_in_memory )
return dataset
class A :
def __init__( self: Tuple , _lowerCAmelCase: Dataset , _lowerCAmelCase: str , _lowerCAmelCase: Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , _lowerCAmelCase: Optional[int] = None , _lowerCAmelCase: Optional[int] = None , **_lowerCAmelCase: List[str] , ) -> Any:
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(F'num_proc {num_proc} must be an integer > 0.' )
UpperCAmelCase_ =dataset
UpperCAmelCase_ =name
UpperCAmelCase_ =con
UpperCAmelCase_ =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
UpperCAmelCase_ =num_proc
UpperCAmelCase_ =to_sql_kwargs
def lowerCAmelCase__ ( self: List[str] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =self.to_sql_kwargs.pop("sql" , lowerCAmelCase__ )
UpperCAmelCase_ =self.to_sql_kwargs.pop("con" , lowerCAmelCase__ )
UpperCAmelCase_ =self.to_sql_kwargs.pop("index" , lowerCAmelCase__ )
UpperCAmelCase_ =self._write(index=lowerCAmelCase__ , **self.to_sql_kwargs )
return written
def lowerCAmelCase__ ( self: Optional[int] , _lowerCAmelCase: List[Any] ) -> str:
'''simple docstring'''
UpperCAmelCase_ =args
UpperCAmelCase_ ={**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
UpperCAmelCase_ =query_table(
table=self.dataset.data , key=slice(lowerCAmelCase__ , offset + self.batch_size ) , indices=self.dataset._indices , )
UpperCAmelCase_ =batch.to_pandas()
UpperCAmelCase_ =df.to_sql(self.name , self.con , index=lowerCAmelCase__ , **lowerCAmelCase__ )
return num_rows or len(lowerCAmelCase__ )
def lowerCAmelCase__ ( self: Any , _lowerCAmelCase: str , **_lowerCAmelCase: Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ =0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
UpperCAmelCase_ =len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , lowerCAmelCase__ , lowerCAmelCase__ )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 54 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if len(_lowerCamelCase ) < 2:
return collection
def circle_sort_util(_lowerCamelCase: list , _lowerCamelCase: int , _lowerCamelCase: int ) -> bool:
__SCREAMING_SNAKE_CASE : Any = False
if low == high:
return swapped
__SCREAMING_SNAKE_CASE : Any = low
__SCREAMING_SNAKE_CASE : Dict = high
while left < right:
if collection[left] > collection[right]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = (
collection[right],
collection[left],
)
__SCREAMING_SNAKE_CASE : str = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = (
collection[right + 1],
collection[left],
)
__SCREAMING_SNAKE_CASE : str = True
__SCREAMING_SNAKE_CASE : Union[str, Any] = low + int((high - low) / 2 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = circle_sort_util(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = circle_sort_util(_lowerCamelCase , mid + 1 , _lowerCamelCase )
return swapped or left_swap or right_swap
__SCREAMING_SNAKE_CASE : Optional[Any] = True
while is_not_sorted is True:
__SCREAMING_SNAKE_CASE : Tuple = circle_sort_util(_lowerCamelCase , 0 , len(_lowerCamelCase ) - 1 )
return collection
if __name__ == "__main__":
UpperCamelCase__ : Optional[int] = input('''Enter numbers separated by a comma:\n''').strip()
UpperCamelCase__ : List[Any] = [int(item) for item in user_input.split(''',''')]
print(circle_sort(unsorted)) | 578 | 0 |
'''simple docstring'''
import os
import sys
a_ : Union[str, Any] = os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
a_ : Any = [
"""torch""",
"""numpy""",
"""tokenizers""",
"""filelock""",
"""requests""",
"""tqdm""",
"""regex""",
"""sentencepiece""",
"""sacremoses""",
"""importlib_metadata""",
"""huggingface_hub""",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __snake_case ( *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : List[Any] ):
return AutoConfig.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __snake_case ( *UpperCAmelCase_ : int , **UpperCAmelCase_ : Dict ):
return AutoTokenizer.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoModel.__doc__ )
def __snake_case ( *UpperCAmelCase_ : int , **UpperCAmelCase_ : Tuple ):
return AutoModel.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __snake_case ( *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Optional[int] ):
return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __snake_case ( *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Dict ):
return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __snake_case ( *UpperCAmelCase_ : Any , **UpperCAmelCase_ : int ):
return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __snake_case ( *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Optional[Any] ):
return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase_ , **UpperCAmelCase_ )
| 445 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def __snake_case ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ):
lowerCamelCase_ = [0] * no_of_processes
lowerCamelCase_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(UpperCAmelCase_ ):
lowerCamelCase_ = burst_time[i]
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 999999999
lowerCamelCase_ = 0
lowerCamelCase_ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(UpperCAmelCase_ ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
lowerCamelCase_ = remaining_time[j]
lowerCamelCase_ = j
lowerCamelCase_ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
lowerCamelCase_ = remaining_time[short]
if minm == 0:
lowerCamelCase_ = 999999999
if remaining_time[short] == 0:
complete += 1
lowerCamelCase_ = False
# Find finish time of current process
lowerCamelCase_ = increment_time + 1
# Calculate waiting time
lowerCamelCase_ = finish_time - arrival_time[short]
lowerCamelCase_ = finar - burst_time[short]
if waiting_time[short] < 0:
lowerCamelCase_ = 0
# Increment time
increment_time += 1
return waiting_time
def __snake_case ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : list[int] ):
lowerCamelCase_ = [0] * no_of_processes
for i in range(UpperCAmelCase_ ):
lowerCamelCase_ = burst_time[i] + waiting_time[i]
return turn_around_time
def __snake_case ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ):
lowerCamelCase_ = 0
lowerCamelCase_ = 0
for i in range(UpperCAmelCase_ ):
lowerCamelCase_ = total_waiting_time + waiting_time[i]
lowerCamelCase_ = total_turn_around_time + turn_around_time[i]
print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("Average turn around time =" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("""Enter how many process you want to analyze""")
a_ : Dict = int(input())
a_ : Any = [0] * no_of_processes
a_ : Optional[int] = [0] * no_of_processes
a_ : Tuple = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
a_ , a_ : str = map(int, input().split())
a_ : List[Any] = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
a_ : int = burst_time
a_ : Union[str, Any] = no_of_processes
a_ : Optional[int] = waiting_time
a_ : Any = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
a_ : Optional[int] = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"""Process""",
"""BurstTime""",
"""ArrivalTime""",
"""WaitingTime""",
"""TurnAroundTime""",
],
)
# Printing the dataFrame
pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
print(fcfs)
| 445 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(__UpperCAmelCase )
return n == n[::-1]
def __magic_name__ ( __UpperCAmelCase = 1000000 ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , __UpperCAmelCase ):
if is_palindrome(__UpperCAmelCase ) and is_palindrome(bin(__UpperCAmelCase ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 109 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : List[Any] = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__ )] )
_UpperCAmelCase : Tuple = np.array(UpperCamelCase__ )
_UpperCAmelCase : Any = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , UpperCamelCase__ ) ) , x.transpose() ) , UpperCamelCase__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : Tuple = (1, 2, 1)
_UpperCAmelCase : Tuple = (1, 1, 0, 7)
_UpperCAmelCase : Tuple = SARIMAX(
UpperCamelCase__ , exog=UpperCamelCase__ , order=UpperCamelCase__ , seasonal_order=UpperCamelCase__ )
_UpperCAmelCase : Any = model.fit(disp=UpperCamelCase__ , maxiter=600 , method='''nm''' )
_UpperCAmelCase : int = model_fit.predict(1 , len(UpperCamelCase__ ) , exog=[test_match] )
return result[0]
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : str = SVR(kernel='''rbf''' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(UpperCamelCase__ , UpperCamelCase__ )
_UpperCAmelCase : Dict = regressor.predict(UpperCamelCase__ )
return y_pred[0]
def lowerCamelCase_ (UpperCamelCase__ : list ):
train_user.sort()
_UpperCAmelCase : Union[str, Any] = np.percentile(UpperCamelCase__ , 25 )
_UpperCAmelCase : Optional[int] = np.percentile(UpperCamelCase__ , 75 )
_UpperCAmelCase : Dict = qa - qa
_UpperCAmelCase : List[str] = qa - (iqr * 0.1)
return low_lim
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : float ):
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Dict = 0
for i in list_vote:
if i > actual_result:
_UpperCAmelCase : str = not_safe + 1
else:
if abs(abs(UpperCamelCase__ ) - abs(UpperCamelCase__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
_lowerCAmelCase :Any = [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
_lowerCAmelCase :str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
_lowerCAmelCase :Dict = Normalizer().fit_transform(data_input_df.values)
# split data
_lowerCAmelCase :Optional[Any] = normalize_df[:, 2].tolist()
_lowerCAmelCase :Optional[Any] = normalize_df[:, 0].tolist()
_lowerCAmelCase :Tuple = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
_lowerCAmelCase :Union[str, Any] = normalize_df[:, [1, 2]].tolist()
_lowerCAmelCase :str = x[: len(x) - 1]
_lowerCAmelCase :Dict = x[len(x) - 1 :]
# for linear regression & sarimax
_lowerCAmelCase :Dict = total_date[: len(total_date) - 1]
_lowerCAmelCase :List[Any] = total_user[: len(total_user) - 1]
_lowerCAmelCase :Dict = total_match[: len(total_match) - 1]
_lowerCAmelCase :Optional[Any] = total_date[len(total_date) - 1 :]
_lowerCAmelCase :List[str] = total_user[len(total_user) - 1 :]
_lowerCAmelCase :str = total_match[len(total_match) - 1 :]
# voting system with forecasting
_lowerCAmelCase :Union[str, Any] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
_lowerCAmelCase :Any = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 506 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
UpperCamelCase_ : Union[str, Any] = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
class __lowerCAmelCase ( _lowercase ):
"""simple docstring"""
snake_case = "swin2sr"
snake_case = {
"hidden_size": "embed_dim",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Optional[Any] , _snake_case : Optional[int]=64 , _snake_case : Tuple=1 , _snake_case : str=3 , _snake_case : Optional[int]=180 , _snake_case : Dict=[6, 6, 6, 6, 6, 6] , _snake_case : List[str]=[6, 6, 6, 6, 6, 6] , _snake_case : List[str]=8 , _snake_case : Optional[Any]=2.0 , _snake_case : Optional[int]=True , _snake_case : Optional[int]=0.0 , _snake_case : Dict=0.0 , _snake_case : Tuple=0.1 , _snake_case : Dict="gelu" , _snake_case : Tuple=False , _snake_case : Tuple=0.0_2 , _snake_case : List[Any]=1e-5 , _snake_case : List[Any]=2 , _snake_case : Tuple=1.0 , _snake_case : Any="1conv" , _snake_case : Union[str, Any]="pixelshuffle" , **_snake_case : int , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**_snake_case )
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = embed_dim
A_ = depths
A_ = len(_snake_case )
A_ = num_heads
A_ = window_size
A_ = mlp_ratio
A_ = qkv_bias
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = drop_path_rate
A_ = hidden_act
A_ = use_absolute_embeddings
A_ = layer_norm_eps
A_ = initializer_range
A_ = upscale
A_ = img_range
A_ = resi_connection
A_ = upsampler
| 482 |
"""simple docstring"""
def A_ (__a , __a , __a ):
'''simple docstring'''
A_ = len(__a )
A_ = [[0] * n for i in range(__a )]
for i in range(__a ):
A_ = y_points[i]
for i in range(2 , __a ):
for j in range(__a , __a ):
A_ = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 482 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = "https://openaipublic.azureedge.net/jukebox/models/"
lowerCamelCase : str = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def _SCREAMING_SNAKE_CASE ( lowercase : Any ):
'''simple docstring'''
if key.endswith('.model.1.bias' ) and len(key.split('.' ) ) > 10:
lowerCamelCase_ = key.replace('.model.1.bias' , '.conv1d_1.bias' )
elif key.endswith('.model.1.weight' ) and len(key.split('.' ) ) > 10:
lowerCamelCase_ = key.replace('.model.1.weight' , '.conv1d_1.weight' )
elif key.endswith('.model.3.bias' ) and len(key.split('.' ) ) > 10:
lowerCamelCase_ = key.replace('.model.3.bias' , '.conv1d_2.bias' )
elif key.endswith('.model.3.weight' ) and len(key.split('.' ) ) > 10:
lowerCamelCase_ = key.replace('.model.3.weight' , '.conv1d_2.weight' )
if "conditioner_blocks.0." in key:
lowerCamelCase_ = key.replace('conditioner_blocks.0' , 'conditioner_blocks' )
if "prime_prior" in key:
lowerCamelCase_ = key.replace('prime_prior' , 'encoder' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
lowerCamelCase_ = key.replace('.emb.' , '.' )
if key.endswith('k' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('.k' , '.codebook' )
if "y_emb." in key:
return key.replace('y_emb.' , 'metadata_embedding.' )
if "x_emb.emb." in key:
lowerCamelCase_ = key.replace('0.x_emb.emb' , 'embed_tokens' )
if "prime_state_ln" in key:
return key.replace('prime_state_ln' , 'encoder.final_layer_norm' )
if ".ln" in key:
return key.replace('.ln' , '.layer_norm' )
if "_ln" in key:
return key.replace('_ln' , '_layer_norm' )
if "prime_state_proj" in key:
return key.replace('prime_state_proj' , 'encoder.proj_in' )
if "prime_x_out" in key:
return key.replace('prime_x_out' , 'encoder.lm_head' )
if "prior.x_out" in key:
return key.replace('x_out' , 'fc_proj_out' )
if "x_emb" in key:
return key.replace('x_emb' , 'embed_tokens' )
return key
def _SCREAMING_SNAKE_CASE ( lowercase : Union[str, Any] , lowercase : Any , lowercase : Union[str, Any] , lowercase : Tuple ):
'''simple docstring'''
lowerCamelCase_ = {}
import re
lowerCamelCase_ = re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
lowerCamelCase_ = re.compile(
r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
lowerCamelCase_ = re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )
lowerCamelCase_ = re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
lowerCamelCase_ = re.compile(
r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
lowerCamelCase_ = re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )
lowerCamelCase_ = re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)' )
lowerCamelCase_ = re.compile(
r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
lowerCamelCase_ = re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(lowercase ):
lowerCamelCase_ = re_encoder_block_conv_in.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[2] ) * 2 + int(groups[3] )
lowerCamelCase_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
lowerCamelCase_ = re_encoder_block_conv_in.sub(lowercase , lowercase )
elif re_encoder_block_resnet.fullmatch(lowercase ):
lowerCamelCase_ = re_encoder_block_resnet.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[2] ) * 2 + int(groups[3] )
lowerCamelCase_ = {'1': 1, '3': 2}[groups[-2]]
lowerCamelCase_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
lowerCamelCase_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
lowerCamelCase_ = prefix + resnet_block
lowerCamelCase_ = re_encoder_block_resnet.sub(lowercase , lowercase )
elif re_encoder_block_proj_out.fullmatch(lowercase ):
lowerCamelCase_ = re_encoder_block_proj_out.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
lowerCamelCase_ = re_encoder_block_proj_out.sub(lowercase , lowercase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(lowercase ):
lowerCamelCase_ = re_decoder_block_conv_out.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCamelCase_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
lowerCamelCase_ = re_decoder_block_conv_out.sub(lowercase , lowercase )
elif re_decoder_block_resnet.fullmatch(lowercase ):
lowerCamelCase_ = re_decoder_block_resnet.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCamelCase_ = {'1': 1, '3': 2}[groups[-2]]
lowerCamelCase_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
lowerCamelCase_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
lowerCamelCase_ = prefix + resnet_block
lowerCamelCase_ = re_decoder_block_resnet.sub(lowercase , lowercase )
elif re_decoder_block_proj_in.fullmatch(lowercase ):
lowerCamelCase_ = re_decoder_block_proj_in.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
lowerCamelCase_ = re_decoder_block_proj_in.sub(lowercase , lowercase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(lowercase ):
lowerCamelCase_ = re_prior_cond_conv_out.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCamelCase_ = f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
lowerCamelCase_ = re_prior_cond_conv_out.sub(lowercase , lowercase )
elif re_prior_cond_resnet.fullmatch(lowercase ):
lowerCamelCase_ = re_prior_cond_resnet.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCamelCase_ = {'1': 1, '3': 2}[groups[-2]]
lowerCamelCase_ = f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
lowerCamelCase_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
lowerCamelCase_ = prefix + resnet_block
lowerCamelCase_ = re_prior_cond_resnet.sub(lowercase , lowercase )
elif re_prior_cond_proj_in.fullmatch(lowercase ):
lowerCamelCase_ = re_prior_cond_proj_in.match(lowercase )
lowerCamelCase_ = regex_match.groups()
lowerCamelCase_ = f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
lowerCamelCase_ = re_prior_cond_proj_in.sub(lowercase , lowercase )
# keep original key
else:
lowerCamelCase_ = original_key
lowerCamelCase_ = replace_key(lowercase )
if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
print(f"""failed converting {original_key} to {key}, does not match""" )
# handle missmatched shape
elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
lowerCamelCase_ = model_state_dict[f"""{key_prefix}.{key}"""]
print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""" )
lowerCamelCase_ = original_key
lowerCamelCase_ = original_key
lowerCamelCase_ = value
return new_dict
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( lowercase : List[Any]=None , lowercase : List[str]=None ):
'''simple docstring'''
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" ):
lowerCamelCase_ = requests.get(f"""{PREFIX}{file}""" , allow_redirects=lowercase )
os.makedirs(f"""{pytorch_dump_folder_path}/""" , exist_ok=lowercase )
open(f"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" , 'wb' ).write(r.content )
lowerCamelCase_ = MODEL_MAPPING[model_name.split('/' )[-1]]
lowerCamelCase_ = JukeboxConfig.from_pretrained(lowercase )
lowerCamelCase_ = JukeboxModel(lowercase )
lowerCamelCase_ = []
lowerCamelCase_ = {}
for i, dict_name in enumerate(lowercase ):
lowerCamelCase_ = torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}""" )['model']
lowerCamelCase_ = {}
for k in old_dic.keys():
if k.endswith('.b' ):
lowerCamelCase_ = old_dic[k]
elif k.endswith('.w' ):
lowerCamelCase_ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
lowerCamelCase_ = old_dic[k]
else:
lowerCamelCase_ = old_dic[k]
lowerCamelCase_ = 'vqvae' if i == 0 else f"""priors.{3 - i}"""
lowerCamelCase_ = fix_jukebox_keys(lowercase , model.state_dict() , lowercase , lowercase )
weight_dict.append(lowercase )
lowerCamelCase_ = weight_dict.pop(0 )
model.vqvae.load_state_dict(lowercase )
for i in range(len(lowercase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(lowercase ).mkdir(exist_ok=lowercase )
with open(f"""{pytorch_dump_folder_path}/mapping.json""" , 'w' ) as txtfile:
json.dump(lowercase , lowercase )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase )
return weight_dict
if __name__ == "__main__":
lowerCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
lowerCamelCase : List[str] = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 70 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = BertTokenizer
UpperCamelCase = BertTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = filter_non_english
def a__ ( self : List[Any] ) -> int:
"""simple docstring"""
super().setUp()
lowerCamelCase_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def a__ ( self : Tuple , A_ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = 'unwanted, running'
return input_text, output_text
def a__ ( self : Any ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer_class(self.vocab_file )
lowerCamelCase_ = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(A_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , [9, 6, 7, 12, 10, 11] )
def a__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = tokenizer.tokenize(A_ )
lowerCamelCase_ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = tokenizer.encode(A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
# With lower casing
lowerCamelCase_ = self.get_tokenizer(do_lower_case=A_ )
lowerCamelCase_ = self.get_rust_tokenizer(do_lower_case=A_ )
lowerCamelCase_ = 'UNwant\u00E9d,running'
lowerCamelCase_ = tokenizer.tokenize(A_ )
lowerCamelCase_ = rust_tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ , add_special_tokens=A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = tokenizer.encode(A_ )
lowerCamelCase_ = rust_tokenizer.encode(A_ )
self.assertListEqual(A_ , A_ )
def a__ ( self : Any ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def a__ ( self : Dict ) -> int:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : str ) -> int:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def a__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : Tuple ) -> str:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def a__ ( self : int ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : str ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : Dict ) -> Any:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , strip_accents=A_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a__ ( self : int ) -> Any:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer(do_lower_case=A_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def a__ ( self : List[Any] ) -> int:
"""simple docstring"""
lowerCamelCase_ = BasicTokenizer()
lowerCamelCase_ = 'a\n\'ll !!to?\'d of, can\'t.'
lowerCamelCase_ = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(A_ ) , A_ )
def a__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
lowerCamelCase_ = {}
for i, token in enumerate(A_ ):
lowerCamelCase_ = i
lowerCamelCase_ = WordpieceTokenizer(vocab=A_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def a__ ( self : Optional[Any] ) -> str:
"""simple docstring"""
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def a__ ( self : List[Any] ) -> int:
"""simple docstring"""
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def a__ ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def a__ ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(A_ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def a__ ( self : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer_class.from_pretrained('bert-base-uncased' )
lowerCamelCase_ = tokenizer.encode('sequence builders' , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer.encode('multi-sequence build' , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(A_ )
lowerCamelCase_ = tokenizer.build_inputs_with_special_tokens(A_ , A_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def a__ ( self : str ) -> str:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
lowerCamelCase_ = tokenizer_r.encode_plus(
A_ , return_attention_mask=A_ , return_token_type_ids=A_ , return_offsets_mapping=A_ , add_special_tokens=A_ , )
lowerCamelCase_ = tokenizer_r.do_lower_case if hasattr(A_ , 'do_lower_case' ) else False
lowerCamelCase_ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def a__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = ['的', '人', '有']
lowerCamelCase_ = ''.join(A_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCamelCase_ = True
lowerCamelCase_ = self.tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = tokenizer_p.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.convert_ids_to_tokens(A_ )
lowerCamelCase_ = tokenizer_p.convert_ids_to_tokens(A_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A_ , A_ )
self.assertListEqual(A_ , A_ )
lowerCamelCase_ = False
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(A_ , **A_ )
lowerCamelCase_ = tokenizer_r.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_p.encode(A_ , add_special_tokens=A_ )
lowerCamelCase_ = tokenizer_r.convert_ids_to_tokens(A_ )
lowerCamelCase_ = tokenizer_p.convert_ids_to_tokens(A_ )
# it is expected that only the first Chinese character is not preceded by "##".
lowerCamelCase_ = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(A_ )
]
self.assertListEqual(A_ , A_ )
self.assertListEqual(A_ , A_ )
| 70 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def _lowerCAmelCase ( A__ , A__ , A__ ):
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , 'IN_MEMORY_MAX_SIZE' , A__ )
lowercase__ = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowercase__ = dataset_size < in_memory_max_size
else:
lowercase__ = False
lowercase__ = is_small_dataset(A__ )
assert result == expected
| 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : List[str] = logging.get_logger(__name__)
a__ : List[Any] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class UpperCAmelCase__( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
A : List[str] = "focalnet"
def __init__( self : Dict , lowerCAmelCase : Union[str, Any]=2_24 , lowerCAmelCase : List[str]=4 , lowerCAmelCase : int=3 , lowerCAmelCase : Union[str, Any]=96 , lowerCAmelCase : List[Any]=False , lowerCAmelCase : int=[1_92, 3_84, 7_68, 7_68] , lowerCAmelCase : str=[2, 2, 6, 2] , lowerCAmelCase : Tuple=[2, 2, 2, 2] , lowerCAmelCase : Optional[Any]=[3, 3, 3, 3] , lowerCAmelCase : int="gelu" , lowerCAmelCase : Any=4.0 , lowerCAmelCase : List[str]=0.0 , lowerCAmelCase : Union[str, Any]=0.1 , lowerCAmelCase : Union[str, Any]=False , lowerCAmelCase : Tuple=1E-4 , lowerCAmelCase : List[Any]=False , lowerCAmelCase : Optional[int]=False , lowerCAmelCase : List[str]=False , lowerCAmelCase : str=0.02 , lowerCAmelCase : Optional[int]=1E-5 , lowerCAmelCase : List[Any]=32 , lowerCAmelCase : List[Any]=None , lowerCAmelCase : Union[str, Any]=None , **lowerCAmelCase : str , ) -> List[str]:
"""simple docstring"""
super().__init__(**lowerCAmelCase)
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = embed_dim
lowercase__ = use_conv_embed
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = focal_levels
lowercase__ = focal_windows
lowercase__ = hidden_act
lowercase__ = mlp_ratio
lowercase__ = hidden_dropout_prob
lowercase__ = drop_path_rate
lowercase__ = use_layerscale
lowercase__ = layerscale_value
lowercase__ = use_post_layernorm
lowercase__ = use_post_layernorm_in_modulation
lowercase__ = normalize_modulator
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = encoder_stride
lowercase__ = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(self.depths) + 1)]
lowercase__, lowercase__ = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase , out_indices=lowerCAmelCase , stage_names=self.stage_names)
| 642 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = 42
class SCREAMING_SNAKE_CASE (a__ , a__ ):
@register_to_config
def __init__( self , _UpperCAmelCase = 3 , _UpperCAmelCase = 3 , _UpperCAmelCase = ("DownEncoderBlock2D",) , _UpperCAmelCase = ("UpDecoderBlock2D",) , _UpperCAmelCase = (64,) , _UpperCAmelCase = 1 , _UpperCAmelCase = "silu" , _UpperCAmelCase = 3 , _UpperCAmelCase = 32 , _UpperCAmelCase = 256 , _UpperCAmelCase = 32 , _UpperCAmelCase = None , _UpperCAmelCase = 0.18215 , _UpperCAmelCase = "group" , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
__A : Optional[int] = Encoder(
in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , down_block_types=_UpperCAmelCase , block_out_channels=_UpperCAmelCase , layers_per_block=_UpperCAmelCase , act_fn=_UpperCAmelCase , norm_num_groups=_UpperCAmelCase , double_z=_UpperCAmelCase , )
__A : Dict = vq_embed_dim if vq_embed_dim is not None else latent_channels
__A : Union[str, Any] = nn.Convad(_UpperCAmelCase , _UpperCAmelCase , 1)
__A : List[Any] = VectorQuantizer(_UpperCAmelCase , _UpperCAmelCase , beta=0.25 , remap=_UpperCAmelCase , sane_index_shape=_UpperCAmelCase)
__A : Dict = nn.Convad(_UpperCAmelCase , _UpperCAmelCase , 1)
# pass init params to Decoder
__A : Any = Decoder(
in_channels=_UpperCAmelCase , out_channels=_UpperCAmelCase , up_block_types=_UpperCAmelCase , block_out_channels=_UpperCAmelCase , layers_per_block=_UpperCAmelCase , act_fn=_UpperCAmelCase , norm_num_groups=_UpperCAmelCase , norm_type=_UpperCAmelCase , )
@apply_forward_hook
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = True):
'''simple docstring'''
__A : Optional[int] = self.encoder(_UpperCAmelCase)
__A : str = self.quant_conv(_UpperCAmelCase)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_UpperCAmelCase)
@apply_forward_hook
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = False , _UpperCAmelCase = True):
'''simple docstring'''
if not force_not_quantize:
__A ,__A ,__A : Dict = self.quantize(_UpperCAmelCase)
else:
__A : int = h
__A : List[Any] = self.post_quant_conv(_UpperCAmelCase)
__A : Union[str, Any] = self.decoder(_UpperCAmelCase , quant if self.config.norm_type == 'spatial' else None)
if not return_dict:
return (dec,)
return DecoderOutput(sample=_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = True):
'''simple docstring'''
__A : Any = sample
__A : Optional[int] = self.encode(_UpperCAmelCase).latents
__A : Union[str, Any] = self.decode(_UpperCAmelCase).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_UpperCAmelCase) | 8 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__SCREAMING_SNAKE_CASE = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__SCREAMING_SNAKE_CASE = tuple[int, int]
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : str , A__ : int , A__ : int , A__ : int , A__ : int , A__ : int , A__ : Node | None , ) -> None:
'''simple docstring'''
a__ : Optional[int] = pos_x
a__ : str = pos_y
a__ : Optional[int] = (pos_y, pos_x)
a__ : List[str] = goal_x
a__ : Any = goal_y
a__ : Any = g_cost
a__ : Optional[int] = parent
a__ : Union[str, Any] = self.calculate_heuristic()
a__ : List[Any] = self.g_cost + self.h_cost
def __lowerCAmelCase ( self : Union[str, Any] ) -> float:
'''simple docstring'''
a__ : List[str] = self.pos_x - self.goal_x
a__ : List[str] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(A__ ) + abs(A__ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : List[Any] , A__ : Node ) -> bool:
'''simple docstring'''
return self.f_cost < other.f_cost
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , A__ : TPosition , A__ : TPosition ) -> Optional[Any]:
'''simple docstring'''
a__ : int = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , A__ )
a__ : Dict = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , A__ )
a__ : Dict = [self.start]
a__ : list[Node] = []
a__ : str = False
def __lowerCAmelCase ( self : List[str] ) -> list[TPosition]:
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
a__ : Dict = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(A__ )
self.closed_nodes.append(A__ )
a__ : List[Any] = self.get_successors(A__ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(A__ )
else:
# retrieve the best current path
a__ : Optional[int] = self.open_nodes.pop(self.open_nodes.index(A__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(A__ )
else:
self.open_nodes.append(A__ )
return [self.start.pos]
def __lowerCAmelCase ( self : Optional[Any] , A__ : Node ) -> list[Node]:
'''simple docstring'''
a__ : Optional[int] = []
for action in delta:
a__ : List[Any] = parent.pos_x + action[1]
a__ : Tuple = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(A__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
A__ , A__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , A__ , ) )
return successors
def __lowerCAmelCase ( self : List[Any] , A__ : Node | None ) -> list[TPosition]:
'''simple docstring'''
a__ : Union[str, Any] = node
a__ : Optional[Any] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
a__ : Any = current_node.parent
path.reverse()
return path
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , A__ : TPosition , A__ : TPosition ) -> None:
'''simple docstring'''
a__ : str = AStar(A__ , A__ )
a__ : Optional[int] = AStar(A__ , A__ )
a__ : List[str] = False
def __lowerCAmelCase ( self : Tuple ) -> list[TPosition]:
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
a__ : int = self.fwd_astar.open_nodes.pop(0 )
a__ : List[Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
A__ , A__ )
self.fwd_astar.closed_nodes.append(A__ )
self.bwd_astar.closed_nodes.append(A__ )
a__ : Tuple = current_bwd_node
a__ : Optional[int] = current_fwd_node
a__ : Optional[int] = {
self.fwd_astar: self.fwd_astar.get_successors(A__ ),
self.bwd_astar: self.bwd_astar.get_successors(A__ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(A__ )
else:
# retrieve the best current path
a__ : Optional[Any] = astar.open_nodes.pop(
astar.open_nodes.index(A__ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(A__ )
else:
astar.open_nodes.append(A__ )
return [self.fwd_astar.start.pos]
def __lowerCAmelCase ( self : List[str] , A__ : Node , A__ : Node ) -> list[TPosition]:
'''simple docstring'''
a__ : str = self.fwd_astar.retrace_path(A__ )
a__ : List[str] = self.bwd_astar.retrace_path(A__ )
bwd_path.pop()
bwd_path.reverse()
a__ : Optional[int] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__SCREAMING_SNAKE_CASE = time.time()
__SCREAMING_SNAKE_CASE = AStar(init, goal)
__SCREAMING_SNAKE_CASE = a_star.search()
__SCREAMING_SNAKE_CASE = time.time() - start_time
print(f'AStar execution time = {end_time:f} seconds')
__SCREAMING_SNAKE_CASE = time.time()
__SCREAMING_SNAKE_CASE = BidirectionalAStar(init, goal)
__SCREAMING_SNAKE_CASE = time.time() - bd_start_time
print(f'BidirectionalAStar execution time = {bd_end_time:f} seconds')
| 688 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
_A = {
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if args.student_type == "roberta":
lowercase_ : List[str] = False
elif args.student_type == "gpt2":
lowercase_ : Any = False
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if args.student_type == "roberta":
lowercase_ : Union[str, Any] = False
def _UpperCamelCase ( ):
lowercase_ : List[Any] = argparse.ArgumentParser(description='Training' )
parser.add_argument('--force' , action='store_true' , help='Overwrite dump_path if it already exists.' )
parser.add_argument(
'--dump_path' , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='The output directory (log, checkpoints, parameters, etc.)' )
parser.add_argument(
'--data_file' , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.' , )
parser.add_argument(
'--student_type' , type=SCREAMING_SNAKE_CASE_ , choices=['distilbert', 'roberta', 'gpt2'] , required=SCREAMING_SNAKE_CASE_ , help='The student type (DistilBERT, RoBERTa).' , )
parser.add_argument('--student_config' , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='Path to the student configuration.' )
parser.add_argument(
'--student_pretrained_weights' , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help='Load student initialization checkpoint.' )
parser.add_argument(
'--teacher_type' , choices=['bert', 'roberta', 'gpt2'] , required=SCREAMING_SNAKE_CASE_ , help='Teacher type (BERT, RoBERTa).' )
parser.add_argument('--teacher_name' , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='The teacher model.' )
parser.add_argument('--temperature' , default=2.0 , type=SCREAMING_SNAKE_CASE_ , help='Temperature for the softmax temperature.' )
parser.add_argument(
'--alpha_ce' , default=0.5 , type=SCREAMING_SNAKE_CASE_ , help='Linear weight for the distillation loss. Must be >=0.' )
parser.add_argument(
'--alpha_mlm' , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.' , )
parser.add_argument('--alpha_clm' , default=0.5 , type=SCREAMING_SNAKE_CASE_ , help='Linear weight for the CLM loss. Must be >=0.' )
parser.add_argument('--alpha_mse' , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help='Linear weight of the MSE loss. Must be >=0.' )
parser.add_argument(
'--alpha_cos' , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help='Linear weight of the cosine embedding loss. Must be >=0.' )
parser.add_argument(
'--mlm' , action='store_true' , help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
parser.add_argument(
'--mlm_mask_prop' , default=0.15 , type=SCREAMING_SNAKE_CASE_ , help='Proportion of tokens for which we need to make a prediction.' , )
parser.add_argument('--word_mask' , default=0.8 , type=SCREAMING_SNAKE_CASE_ , help='Proportion of tokens to mask out.' )
parser.add_argument('--word_keep' , default=0.1 , type=SCREAMING_SNAKE_CASE_ , help='Proportion of tokens to keep.' )
parser.add_argument('--word_rand' , default=0.1 , type=SCREAMING_SNAKE_CASE_ , help='Proportion of tokens to randomly replace.' )
parser.add_argument(
'--mlm_smoothing' , default=0.7 , type=SCREAMING_SNAKE_CASE_ , help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).' , )
parser.add_argument('--token_counts' , type=SCREAMING_SNAKE_CASE_ , help='The token counts in the data_file for MLM.' )
parser.add_argument(
'--restrict_ce_to_mask' , action='store_true' , help='If true, compute the distillation loss only the [MLM] prediction distribution.' , )
parser.add_argument(
'--freeze_pos_embs' , action='store_true' , help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.' , )
parser.add_argument(
'--freeze_token_type_embds' , action='store_true' , help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.' , )
parser.add_argument('--n_epoch' , type=SCREAMING_SNAKE_CASE_ , default=3 , help='Number of pass on the whole dataset.' )
parser.add_argument('--batch_size' , type=SCREAMING_SNAKE_CASE_ , default=5 , help='Batch size (for each process).' )
parser.add_argument(
'--group_by_size' , action='store_false' , help='If true, group sequences that have similar length into the same batch. Default is true.' , )
parser.add_argument(
'--gradient_accumulation_steps' , type=SCREAMING_SNAKE_CASE_ , default=50 , help='Gradient accumulation for larger training batches.' , )
parser.add_argument('--warmup_prop' , default=0.05 , type=SCREAMING_SNAKE_CASE_ , help='Linear warmup proportion.' )
parser.add_argument('--weight_decay' , default=0.0 , type=SCREAMING_SNAKE_CASE_ , help='Weight decay if we apply some.' )
parser.add_argument('--learning_rate' , default=5e-4 , type=SCREAMING_SNAKE_CASE_ , help='The initial learning rate for Adam.' )
parser.add_argument('--adam_epsilon' , default=1e-6 , type=SCREAMING_SNAKE_CASE_ , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , default=5.0 , type=SCREAMING_SNAKE_CASE_ , help='Max gradient norm.' )
parser.add_argument('--initializer_range' , default=0.02 , type=SCREAMING_SNAKE_CASE_ , help='Random initialization range.' )
parser.add_argument(
'--fp16' , action='store_true' , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit' , )
parser.add_argument(
'--fp16_opt_level' , type=SCREAMING_SNAKE_CASE_ , default='O1' , help=(
'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
'See details at https://nvidia.github.io/apex/amp.html'
) , )
parser.add_argument('--n_gpu' , type=SCREAMING_SNAKE_CASE_ , default=1 , help='Number of GPUs in the node.' )
parser.add_argument('--local_rank' , type=SCREAMING_SNAKE_CASE_ , default=-1 , help='Distributed training - Local rank' )
parser.add_argument('--seed' , type=SCREAMING_SNAKE_CASE_ , default=56 , help='Random seed' )
parser.add_argument('--log_interval' , type=SCREAMING_SNAKE_CASE_ , default=500 , help='Tensorboard logging interval.' )
parser.add_argument('--checkpoint_interval' , type=SCREAMING_SNAKE_CASE_ , default=4_000 , help='Checkpoint interval.' )
lowercase_ : Optional[Any] = parser.parse_args()
sanity_checks(SCREAMING_SNAKE_CASE_ )
# ARGS #
init_gpu_params(SCREAMING_SNAKE_CASE_ )
set_seed(SCREAMING_SNAKE_CASE_ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
' itUse `--force` if you want to overwrite it' )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(f'''Param: {args}''' )
with open(os.path.join(args.dump_path , 'parameters.json' ) , 'w' ) as f:
json.dump(vars(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , indent=4 )
git_log(args.dump_path )
lowercase_ ,lowercase_ ,lowercase_ : int = MODEL_CLASSES[args.student_type]
lowercase_ ,lowercase_ ,lowercase_ : Dict = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowercase_ : List[str] = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowercase_ : int = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowercase_ : Dict = tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE_ )
lowercase_ : Union[str, Any] = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''' )
lowercase_ : Any = special_tok_ids
lowercase_ : List[str] = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''' )
with open(args.data_file , 'rb' ) as fp:
lowercase_ : Optional[int] = pickle.load(SCREAMING_SNAKE_CASE_ )
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , 'rb' ) as fp:
lowercase_ : str = pickle.load(SCREAMING_SNAKE_CASE_ )
lowercase_ : Any = np.maximum(SCREAMING_SNAKE_CASE_ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowercase_ : Optional[Any] = 0.0 # do not predict special tokens
lowercase_ : Any = torch.from_numpy(SCREAMING_SNAKE_CASE_ )
else:
lowercase_ : Optional[int] = None
lowercase_ : Tuple = LmSeqsDataset(params=SCREAMING_SNAKE_CASE_ , data=SCREAMING_SNAKE_CASE_ )
logger.info('Data loader created.' )
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''' )
lowercase_ : Optional[int] = student_config_class.from_pretrained(args.student_config )
lowercase_ : List[Any] = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowercase_ : Tuple = student_model_class.from_pretrained(args.student_pretrained_weights , config=SCREAMING_SNAKE_CASE_ )
else:
lowercase_ : Union[str, Any] = student_model_class(SCREAMING_SNAKE_CASE_ )
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''' )
logger.info('Student loaded.' )
# TEACHER #
lowercase_ : Optional[int] = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=SCREAMING_SNAKE_CASE_ )
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''' )
logger.info(f'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowercase_ : Optional[Any] = Distiller(
params=SCREAMING_SNAKE_CASE_ , dataset=SCREAMING_SNAKE_CASE_ , token_probs=SCREAMING_SNAKE_CASE_ , student=SCREAMING_SNAKE_CASE_ , teacher=SCREAMING_SNAKE_CASE_ )
distiller.train()
logger.info('Let\'s go get some drinks.' )
if __name__ == "__main__":
main()
| 438 | '''simple docstring'''
import os
import string
import sys
_A = 1 << 8
_A = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 2_7,
'up': 6_5 + ARROW_KEY_FLAG,
'down': 6_6 + ARROW_KEY_FLAG,
'right': 6_7 + ARROW_KEY_FLAG,
'left': 6_8 + ARROW_KEY_FLAG,
'mod_int': 9_1,
'undefined': sys.maxsize,
'interrupt': 3,
'insert': 5_0,
'delete': 5_1,
'pg_up': 5_3,
'pg_down': 5_4,
}
_A = KEYMAP['up']
_A = KEYMAP['left']
if sys.platform == "win32":
_A = []
_A = {
b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
}
for i in range(1_0):
_A = ord(str(i))
def _UpperCamelCase ( ):
if os.name == "nt":
import msvcrt
lowercase_ : Optional[Any] = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(SCREAMING_SNAKE_CASE_ ) == 0:
# Read the keystroke
lowercase_ : int = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowercase_ : int = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowercase_ : List[Any] = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_ )
if ord(SCREAMING_SNAKE_CASE_ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
lowercase_ : Optional[int] = chr(KEYMAP['esc'] )
except KeyError:
lowercase_ : Tuple = cha[1]
else:
lowercase_ : Tuple = ch.decode(SCREAMING_SNAKE_CASE_ )
else:
lowercase_ : Dict = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowercase_ : int = sys.stdin.fileno()
lowercase_ : Union[str, Any] = termios.tcgetattr(SCREAMING_SNAKE_CASE_ )
try:
tty.setraw(SCREAMING_SNAKE_CASE_ )
lowercase_ : Any = sys.stdin.read(1 )
finally:
termios.tcsetattr(SCREAMING_SNAKE_CASE_ , termios.TCSADRAIN , SCREAMING_SNAKE_CASE_ )
return ch
def _UpperCamelCase ( ):
lowercase_ : Union[str, Any] = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["esc"]:
lowercase_ : Any = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["mod_int"]:
lowercase_ : Dict = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(SCREAMING_SNAKE_CASE_ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(SCREAMING_SNAKE_CASE_ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 438 | 1 |
def A ( _lowercase = "The quick brown fox jumps over the lazy dog" , ):
SCREAMING_SNAKE_CASE : Any = set()
# Replace all the whitespace in our sentence
SCREAMING_SNAKE_CASE : str = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(UpperCamelCase__ ) == 26
def A ( _lowercase = "The quick brown fox jumps over the lazy dog" , ):
SCREAMING_SNAKE_CASE : int = [False] * 26
for char in input_str:
if char.islower():
SCREAMING_SNAKE_CASE : int = True
elif char.isupper():
SCREAMING_SNAKE_CASE : int = True
return all(UpperCamelCase__ )
def A ( _lowercase = "The quick brown fox jumps over the lazy dog" , ):
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def A ( ):
from timeit import timeit
SCREAMING_SNAKE_CASE : List[str] = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
print(timeit('''is_pangram()''' , setup=UpperCamelCase__ ) )
print(timeit('''is_pangram_faster()''' , setup=UpperCamelCase__ ) )
print(timeit('''is_pangram_fastest()''' , setup=UpperCamelCase__ ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 248 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_lowerCAmelCase :Dict = logging.get_logger(__name__)
_lowerCAmelCase :Tuple = {'vocab_file': 'spiece.model'}
_lowerCAmelCase :Optional[int] = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
}
}
_lowerCAmelCase :Optional[Any] = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
# Segments (not really needed)
_lowerCAmelCase :Optional[Any] = 0
_lowerCAmelCase :Any = 1
_lowerCAmelCase :int = 2
_lowerCAmelCase :List[str] = 3
_lowerCAmelCase :List[Any] = 4
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ =VOCAB_FILES_NAMES
a__ =PRETRAINED_VOCAB_FILES_MAP
a__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ ='''left'''
def __init__( self , A , A=False , A=True , A=False , A="<s>" , A="</s>" , A="<unk>" , A="<sep>" , A="<pad>" , A="<cls>" , A="<mask>" , A=["<eop>", "<eod>"] , A = None , **A , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase : Optional[int] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token
_UpperCAmelCase : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , additional_special_tokens=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Tuple = do_lower_case
_UpperCAmelCase : Optional[int] = remove_space
_UpperCAmelCase : Union[str, Any] = keep_accents
_UpperCAmelCase : Union[str, Any] = vocab_file
_UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
return len(self.sp_model )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_UpperCAmelCase : Union[str, Any] = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> str:
_UpperCAmelCase : List[Any] = self.__dict__.copy()
_UpperCAmelCase : Union[str, Any] = None
return state
def __setstate__( self , A ) -> str:
_UpperCAmelCase : List[str] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase : List[Any] = {}
_UpperCAmelCase : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self , A ) -> Union[str, Any]:
if self.remove_space:
_UpperCAmelCase : List[Any] = ''' '''.join(inputs.strip().split() )
else:
_UpperCAmelCase : Union[str, Any] = inputs
_UpperCAmelCase : Optional[int] = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
_UpperCAmelCase : Any = unicodedata.normalize('''NFKD''' , A )
_UpperCAmelCase : int = ''''''.join([c for c in outputs if not unicodedata.combining(A )] )
if self.do_lower_case:
_UpperCAmelCase : str = outputs.lower()
return outputs
def __lowerCAmelCase ( self , A ) -> List[str]:
_UpperCAmelCase : Dict = self.preprocess_text(A )
_UpperCAmelCase : Dict = self.sp_model.encode(A , out_type=A )
_UpperCAmelCase : Any = []
for piece in pieces:
if len(A ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
_UpperCAmelCase : Union[str, Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(A , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_UpperCAmelCase : Dict = cur_pieces[1:]
else:
_UpperCAmelCase : Union[str, Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(A )
else:
new_pieces.append(A )
return new_pieces
def __lowerCAmelCase ( self , A ) -> str:
return self.sp_model.PieceToId(A )
def __lowerCAmelCase ( self , A ) -> Any:
return self.sp_model.IdToPiece(A )
def __lowerCAmelCase ( self , A ) -> List[str]:
_UpperCAmelCase : Optional[int] = ''''''.join(A ).replace(A , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self , A , A = False , A = None , A = True , **A , ) -> str:
_UpperCAmelCase : List[Any] = kwargs.pop('''use_source_tokenizer''' , A )
_UpperCAmelCase : Union[str, Any] = self.convert_ids_to_tokens(A , skip_special_tokens=A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Any = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
_UpperCAmelCase : Optional[Any] = []
sub_texts.append(A )
else:
current_sub_text.append(A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
_UpperCAmelCase : Dict = ''''''.join(A )
_UpperCAmelCase : Optional[Any] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
_UpperCAmelCase : List[Any] = self.clean_up_tokenization(A )
return clean_text
else:
return text
def __lowerCAmelCase ( self , A , A = None ) -> List[int]:
_UpperCAmelCase : Tuple = [self.sep_token_id]
_UpperCAmelCase : int = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCAmelCase ( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is not None:
return ([0] * len(A )) + [1] + ([0] * len(A )) + [1, 1]
return ([0] * len(A )) + [1, 1]
def __lowerCAmelCase ( self , A , A = None ) -> List[int]:
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : Dict = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCAmelCase ( self , A , A = None ) -> Tuple[str]:
if not os.path.isdir(A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase : List[str] = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , '''wb''' ) as fi:
_UpperCAmelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 506 | 0 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
UpperCAmelCase = """\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
UpperCAmelCase = """
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"precision\": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'precision@10': 1.0}
"""
def lowercase ( a__ : List[Any] , a__ : Tuple ) -> str:
return float((preds == labels).mean() )
def lowercase ( a__ : Optional[Any] , a__ : int ) -> Tuple:
_UpperCamelCase = simple_accuracy(a__ , a__ )
_UpperCamelCase = float(fa_score(y_true=a__ , y_pred=a__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowercase ( a__ : Optional[Any] , a__ : Optional[int] ) -> Any:
_UpperCamelCase = np.array(a__ )
_UpperCamelCase = np.array(a__ )
_UpperCamelCase = en_sentvecs.shape[0]
# mean centering
_UpperCamelCase = en_sentvecs - np.mean(a__ , axis=0 )
_UpperCamelCase = in_sentvecs - np.mean(a__ , axis=0 )
_UpperCamelCase = cdist(a__ , a__ , '''cosine''' )
_UpperCamelCase = np.array(range(a__ ) )
_UpperCamelCase = sim.argsort(axis=1 )[:, :10]
_UpperCamelCase = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCAmelCase_ ( datasets.Metric):
def _UpperCamelCase ( self : Dict ) -> Dict:
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
'''references''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None , )
def _UpperCamelCase ( self : str , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ) -> List[str]:
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(__UpperCamelCase , __UpperCamelCase )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(__UpperCamelCase , __UpperCamelCase )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(__UpperCamelCase , __UpperCamelCase )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''' )
| 342 | """simple docstring"""
from functools import lru_cache
@lru_cache
def lowercase ( a__ : int ) -> int:
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 342 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase:
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=1_3 , __SCREAMING_SNAKE_CASE=3_0 , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=3_2 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=3_7 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=1_0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=2 , ) -> List[str]:
"""simple docstring"""
a__ = parent
a__ = batch_size
a__ = image_size
a__ = patch_size
a__ = num_channels
a__ = is_training
a__ = use_labels
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = intermediate_size
a__ = hidden_act
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = type_sequence_label_size
a__ = initializer_range
a__ = scope
a__ = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
a__ = (image_size // patch_size) ** 2
a__ = num_patches + 1
def lowercase__ ( self ) -> Optional[int]:
"""simple docstring"""
a__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ = None
if self.use_labels:
a__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ = self.get_config()
return config, pixel_values, labels
def lowercase__ ( self ) -> List[str]:
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowercase__ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
a__ = ViTModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
a__ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase__ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
a__ = ViTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
a__ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
a__ = 1
a__ = ViTForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
a__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a__ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase__ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
a__ = self.type_sequence_label_size
a__ = ViTForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
a__ = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
a__ = 1
a__ = ViTForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
a__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a__ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
a__ = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) ,
) = config_and_inputs
a__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase(_lowercase , _lowercase , unittest.TestCase ):
__snake_case: int = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__snake_case: Any = (
{'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
if is_torch_available()
else {}
)
__snake_case: Optional[Any] = True
__snake_case: int = False
__snake_case: Dict = False
__snake_case: str = False
def lowercase__ ( self ) -> int:
"""simple docstring"""
a__ = ViTModelTester(self )
a__ = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE , hidden_size=3_7 )
def lowercase__ ( self ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def lowercase__ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
def lowercase__ ( self ) -> Dict:
"""simple docstring"""
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear ) )
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ = model_class(__SCREAMING_SNAKE_CASE )
a__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ = [*signature.parameters.keys()]
a__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE )
def lowercase__ ( self ) -> Tuple:
"""simple docstring"""
a__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def lowercase__ ( self ) -> Optional[Any]:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ = ViTModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def __magic_name__ ( ) -> Dict:
a__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase(unittest.TestCase ):
@cached_property
def lowercase__ ( self ) -> Dict:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def lowercase__ ( self ) -> Any:
"""simple docstring"""
a__ = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224' ).to(__SCREAMING_SNAKE_CASE )
a__ = self.default_image_processor
a__ = prepare_img()
a__ = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='pt' ).to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
a__ = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
a__ = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
a__ = torch.tensor([-0.27_44, 0.82_15, -0.08_36] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def lowercase__ ( self ) -> Any:
"""simple docstring"""
a__ = ViTModel.from_pretrained('facebook/dino-vits8' ).to(__SCREAMING_SNAKE_CASE )
a__ = ViTImageProcessor.from_pretrained('facebook/dino-vits8' , size=4_8_0 )
a__ = prepare_img()
a__ = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
a__ = inputs.pixel_values.to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
a__ = model(__SCREAMING_SNAKE_CASE , interpolate_pos_encoding=__SCREAMING_SNAKE_CASE )
# verify the logits
a__ = torch.Size((1, 3_6_0_1, 3_8_4) )
self.assertEqual(outputs.last_hidden_state.shape , __SCREAMING_SNAKE_CASE )
a__ = torch.tensor(
[[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def lowercase__ ( self ) -> Tuple:
"""simple docstring"""
a__ = ViTModel.from_pretrained('facebook/dino-vits8' , torch_dtype=torch.floataa , device_map='auto' )
a__ = self.default_image_processor
a__ = prepare_img()
a__ = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='pt' )
a__ = inputs.pixel_values.to(__SCREAMING_SNAKE_CASE )
# forward pass to make sure inference works in fp16
with torch.no_grad():
a__ = model(__SCREAMING_SNAKE_CASE )
| 273 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowercase(_lowercase , _lowercase , unittest.TestCase ):
__snake_case: Optional[int] = IFImgaImgSuperResolutionPipeline
__snake_case: Dict = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
__snake_case: Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'} )
__snake_case: List[Any] = PipelineTesterMixin.required_optional_params - {'latents'}
def lowercase__ ( self ) -> Tuple:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def lowercase__ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=0 ) -> Union[str, Any]:
"""simple docstring"""
if str(__SCREAMING_SNAKE_CASE ).startswith('mps' ):
a__ = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
a__ = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
a__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
a__ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
a__ = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowercase__ ( self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowercase__ ( self ) -> Any:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowercase__ ( self ) -> List[str]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowercase__ ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_save_load_local()
def lowercase__ ( self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 273 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : int = {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}
class _lowerCAmelCase( _a):
"""simple docstring"""
lowerCamelCase__ = '''t5'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , UpperCAmelCase=3_21_28 , UpperCAmelCase=5_12 , UpperCAmelCase=64 , UpperCAmelCase=20_48 , UpperCAmelCase=6 , UpperCAmelCase=None , UpperCAmelCase=8 , UpperCAmelCase=32 , UpperCAmelCase=1_28 , UpperCAmelCase=0.1 , UpperCAmelCase=1e-6 , UpperCAmelCase=1.0 , UpperCAmelCase="relu" , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=0 , UpperCAmelCase=1 , **UpperCAmelCase , )-> List[str]:
__A = vocab_size
__A = d_model
__A = d_kv
__A = d_ff
__A = num_layers
__A = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__A = num_heads
__A = relative_attention_num_buckets
__A = relative_attention_max_distance
__A = dropout_rate
__A = layer_norm_epsilon
__A = initializer_factor
__A = feed_forward_proj
__A = use_cache
__A = self.feed_forward_proj.split('''-''' )
__A = act_info[-1]
__A = act_info[0] == '''gated'''
if len(UpperCAmelCase ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase ) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__A = '''gelu_new'''
super().__init__(
pad_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , is_encoder_decoder=UpperCAmelCase , **UpperCAmelCase , )
class _lowerCAmelCase( _a):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE__ ( self )-> Mapping[str, Mapping[int, str]]:
__A = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
__A = '''past_encoder_sequence + sequence'''
__A = {0: '''batch'''}
__A = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
__A = {0: '''batch''', 1: '''decoder_sequence'''}
__A = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase , direction='''inputs''' )
return common_inputs
@property
def SCREAMING_SNAKE_CASE__ ( self )-> int:
return 13
| 341 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=14 , UpperCAmelCase=7 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=99 , UpperCAmelCase=32 , UpperCAmelCase=5 , UpperCAmelCase=4 , UpperCAmelCase=37 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=5_12 , UpperCAmelCase=16 , UpperCAmelCase=2 , UpperCAmelCase=0.02 , UpperCAmelCase=3 , UpperCAmelCase=4 , UpperCAmelCase=None , )-> Dict:
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_token_type_ids
__A = use_input_mask
__A = use_labels
__A = use_mc_token_ids
__A = vocab_size
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = max_position_embeddings
__A = type_vocab_size
__A = type_sequence_label_size
__A = initializer_range
__A = num_labels
__A = num_choices
__A = scope
__A = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self )-> Optional[int]:
__A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__A = None
if self.use_input_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = None
if self.use_token_type_ids:
__A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__A = None
if self.use_mc_token_ids:
__A = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
__A = None
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__A = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__A = ids_tensor([self.batch_size] , self.num_choices )
__A = self.get_config()
__A = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , *UpperCAmelCase )-> Tuple:
__A = CTRLModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
model(UpperCAmelCase , token_type_ids=UpperCAmelCase , head_mask=UpperCAmelCase )
model(UpperCAmelCase , token_type_ids=UpperCAmelCase )
__A = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , *UpperCAmelCase )-> Optional[Any]:
__A = CTRLLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__A = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self )-> Optional[int]:
__A = self.prepare_config_and_inputs()
(
(
__A
) , (
__A
) , (
__A
) , (
__A
) , (
__A
) , (
__A
) , (
__A
) , (
__A
) , (
__A
) ,
) = config_and_inputs
__A = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , *UpperCAmelCase )-> Optional[int]:
__A = self.num_labels
__A = CTRLForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__A = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class _lowerCAmelCase( _a , _a , _a , unittest.TestCase):
"""simple docstring"""
lowerCamelCase__ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
lowerCamelCase__ = (CTRLLMHeadModel,) if is_torch_available() else ()
lowerCamelCase__ = (
{
'''feature-extraction''': CTRLModel,
'''text-classification''': CTRLForSequenceClassification,
'''text-generation''': CTRLLMHeadModel,
'''zero-shot''': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> Dict:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self )-> str:
__A = CTRLModelTester(self )
__A = ConfigTester(self , config_class=UpperCAmelCase , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self )-> Union[str, Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self )-> List[str]:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self )-> List[Any]:
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self )-> Dict:
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCAmelCase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE__ ( self )-> Optional[int]:
pass
@slow
def SCREAMING_SNAKE_CASE__ ( self )-> Optional[Any]:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = CTRLModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@unittest.skip('''The model doesn\'t support left padding''' ) # and it's not used enough to be worth fixing :)
def SCREAMING_SNAKE_CASE__ ( self )-> Union[str, Any]:
pass
@require_torch
class _lowerCAmelCase( unittest.TestCase):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self )-> Dict:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def SCREAMING_SNAKE_CASE__ ( self )-> List[Any]:
__A = CTRLLMHeadModel.from_pretrained('''ctrl''' )
model.to(UpperCAmelCase )
__A = torch.tensor(
[[1_18_59, 0, 16_11, 8]] , dtype=torch.long , device=UpperCAmelCase ) # Legal the president is
__A = [
1_18_59,
0,
16_11,
8,
5,
1_50,
2_64_49,
2,
19,
3_48,
4_69,
3,
25_95,
48,
2_07_40,
24_65_33,
24_65_33,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
__A = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].tolist() , UpperCAmelCase )
| 341 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def a_ ( _UpperCAmelCase : int = 8 ,_UpperCAmelCase : int | None = None ) -> str:
__snake_case : Tuple = np.random.default_rng(seed=_UpperCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
__snake_case : int = 6 * key_len
# Measurement basis for Alice's qubits.
__snake_case : str = rng.integers(2 ,size=_UpperCAmelCase )
# The set of states Alice will prepare.
__snake_case : Optional[int] = rng.integers(2 ,size=_UpperCAmelCase )
# Measurement basis for Bob's qubits.
__snake_case : List[Any] = rng.integers(2 ,size=_UpperCAmelCase )
# Quantum Circuit to simulate BB84
__snake_case : Optional[int] = qiskit.QuantumCircuit(_UpperCAmelCase ,name='BB84' )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(_UpperCAmelCase ):
if alice_state[index] == 1:
bbaa_circ.x(_UpperCAmelCase )
if alice_basis[index] == 1:
bbaa_circ.h(_UpperCAmelCase )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(_UpperCAmelCase ):
if bob_basis[index] == 1:
bbaa_circ.h(_UpperCAmelCase )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
__snake_case : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
__snake_case : Optional[int] = qiskit.execute(_UpperCAmelCase ,_UpperCAmelCase ,shots=1 ,seed_simulator=_UpperCAmelCase )
# Returns the result of measurement.
__snake_case : Any = job.result().get_counts(_UpperCAmelCase ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
__snake_case : str = ''.join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
__snake_case : List[Any] = gen_key[:key_len] if len(_UpperCAmelCase ) >= key_len else gen_key.ljust(_UpperCAmelCase ,'0' )
return key
if __name__ == "__main__":
print(F"""The generated key is : {bbaa(8, seed=0)}""")
from doctest import testmod
testmod()
| 286 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class snake_case__ :
def __init__( self : List[Any] , __a : str , __a : Dict , __a : List[Any] , __a : str , __a : str , __a : List[str]=0.2 , __a : Any=0.2 ) -> Any:
'''simple docstring'''
__snake_case : Any = bp_numa
__snake_case : str = bp_numa
__snake_case : Optional[Any] = bp_numa
__snake_case : Any = conva_get[:2]
__snake_case : Dict = conva_get[2]
__snake_case : Optional[int] = size_pa
__snake_case : str = rate_w
__snake_case : Optional[Any] = rate_t
__snake_case : Optional[Any] = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
__snake_case : List[Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
__snake_case : Any = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
__snake_case : Optional[int] = -2 * np.random.rand(self.conva[1] ) + 1
__snake_case : Optional[Any] = -2 * np.random.rand(self.num_bpa ) + 1
__snake_case : str = -2 * np.random.rand(self.num_bpa ) + 1
def A_ ( self : int , __a : Dict ) -> Optional[Any]:
'''simple docstring'''
# save model dict with pickle
__snake_case : int = {
'num_bp1': self.num_bpa,
'num_bp2': self.num_bpa,
'num_bp3': self.num_bpa,
'conv1': self.conva,
'step_conv1': self.step_conva,
'size_pooling1': self.size_poolinga,
'rate_weight': self.rate_weight,
'rate_thre': self.rate_thre,
'w_conv1': self.w_conva,
'wkj': self.wkj,
'vji': self.vji,
'thre_conv1': self.thre_conva,
'thre_bp2': self.thre_bpa,
'thre_bp3': self.thre_bpa,
}
with open(__a , 'wb' ) as f:
pickle.dump(__a , __a )
print(f'''Model saved： {save_path}''' )
@classmethod
def A_ ( cls : List[Any] , __a : Union[str, Any] ) -> int:
'''simple docstring'''
# read saved model
with open(__a , 'rb' ) as f:
__snake_case : Dict = pickle.load(__a ) # noqa: S301
__snake_case : Tuple = model_dic.get('conv1' )
conv_get.append(model_dic.get('step_conv1' ) )
__snake_case : Optional[Any] = model_dic.get('size_pooling1' )
__snake_case : int = model_dic.get('num_bp1' )
__snake_case : Optional[Any] = model_dic.get('num_bp2' )
__snake_case : Optional[Any] = model_dic.get('num_bp3' )
__snake_case : Any = model_dic.get('rate_weight' )
__snake_case : Optional[int] = model_dic.get('rate_thre' )
# create model instance
__snake_case : Any = CNN(__a , __a , __a , __a , __a , __a , __a )
# modify model parameter
__snake_case : Dict = model_dic.get('w_conv1' )
__snake_case : Any = model_dic.get('wkj' )
__snake_case : List[str] = model_dic.get('vji' )
__snake_case : int = model_dic.get('thre_conv1' )
__snake_case : Optional[int] = model_dic.get('thre_bp2' )
__snake_case : List[Any] = model_dic.get('thre_bp3' )
return conv_ins
def A_ ( self : List[Any] , __a : Tuple ) -> Optional[int]:
'''simple docstring'''
return 1 / (1 + np.exp(-1 * x ))
def A_ ( self : Any , __a : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return round(__a , 3 )
def A_ ( self : Optional[Any] , __a : Tuple , __a : List[str] , __a : Dict , __a : Optional[int] , __a : List[str] ) -> str:
'''simple docstring'''
# convolution process
__snake_case : int = convs[0]
__snake_case : List[str] = convs[1]
__snake_case : Optional[Any] = np.shape(__a )[0]
# get the data slice of original image data, data_focus
__snake_case : str = []
for i_focus in range(0 , size_data - size_conv + 1 , __a ):
for j_focus in range(0 , size_data - size_conv + 1 , __a ):
__snake_case : Any = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(__a )
# calculate the feature map of every single kernel, and saved as list of matrix
__snake_case : Optional[int] = []
__snake_case : Dict = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(__a ):
__snake_case : Optional[int] = []
for i_focus in range(len(__a ) ):
__snake_case : List[Any] = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(__a ) )
__snake_case : List[Any] = np.asmatrix(__a ).reshape(
__a , __a )
data_featuremap.append(__a )
# expanding the data slice to One dimenssion
__snake_case : Union[str, Any] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(__a ) )
__snake_case : List[Any] = np.asarray(__a )
return focus_list, data_featuremap
def A_ ( self : Any , __a : int , __a : Tuple , __a : List[Any]="average_pool" ) -> Dict:
'''simple docstring'''
# pooling process
__snake_case : List[str] = len(featuremaps[0] )
__snake_case : Tuple = int(size_map / size_pooling )
__snake_case : int = []
for i_map in range(len(__a ) ):
__snake_case : str = featuremaps[i_map]
__snake_case : Optional[Any] = []
for i_focus in range(0 , __a , __a ):
for j_focus in range(0 , __a , __a ):
__snake_case : Dict = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(__a ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(__a ) )
__snake_case : List[str] = np.asmatrix(__a ).reshape(__a , __a )
featuremap_pooled.append(__a )
return featuremap_pooled
def A_ ( self : List[str] , __a : Union[str, Any] ) -> int:
'''simple docstring'''
# expanding three dimension data to one dimension list
__snake_case : Tuple = []
for i in range(len(__a ) ):
__snake_case : Optional[int] = np.shape(data[i] )
__snake_case : List[str] = data[i].reshape(1 , shapes[0] * shapes[1] )
__snake_case : List[Any] = data_listed.getA().tolist()[0]
data_expanded.extend(__a )
__snake_case : Optional[int] = np.asarray(__a )
return data_expanded
def A_ ( self : Union[str, Any] , __a : int ) -> Any:
'''simple docstring'''
# expanding matrix to one dimension list
__snake_case : int = np.asarray(__a )
__snake_case : str = np.shape(__a )
__snake_case : Any = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def A_ ( self : List[Any] , __a : str , __a : Optional[int] , __a : List[str] , __a : int , __a : List[str] ) -> Dict:
'''simple docstring'''
__snake_case : Union[str, Any] = []
__snake_case : Tuple = 0
for i_map in range(__a ):
__snake_case : Union[str, Any] = np.ones((size_map, size_map) )
for i in range(0 , __a , __a ):
for j in range(0 , __a , __a ):
__snake_case : Any = pd_pool[
i_pool
]
__snake_case : List[Any] = i_pool + 1
__snake_case : List[Any] = np.multiply(
__a , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(__a )
return pd_all
def A_ ( self : Tuple , __a : List[str] , __a : Optional[int] , __a : Union[str, Any] , __a : Dict , __a : Tuple , __a : Optional[int]=bool ) -> List[Any]:
'''simple docstring'''
# model traning
print('----------------------Start Training-------------------------' )
print((' - - Shape: Train_Data ', np.shape(__a )) )
print((' - - Shape: Teach_Data ', np.shape(__a )) )
__snake_case : str = 0
__snake_case : List[str] = []
__snake_case : List[Any] = 10000
while rp < n_repeat and mse >= error_accuracy:
__snake_case : int = 0
print(f'''-------------Learning Time {rp}--------------''' )
for p in range(len(__a ) ):
# print('------------Learning Image: %d--------------'%p)
__snake_case : List[Any] = np.asmatrix(datas_train[p] )
__snake_case : Optional[Any] = np.asarray(datas_teach[p] )
__snake_case , __snake_case : List[Any] = self.convolute(
__a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__snake_case : Tuple = self.pooling(__a , self.size_poolinga )
__snake_case : Dict = np.shape(__a )
__snake_case : Tuple = self._expand(__a )
__snake_case : str = data_bp_input
__snake_case : List[Any] = np.dot(__a , self.vji.T ) - self.thre_bpa
__snake_case : Any = self.sig(__a )
__snake_case : Tuple = np.dot(__a , self.wkj.T ) - self.thre_bpa
__snake_case : Optional[Any] = self.sig(__a )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
__snake_case : Tuple = np.multiply(
(data_teach - bp_outa) , np.multiply(__a , (1 - bp_outa) ) )
__snake_case : Tuple = np.multiply(
np.dot(__a , self.wkj ) , np.multiply(__a , (1 - bp_outa) ) )
__snake_case : Union[str, Any] = np.dot(__a , self.vji )
__snake_case : Optional[int] = pd_i_all / (self.size_poolinga * self.size_poolinga)
__snake_case : Tuple = pd_conva_pooled.T.getA().tolist()
__snake_case : Optional[Any] = self._calculate_gradient_from_pool(
__a , __a , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
__snake_case : int = self._expand_mat(pd_conva_all[k_conv] )
__snake_case : Optional[int] = self.rate_weight * np.dot(__a , __a )
__snake_case : List[Any] = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
__snake_case : Optional[Any] = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
__snake_case : Tuple = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
__snake_case : Optional[Any] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
__snake_case : str = self.thre_bpa - pd_k_all * self.rate_thre
__snake_case : Optional[int] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
__snake_case : Any = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
__snake_case : Tuple = rp + 1
__snake_case : Tuple = error_count / patterns
all_mse.append(__a )
def draw_error():
__snake_case : Union[str, Any] = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(__a , '+-' )
plt.plot(__a , 'r--' )
plt.xlabel('Learning Times' )
plt.ylabel('All_mse' )
plt.grid(__a , alpha=0.5 )
plt.show()
print('------------------Training Complished---------------------' )
print((' - - Training epoch: ', rp, f''' - - Mse: {mse:.6f}''') )
if draw_e:
draw_error()
return mse
def A_ ( self : Tuple , __a : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
# model predict
__snake_case : str = []
print('-------------------Start Testing-------------------------' )
print((' - - Shape: Test_Data ', np.shape(__a )) )
for p in range(len(__a ) ):
__snake_case : int = np.asmatrix(datas_test[p] )
__snake_case , __snake_case : str = self.convolute(
__a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__snake_case : List[str] = self.pooling(__a , self.size_poolinga )
__snake_case : List[Any] = self._expand(__a )
__snake_case : Optional[Any] = data_bp_input
__snake_case : Optional[Any] = bp_outa * self.vji.T - self.thre_bpa
__snake_case : Any = self.sig(__a )
__snake_case : Any = bp_outa * self.wkj.T - self.thre_bpa
__snake_case : str = self.sig(__a )
produce_out.extend(bp_outa.getA().tolist() )
__snake_case : List[Any] = [list(map(self.do_round , __a ) ) for each in produce_out]
return np.asarray(__a )
def A_ ( self : Optional[Any] , __a : Optional[int] ) -> Tuple:
'''simple docstring'''
# return the data of image after convoluting process so we can check it out
__snake_case : int = np.asmatrix(__a )
__snake_case , __snake_case : int = self.convolute(
__a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
__snake_case : Dict = self.pooling(__a , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass
| 286 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ = logging.get_logger(__name__)
a__ = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : int = "blip_2_vision_model"
def __init__( self , _a=1_4_0_8 , _a=6_1_4_4 , _a=3_9 , _a=1_6 , _a=2_2_4 , _a=1_4 , _a="gelu" , _a=0.0_0001 , _a=0.0 , _a=1e-1_0 , _a=True , **_a , ) -> str:
super().__init__(**_a )
_a : Any = hidden_size
_a : Optional[Any] = intermediate_size
_a : str = num_hidden_layers
_a : Union[str, Any] = num_attention_heads
_a : Any = patch_size
_a : str = image_size
_a : Tuple = initializer_range
_a : List[Any] = attention_dropout
_a : Any = layer_norm_eps
_a : List[Any] = hidden_act
_a : Optional[int] = qkv_bias
@classmethod
def __lowercase ( cls , _a , **_a ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_a )
_a , _a : List[str] = cls.get_config_dict(_a , **_a )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
_a : Optional[Any] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_a , **_a )
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = "blip_2_qformer"
def __init__( self , _a=3_0_5_2_2 , _a=7_6_8 , _a=1_2 , _a=1_2 , _a=3_0_7_2 , _a="gelu" , _a=0.1 , _a=0.1 , _a=5_1_2 , _a=0.02 , _a=1e-1_2 , _a=0 , _a="absolute" , _a=2 , _a=1_4_0_8 , **_a , ) -> Union[str, Any]:
super().__init__(pad_token_id=_a , **_a )
_a : List[str] = vocab_size
_a : List[str] = hidden_size
_a : List[str] = num_hidden_layers
_a : int = num_attention_heads
_a : List[Any] = hidden_act
_a : str = intermediate_size
_a : Dict = hidden_dropout_prob
_a : int = attention_probs_dropout_prob
_a : str = max_position_embeddings
_a : int = initializer_range
_a : Optional[Any] = layer_norm_eps
_a : Optional[Any] = position_embedding_type
_a : Any = cross_attention_frequency
_a : int = encoder_hidden_size
@classmethod
def __lowercase ( cls , _a , **_a ) -> "PretrainedConfig":
cls._set_token_in_kwargs(_a )
_a , _a : Optional[int] = cls.get_config_dict(_a , **_a )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
_a : Optional[int] = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_a , **_a )
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = "blip-2"
UpperCAmelCase__ : Dict = True
def __init__( self , _a=None , _a=None , _a=None , _a=3_2 , **_a ) -> Optional[int]:
super().__init__(**_a )
if vision_config is None:
_a : Any = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' )
if qformer_config is None:
_a : int = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' )
if text_config is None:
_a : Union[str, Any] = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
_a : List[Any] = BlipaVisionConfig(**_a )
_a : Tuple = BlipaQFormerConfig(**_a )
_a : Tuple = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
_a : Union[str, Any] = CONFIG_MAPPING[text_model_type](**_a )
_a : Dict = self.text_config.tie_word_embeddings
_a : Dict = self.text_config.is_encoder_decoder
_a : Any = num_query_tokens
_a : Tuple = self.vision_config.hidden_size
_a : Optional[int] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_a : Tuple = 1.0
_a : Any = 0.02
@classmethod
def __lowercase ( cls , _a , _a , _a , **_a , ) -> Tuple:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_a , )
def __lowercase ( self ) -> List[Any]:
_a : Dict = copy.deepcopy(self.__dict__ )
_a : int = self.vision_config.to_dict()
_a : List[str] = self.qformer_config.to_dict()
_a : int = self.text_config.to_dict()
_a : Dict = self.__class__.model_type
return output
| 578 |
def __UpperCAmelCase ( __a : int = 100 ) -> int:
"""simple docstring"""
_a : str = (n * (n + 1) // 2) ** 2
_a : int = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 578 | 1 |
from collections.abc import Callable
def a ( snake_case__: Callable[[float], float] , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = a
lowercase_ = b
if function(snake_case__ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case__ ) == 0:
return b
elif (
function(snake_case__ ) * function(snake_case__ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('''could not find root in given interval.''' )
else:
lowercase_ = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(snake_case__ ) == 0:
return mid
elif function(snake_case__ ) * function(snake_case__ ) < 0:
lowercase_ = mid
else:
lowercase_ = mid
lowercase_ = start + (end - start) / 2.0
return mid
def a ( snake_case__: float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_0_0_0))
import doctest
doctest.testmod()
| 97 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_lowerCamelCase : Optional[int] = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
_lowerCamelCase : List[str] = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
_lowerCamelCase : Dict = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE__ ( datasets.Metric ):
'''simple docstring'''
def A ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge'] , reference_urls=[
'https://en.wikipedia.org/wiki/ROUGE_(metric)',
'https://github.com/google-research/google-research/tree/master/rouge',
] , )
def A ( self : Union[str, Any] , lowercase : Tuple , lowercase : Optional[Any] , lowercase : int=None , lowercase : str=True , lowercase : List[str]=False ):
'''simple docstring'''
if rouge_types is None:
_snake_case = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
_snake_case = rouge_scorer.RougeScorer(rouge_types=lowercase , use_stemmer=lowercase )
if use_aggregator:
_snake_case = scoring.BootstrapAggregator()
else:
_snake_case = []
for ref, pred in zip(lowercase , lowercase ):
_snake_case = scorer.score(lowercase , lowercase )
if use_aggregator:
aggregator.add_scores(lowercase )
else:
scores.append(lowercase )
if use_aggregator:
_snake_case = aggregator.aggregate()
else:
_snake_case = {}
for key in scores[0]:
_snake_case = [score[key] for score in scores]
return result | 686 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class a ( __magic_name__ ,unittest.TestCase ):
_snake_case = ShapEImgaImgPipeline
_snake_case = ['''image''']
_snake_case = ['''image''']
_snake_case = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
_snake_case = False
@property
def __snake_case ( self : str ):
return 32
@property
def __snake_case ( self : Optional[int] ):
return 32
@property
def __snake_case ( self : int ):
return self.time_input_dim * 4
@property
def __snake_case ( self : List[Any] ):
return 8
@property
def __snake_case ( self : Optional[Any] ):
torch.manual_seed(0 )
snake_case : List[str] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size, image_size=64, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=1, )
snake_case : List[Any] = CLIPVisionModel(SCREAMING_SNAKE_CASE_ )
return model
@property
def __snake_case ( self : str ):
snake_case : Optional[int] = CLIPImageProcessor(
crop_size=2_24, do_center_crop=SCREAMING_SNAKE_CASE_, do_normalize=SCREAMING_SNAKE_CASE_, do_resize=SCREAMING_SNAKE_CASE_, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], resample=3, size=2_24, )
return image_processor
@property
def __snake_case ( self : Optional[int] ):
torch.manual_seed(0 )
snake_case : List[Any] = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
snake_case : List[str] = PriorTransformer(**SCREAMING_SNAKE_CASE_ )
return model
@property
def __snake_case ( self : Dict ):
torch.manual_seed(0 )
snake_case : List[str] = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
snake_case : Optional[Any] = ShapERenderer(**SCREAMING_SNAKE_CASE_ )
return model
def __snake_case ( self : List[Any] ):
snake_case : Any = self.dummy_prior
snake_case : str = self.dummy_image_encoder
snake_case : Dict = self.dummy_image_processor
snake_case : Tuple = self.dummy_renderer
snake_case : Optional[int] = HeunDiscreteScheduler(
beta_schedule='''exp''', num_train_timesteps=10_24, prediction_type='''sample''', use_karras_sigmas=SCREAMING_SNAKE_CASE_, clip_sample=SCREAMING_SNAKE_CASE_, clip_sample_range=1.0, )
snake_case : Dict = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __snake_case ( self : Dict, SCREAMING_SNAKE_CASE_ : Union[str, Any], SCREAMING_SNAKE_CASE_ : Optional[Any]=0 ):
snake_case : int = floats_tensor((1, 3, 64, 64), rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
snake_case : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
snake_case : Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
snake_case : int = {
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __snake_case ( self : List[Any] ):
snake_case : List[str] = '''cpu'''
snake_case : Optional[Any] = self.get_dummy_components()
snake_case : List[str] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
snake_case : Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : Any = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
snake_case : Dict = output.images[0]
snake_case : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
snake_case : Optional[int] = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __snake_case ( self : Optional[Any] ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __snake_case ( self : Optional[int] ):
snake_case : Union[str, Any] = torch_device == '''cpu'''
snake_case : int = True
self._test_inference_batch_single_identical(
batch_size=2, test_max_difference=SCREAMING_SNAKE_CASE_, relax_max_difference=SCREAMING_SNAKE_CASE_, )
def __snake_case ( self : List[Any] ):
snake_case : Tuple = self.get_dummy_components()
snake_case : Union[str, Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
snake_case : str = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : List[str] = 1
snake_case : Optional[Any] = 2
snake_case : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
for key in inputs.keys():
if key in self.batch_params:
snake_case : Optional[int] = batch_size * [inputs[key]]
snake_case : Dict = pipe(**SCREAMING_SNAKE_CASE_, num_images_per_prompt=SCREAMING_SNAKE_CASE_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def __snake_case ( self : Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self : int ):
snake_case : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
snake_case : Union[str, Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
snake_case : Union[str, Any] = ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
snake_case : Any = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
snake_case : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(0 )
snake_case : List[str] = pipe(
SCREAMING_SNAKE_CASE_, generator=SCREAMING_SNAKE_CASE_, guidance_scale=3.0, num_inference_steps=64, frame_size=64, output_type='''np''', ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
| 716 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
"self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
"self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
UpperCAmelCase = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def A ( A_ : Dict , A_ : Any , A_ : List[str] , A_ : Tuple , A_ : Optional[Any] ):
for attribute in key.split('''.''' ):
snake_case : Tuple = getattr(A_ , A_ )
if weight_type is not None:
snake_case : Optional[Any] = getattr(A_ , A_ ).shape
else:
snake_case : Any = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case : Optional[Any] = value
elif weight_type == "weight_g":
snake_case : Any = value
elif weight_type == "weight_v":
snake_case : int = value
elif weight_type == "bias":
snake_case : Any = value
else:
snake_case : Optional[int] = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def A ( A_ : str , A_ : str ):
snake_case : Dict = []
snake_case : Optional[Any] = fairseq_model.state_dict()
snake_case : int = hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == '''group''' , )
snake_case : int = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
snake_case : Optional[int] = True
if "*" in mapped_key:
snake_case : Dict = name.split(A_ )[0].split('''.''' )[-2]
snake_case : List[str] = mapped_key.replace('''*''' , A_ )
if "weight_g" in name:
snake_case : Optional[Any] = '''weight_g'''
elif "weight_v" in name:
snake_case : Optional[Any] = '''weight_v'''
elif "bias" in name and "relative_attention_bias" not in name:
snake_case : Any = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case : Optional[int] = '''weight'''
else:
snake_case : Union[str, Any] = None
set_recursively(A_ , A_ , A_ , A_ , A_ )
continue
if not is_used:
unused_weights.append(A_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def A ( A_ : Tuple , A_ : Any , A_ : int , A_ : Dict , A_ : Optional[Any] ):
snake_case : List[str] = full_name.split('''conv_layers.''' )[-1]
snake_case : List[Any] = name.split('''.''' )
snake_case : Dict = int(items[0] )
snake_case : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case : Tuple = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case : Dict = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case : str = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case : Tuple = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A_ )
@torch.no_grad()
def A ( A_ : Tuple , A_ : Any , A_ : Optional[Any]=None ):
# load the pre-trained checkpoints
snake_case : Any = torch.load(A_ )
snake_case : List[str] = WavLMConfigOrig(checkpoint['''cfg'''] )
snake_case : Union[str, Any] = WavLMOrig(A_ )
model.load_state_dict(checkpoint['''model'''] )
model.eval()
if config_path is not None:
snake_case : Union[str, Any] = WavLMConfig.from_pretrained(A_ )
else:
snake_case : Any = WavLMConfig()
snake_case : Optional[int] = WavLMModel(A_ )
recursively_load_weights(A_ , A_ )
hf_wavlm.save_pretrained(A_ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
UpperCAmelCase = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 555 | 0 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase__ : List[Any] = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase__ : Optional[int] = importlib.util.spec_from_file_location(
"transformers",
os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCamelCase__ : Optional[int] = spec.loader.load_module()
UpperCamelCase__ : Optional[Any] = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCamelCase__ : Union[str, Any] = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
UpperCamelCase__ : Optional[Any] = {
"CLIPConfigMixin",
"DecisionTransformerConfigMixin",
"EncoderDecoderConfigMixin",
"RagConfigMixin",
"SpeechEncoderDecoderConfigMixin",
"VisionEncoderDecoderConfigMixin",
"VisionTextDualEncoderConfigMixin",
}
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for config_class in list(CONFIG_MAPPING.values() ):
_SCREAMING_SNAKE_CASE = False
# source code of `config_class`
_SCREAMING_SNAKE_CASE = inspect.getsource(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = _re_checkpoint.findall(SCREAMING_SNAKE_CASE_ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
_SCREAMING_SNAKE_CASE = F"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
_SCREAMING_SNAKE_CASE = True
break
_SCREAMING_SNAKE_CASE = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
_SCREAMING_SNAKE_CASE = """\n""".join(sorted(SCREAMING_SNAKE_CASE_ ) )
raise ValueError(F"The following configurations don't contain any valid checkpoint:\n{message}" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 591 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
# initialize config
if "resnet-50" in model_name:
_SCREAMING_SNAKE_CASE = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
_SCREAMING_SNAKE_CASE = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
_SCREAMING_SNAKE_CASE = DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE_ , backbone_config=SCREAMING_SNAKE_CASE_ )
# set label attributes
_SCREAMING_SNAKE_CASE = """panoptic""" in model_name
if is_panoptic:
_SCREAMING_SNAKE_CASE = 2_50
else:
_SCREAMING_SNAKE_CASE = 91
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = """coco-detection-id2label.json"""
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
F"encoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
F"decoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append(
(
F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
F"decoder.layers.{i}.encoder_attn.out_proj.weight",
) )
rename_keys.append(
(
F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
F"decoder.layers.{i}.encoder_attn.out_proj.bias",
) )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = state_dict.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
if is_panoptic:
_SCREAMING_SNAKE_CASE = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight[:2_56, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[:2_56]
_SCREAMING_SNAKE_CASE = in_proj_weight[2_56:5_12, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[2_56:5_12]
_SCREAMING_SNAKE_CASE = in_proj_weight[-2_56:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight[:2_56, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[:2_56]
_SCREAMING_SNAKE_CASE = in_proj_weight[2_56:5_12, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[2_56:5_12]
_SCREAMING_SNAKE_CASE = in_proj_weight[-2_56:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
_SCREAMING_SNAKE_CASE = state_dict.pop(
F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[:2_56, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[:2_56]
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[2_56:5_12, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[2_56:5_12]
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[-2_56:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[-2_56:]
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_SCREAMING_SNAKE_CASE = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = get_detr_config(SCREAMING_SNAKE_CASE_ )
# load original model from torch hub
_SCREAMING_SNAKE_CASE = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F"Converting model {model_name}..." )
_SCREAMING_SNAKE_CASE = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=SCREAMING_SNAKE_CASE_ ).eval()
_SCREAMING_SNAKE_CASE = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE_ ):
if is_panoptic:
_SCREAMING_SNAKE_CASE = """detr.""" + src
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# query, key and value matrices need special treatment
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , is_panoptic=SCREAMING_SNAKE_CASE_ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_SCREAMING_SNAKE_CASE = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
_SCREAMING_SNAKE_CASE = state_dict.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_SCREAMING_SNAKE_CASE = state_dict.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
_SCREAMING_SNAKE_CASE = state_dict.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
_SCREAMING_SNAKE_CASE = state_dict.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
# finally, create HuggingFace model and load state dict
_SCREAMING_SNAKE_CASE = DetrForSegmentation(SCREAMING_SNAKE_CASE_ ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
model.eval()
# verify our conversion on an image
_SCREAMING_SNAKE_CASE = """coco_panoptic""" if is_panoptic else """coco_detection"""
_SCREAMING_SNAKE_CASE = DetrImageProcessor(format=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(images=prepare_img() , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoding["""pixel_values"""]
_SCREAMING_SNAKE_CASE = detr(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = model(SCREAMING_SNAKE_CASE_ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F"nielsr/{model_name}" )
processor.push_to_hub(F"nielsr/{model_name}" )
if __name__ == "__main__":
UpperCamelCase__ : Any = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
UpperCamelCase__ : Any = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 591 | 1 |
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase : Any = mf_knapsack(i - 1 , __magic_name__ , __magic_name__ , __magic_name__ )
else:
lowercase : List[Any] = max(
mf_knapsack(i - 1 , __magic_name__ , __magic_name__ , __magic_name__ ) , mf_knapsack(i - 1 , __magic_name__ , __magic_name__ , j - wt[i - 1] ) + val[i - 1] , )
lowercase : List[str] = val
return f[i][j]
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
lowercase : Optional[Any] = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
lowercase : Optional[Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
lowercase : List[str] = dp[i - 1][w_]
return dp[n][w_], dp
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
if not (isinstance(__magic_name__ , (list, tuple) ) and isinstance(__magic_name__ , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
lowercase : str = len(__magic_name__ )
if num_items != len(__magic_name__ ):
lowercase : int = (
'''The number of weights must be the same as the number of values.\n'''
F"""But got {num_items} weights and {len(__magic_name__ )} values"""
)
raise ValueError(__magic_name__ )
for i in range(__magic_name__ ):
if not isinstance(wt[i] , __magic_name__ ):
lowercase : List[str] = (
'''All weights must be integers but got weight of '''
F"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(__magic_name__ )
lowercase : Any = knapsack(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
lowercase : set = set()
_construct_solution(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
return optimal_val, example_optional_set
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(__magic_name__ , __magic_name__ , i - 1 , __magic_name__ , __magic_name__ )
else:
optimal_set.add(__magic_name__ )
_construct_solution(__magic_name__ , __magic_name__ , i - 1 , j - wt[i - 1] , __magic_name__ )
if __name__ == "__main__":
lowerCAmelCase_ = [3, 2, 4, 4]
lowerCAmelCase_ = [4, 3, 2, 3]
lowerCAmelCase_ = 4
lowerCAmelCase_ = 6
lowerCAmelCase_ = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
lowerCAmelCase_ , lowerCAmelCase_ = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
lowerCAmelCase_ , lowerCAmelCase_ = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('optimal_value = ', optimal_solution)
print('An optimal subset corresponding to the optimal value', optimal_subset) | 721 |
def snake_case( __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def snake_case( __magic_name__ ) -> list[tuple[int, int]]:
'''simple docstring'''
lowercase : Optional[Any] = 0
lowercase : Tuple = len(__magic_name__ ) # No of vertices in graph
lowercase : int = [0] * n
lowercase : int = [False] * n
def dfs(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase : List[str] = True
lowercase : str = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(__magic_name__ , __magic_name__ , __magic_name__ , id_ )
lowercase : Tuple = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
lowercase : List[Any] = min(low[at] , low[to] )
lowercase : list[tuple[int, int]] = []
for i in range(__magic_name__ ):
if not visited[i]:
dfs(__magic_name__ , -1 , __magic_name__ , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod() | 596 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase : Any = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict = [
"MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegaForCausalLM",
"MegaForMaskedLM",
"MegaForMultipleChoice",
"MegaForQuestionAnswering",
"MegaForSequenceClassification",
"MegaForTokenClassification",
"MegaModel",
"MegaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 457 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = logging.get_logger()
# the current default level is logging.WARNING
lowerCamelCase = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(A )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = logging.get_verbosity()
lowerCamelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
lowerCamelCase = """Testing 1, 2, 3"""
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , msg + """\n""" )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , """""" )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , msg + """\n""" )
# restore to the original level
logging.set_verbosity(A )
@mockenv(TRANSFORMERS_VERBOSITY="""error""" )
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
lowerCamelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
lowerCamelCase = os.getenv("""TRANSFORMERS_VERBOSITY""" , A )
lowerCamelCase = logging.log_levels[env_level_str]
lowerCamelCase = logging.get_verbosity()
self.assertEqual(
A , A , F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}' , )
# restore to the original level
lowerCamelCase = """"""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY="""super-error""" )
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
lowerCamelCase = logging.logging.getLogger()
with CaptureLogger(A ) as cl:
# this action activates the env var
logging.get_logger("""transformers.models.bart.tokenization_bart""" )
self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error""" , cl.out )
# no need to restore as nothing was changed
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
lowerCamelCase = logging.get_logger("""transformers.models.bart.tokenization_bart""" )
lowerCamelCase = """Testing 1, 2, 3"""
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1""" ):
# nothing should be logged as env var disables this method
with CaptureLogger(A ) as cl:
logger.warning_advice(A )
self.assertEqual(cl.out , """""" )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""""" ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(A ) as cl:
logger.warning_advice(A )
self.assertEqual(cl.out , msg + """\n""" )
def __lowerCamelCase ( ):
'''simple docstring'''
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 457 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class a ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Tuple = """dinat"""
UpperCamelCase_ : int = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Optional[Any] , lowerCamelCase__ : int=4 , lowerCamelCase__ : List[Any]=3 , lowerCamelCase__ : str=64 , lowerCamelCase__ : List[str]=[3, 4, 6, 5] , lowerCamelCase__ : List[Any]=[2, 4, 8, 16] , lowerCamelCase__ : Union[str, Any]=7 , lowerCamelCase__ : Optional[Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , lowerCamelCase__ : int=3.0 , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Optional[int]=0.0 , lowerCamelCase__ : Dict=0.0 , lowerCamelCase__ : Dict=0.1 , lowerCamelCase__ : List[str]="gelu" , lowerCamelCase__ : Tuple=0.0_2 , lowerCamelCase__ : Union[str, Any]=1e-5 , lowerCamelCase__ : Union[str, Any]=0.0 , lowerCamelCase__ : Optional[Any]=None , lowerCamelCase__ : Tuple=None , **lowerCamelCase__ : List[Any] , ) -> Any:
"""simple docstring"""
super().__init__(**lowerCamelCase__ )
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(lowerCamelCase__ )
__lowercase = num_heads
__lowercase = kernel_size
__lowercase = dilations
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(lowerCamelCase__ ) - 1) )
__lowercase = layer_scale_init_value
__lowercase = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(lowerCamelCase__ ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=lowerCamelCase__ , out_indices=lowerCamelCase__ , stage_names=self.stage_names )
| 716 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def UpperCAmelCase_ ( self : List[str] , lowerCamelCase__ : float ) -> float:
"""simple docstring"""
return 0.0
def _A( UpperCamelCase__ : np.ndarray , UpperCamelCase__ : int ) -> tuple[int | float, int | float]:
'''simple docstring'''
__lowercase = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
__lowercase = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _A( UpperCamelCase__ : FilterType , UpperCamelCase__ : int ) -> None:
'''simple docstring'''
__lowercase = 512
__lowercase = [1] + [0] * (size - 1)
__lowercase = [filter_type.process(UpperCamelCase__ ) for item in inputs]
__lowercase = [0] * (samplerate - size) # zero-padding
outputs += filler
__lowercase = np.abs(np.fft.fft(UpperCamelCase__ ) )
__lowercase = 20 * np.logaa(UpperCamelCase__ )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
# Display within reasonable bounds
__lowercase = get_bounds(UpperCamelCase__ , UpperCamelCase__ )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel('''Gain (dB)''' )
plt.plot(UpperCamelCase__ )
plt.show()
def _A( UpperCamelCase__ : FilterType , UpperCamelCase__ : int ) -> None:
'''simple docstring'''
__lowercase = 512
__lowercase = [1] + [0] * (size - 1)
__lowercase = [filter_type.process(UpperCamelCase__ ) for item in inputs]
__lowercase = [0] * (samplerate - size) # zero-padding
outputs += filler
__lowercase = np.angle(np.fft.fft(UpperCamelCase__ ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel('''Frequency (Hz)''' )
plt.xscale('''log''' )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel('''Phase shift (Radians)''' )
plt.plot(np.unwrap(UpperCamelCase__ , -2 * pi ) )
plt.show()
| 362 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowercase__ ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=30 , SCREAMING_SNAKE_CASE=400 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=1 / 255 , SCREAMING_SNAKE_CASE=True , ) -> Dict:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_lowerCamelCase : Union[str, Any] = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
_lowerCamelCase : Union[str, Any] = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : Optional[int] = num_channels
_lowerCamelCase : Union[str, Any] = min_resolution
_lowerCamelCase : Optional[int] = max_resolution
_lowerCamelCase : List[Any] = do_resize
_lowerCamelCase : str = size
_lowerCamelCase : Union[str, Any] = do_normalize
_lowerCamelCase : Union[str, Any] = image_mean
_lowerCamelCase : Tuple = image_std
_lowerCamelCase : List[Any] = do_rescale
_lowerCamelCase : Dict = rescale_factor
_lowerCamelCase : Any = do_pad
def UpperCamelCase_ ( self) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False) -> Any:
if not batched:
_lowerCamelCase : Tuple = image_inputs[0]
if isinstance(SCREAMING_SNAKE_CASE , Image.Image):
_lowerCamelCase , _lowerCamelCase : Any = image.size
else:
_lowerCamelCase , _lowerCamelCase : int = image.shape[1], image.shape[2]
if w < h:
_lowerCamelCase : Optional[int] = int(self.size["""shortest_edge"""] * h / w)
_lowerCamelCase : Optional[int] = self.size["""shortest_edge"""]
elif w > h:
_lowerCamelCase : Any = self.size["""shortest_edge"""]
_lowerCamelCase : Union[str, Any] = int(self.size["""shortest_edge"""] * w / h)
else:
_lowerCamelCase : Optional[Any] = self.size["""shortest_edge"""]
_lowerCamelCase : Optional[Any] = self.size["""shortest_edge"""]
else:
_lowerCamelCase : Any = []
for image in image_inputs:
_lowerCamelCase , _lowerCamelCase : List[str] = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
_lowerCamelCase : List[Any] = max(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE: item[0])[0]
_lowerCamelCase : int = max(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase__ ( A_ ,unittest.TestCase ):
__UpperCAmelCase = ConditionalDetrImageProcessor if is_vision_available() else None
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Dict = ConditionalDetrImageProcessingTester(self)
@property
def UpperCamelCase_ ( self) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self) -> Dict:
_lowerCamelCase : Any = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , """image_mean"""))
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , """image_std"""))
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , """do_normalize"""))
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , """do_resize"""))
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE , """size"""))
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : int = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333})
self.assertEqual(image_processor.do_pad , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE)
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84})
self.assertEqual(image_processor.do_pad , SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Tuple:
pass
def UpperCamelCase_ ( self) -> Any:
# Initialize image_processing
_lowerCamelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_lowerCamelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE)
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , Image.Image)
# Test not batched input
_lowerCamelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
_lowerCamelCase , _lowerCamelCase : Optional[Any] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[Any] = image_processing(SCREAMING_SNAKE_CASE , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase_ ( self) -> int:
# Initialize image_processing
_lowerCamelCase : Dict = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_lowerCamelCase : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE , numpify=SCREAMING_SNAKE_CASE)
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , np.ndarray)
# Test not batched input
_lowerCamelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
_lowerCamelCase , _lowerCamelCase : int = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCamelCase : int = image_processing(SCREAMING_SNAKE_CASE , return_tensors="""pt""").pixel_values
_lowerCamelCase , _lowerCamelCase : Any = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase_ ( self) -> Optional[Any]:
# Initialize image_processing
_lowerCamelCase : Tuple = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_lowerCamelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE , torchify=SCREAMING_SNAKE_CASE)
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor)
# Test not batched input
_lowerCamelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
_lowerCamelCase , _lowerCamelCase : Optional[int] = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_lowerCamelCase : Optional[Any] = image_processing(SCREAMING_SNAKE_CASE , return_tensors="""pt""").pixel_values
_lowerCamelCase , _lowerCamelCase : Tuple = self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCamelCase_ ( self) -> str:
# prepare image and target
_lowerCamelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""") as f:
_lowerCamelCase : List[Any] = json.loads(f.read())
_lowerCamelCase : Union[str, Any] = {"""image_id""": 3_9769, """annotations""": target}
# encode them
_lowerCamelCase : Any = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""")
_lowerCamelCase : Optional[Any] = image_processing(images=SCREAMING_SNAKE_CASE , annotations=SCREAMING_SNAKE_CASE , return_tensors="""pt""")
# verify pixel values
_lowerCamelCase : Dict = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["""pixel_values"""].shape , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Tuple = torch.tensor([0.27_96, 0.31_38, 0.34_81])
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4))
# verify area
_lowerCamelCase : List[str] = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , SCREAMING_SNAKE_CASE))
# verify boxes
_lowerCamelCase : Union[str, Any] = torch.Size([6, 4])
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , SCREAMING_SNAKE_CASE , atol=1e-3))
# verify image_id
_lowerCamelCase : Union[str, Any] = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , SCREAMING_SNAKE_CASE))
# verify is_crowd
_lowerCamelCase : Any = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , SCREAMING_SNAKE_CASE))
# verify class_labels
_lowerCamelCase : Any = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , SCREAMING_SNAKE_CASE))
# verify orig_size
_lowerCamelCase : List[str] = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , SCREAMING_SNAKE_CASE))
# verify size
_lowerCamelCase : Tuple = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , SCREAMING_SNAKE_CASE))
@slow
def UpperCamelCase_ ( self) -> Optional[Any]:
# prepare image, target and masks_path
_lowerCamelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""") as f:
_lowerCamelCase : Optional[int] = json.loads(f.read())
_lowerCamelCase : Optional[Any] = {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}
_lowerCamelCase : List[str] = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""")
# encode them
_lowerCamelCase : List[str] = ConditionalDetrImageProcessor(format="""coco_panoptic""")
_lowerCamelCase : Dict = image_processing(images=SCREAMING_SNAKE_CASE , annotations=SCREAMING_SNAKE_CASE , masks_path=SCREAMING_SNAKE_CASE , return_tensors="""pt""")
# verify pixel values
_lowerCamelCase : int = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["""pixel_values"""].shape , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = torch.tensor([0.27_96, 0.31_38, 0.34_81])
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4))
# verify area
_lowerCamelCase : Union[str, Any] = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , SCREAMING_SNAKE_CASE))
# verify boxes
_lowerCamelCase : int = torch.Size([6, 4])
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , SCREAMING_SNAKE_CASE , atol=1e-3))
# verify image_id
_lowerCamelCase : List[str] = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , SCREAMING_SNAKE_CASE))
# verify is_crowd
_lowerCamelCase : Dict = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , SCREAMING_SNAKE_CASE))
# verify class_labels
_lowerCamelCase : str = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , SCREAMING_SNAKE_CASE))
# verify masks
_lowerCamelCase : Any = 82_2873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , SCREAMING_SNAKE_CASE)
# verify orig_size
_lowerCamelCase : List[Any] = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , SCREAMING_SNAKE_CASE))
# verify size
_lowerCamelCase : Optional[Any] = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , SCREAMING_SNAKE_CASE))
| 88 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : int ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = AlbertConfig.from_json_file(__lowercase )
print(f'Building PyTorch model from configuration: {config}' )
_UpperCAmelCase = AlbertForPreTraining(__lowercase )
# Load weights from tf checkpoint
load_tf_weights_in_albert(__lowercase , __lowercase , __lowercase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , __lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--albert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained ALBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 236 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'),
('gamma', 'weight'),
('pegasus', 'model'),
]
__SCREAMING_SNAKE_CASE : int = [
('.output.dense', '.fc2'),
('intermediate.LayerNorm', 'final_layer_norm'),
('intermediate.dense', 'fc1'),
]
__SCREAMING_SNAKE_CASE : str = (
INIT_COMMON
+ [
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.out_proj'),
('attention.self', 'self_attn'),
('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
('attention.encdec_output.dense', 'encoder_attn.out_proj'),
('attention.encdec', 'encoder_attn'),
('key', 'k_proj'),
('value', 'v_proj'),
('query', 'q_proj'),
('decoder.LayerNorm', 'decoder.layernorm_embedding'),
]
+ END_COMMON
)
__SCREAMING_SNAKE_CASE : Optional[Any] = (
INIT_COMMON
+ [
('embeddings.word_embeddings', 'shared.weight'),
('embeddings.position_embeddings', 'embed_positions.weight'),
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.output'),
('attention.self', 'self_attn.self'),
('encoder.LayerNorm', 'encoder.layernorm_embedding'),
]
+ END_COMMON
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
'encdec/key/bias',
'encdec/query/bias',
'encdec/value/bias',
'self/key/bias',
'self/query/bias',
'self/value/bias',
'encdec_output/dense/bias',
'attention/output/dense/bias',
]
def snake_case (__lowercase , __lowercase ) -> Dict:
'''simple docstring'''
for tf_name, hf_name in patterns:
_snake_case : Union[str, Any] = k.replace(__lowercase , __lowercase )
return k
def snake_case (__lowercase , __lowercase ) -> BigBirdPegasusForConditionalGeneration:
'''simple docstring'''
_snake_case : List[str] = BigBirdPegasusConfig(**__lowercase )
_snake_case : Union[str, Any] = BigBirdPegasusForConditionalGeneration(__lowercase )
_snake_case : int = torch_model.state_dict()
_snake_case : List[str] = {}
# separating decoder weights
_snake_case : List[str] = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
_snake_case : List[Any] = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder" )}
for k, v in tqdm(decoder_weights.items() , "tf -> hf conversion" ):
_snake_case : Optional[Any] = [k.endswith(__lowercase ) for ending in KEYS_TO_IGNORE]
if any(__lowercase ):
continue
_snake_case : int = DECODER_PATTERNS
_snake_case : Optional[Any] = rename_state_dict_key(__lowercase , __lowercase )
if new_k not in state_dict:
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
_snake_case : int = v.T
_snake_case : Tuple = torch.from_numpy(__lowercase )
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
for k, v in tqdm(remaining_weights.items() , "tf -> hf conversion" ):
_snake_case : Tuple = [k.endswith(__lowercase ) for ending in KEYS_TO_IGNORE]
if any(__lowercase ):
continue
_snake_case : Tuple = REMAINING_PATTERNS
_snake_case : Dict = rename_state_dict_key(__lowercase , __lowercase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
_snake_case : Tuple = v.T
_snake_case : Any = torch.from_numpy(__lowercase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""
_snake_case : str = mapping["model.embed_positions.weight"]
_snake_case : Any = mapping.pop("model.embed_positions.weight" )
_snake_case ,_snake_case : str = torch_model.load_state_dict(__lowercase , strict=__lowercase )
_snake_case : List[str] = [
k
for k in missing
if k
not in [
"final_logits_bias",
"model.encoder.embed_tokens.weight",
"model.decoder.embed_tokens.weight",
"lm_head.weight",
]
]
assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], F"""no matches found for the following tf keys {extra}"""
return torch_model
def snake_case (__lowercase ) -> Dict:
'''simple docstring'''
_snake_case : Optional[Any] = tf.train.list_variables(__lowercase )
_snake_case : str = {}
_snake_case : int = ["global_step"]
for name, shape in tqdm(__lowercase , desc="converting tf checkpoint to dict" ):
_snake_case : List[str] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_snake_case : Tuple = tf.train.load_variable(__lowercase , __lowercase )
_snake_case : Optional[int] = array
return tf_weights
def snake_case (__lowercase , __lowercase , __lowercase ) -> int:
'''simple docstring'''
_snake_case : Tuple = get_tf_weights_as_numpy(__lowercase )
_snake_case : Optional[Any] = convert_bigbird_pegasus(__lowercase , __lowercase )
torch_model.save_pretrained(__lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
__SCREAMING_SNAKE_CASE : List[str] = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update) | 580 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def snake_case (__lowercase ) -> List[Any]:
'''simple docstring'''
_snake_case : int = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class lowercase_ ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
_lowerCamelCase = StableDiffusionLatentUpscalePipeline
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'height',
'width',
'cross_attention_kwargs',
'negative_prompt_embeds',
'prompt_embeds',
}
_lowerCamelCase = PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowerCamelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_lowerCamelCase = frozenset([] )
_lowerCamelCase = True
@property
def UpperCamelCase ( self ):
_snake_case : Optional[int] = 1
_snake_case : Any = 4
_snake_case : int = (16, 16)
_snake_case : Optional[int] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowercase_ )
return image
def UpperCamelCase ( self ):
torch.manual_seed(0 )
_snake_case : Tuple = UNetaDConditionModel(
act_fn="gelu" , attention_head_dim=8 , norm_num_groups=lowercase_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"KDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
) , in_channels=8 , mid_block_type=lowercase_ , only_cross_attention=lowercase_ , out_channels=5 , resnet_time_scale_shift="scale_shift" , time_embedding_type="fourier" , timestep_post_act="gelu" , up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D") , )
_snake_case : Any = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
_snake_case : Union[str, Any] = EulerDiscreteScheduler(prediction_type="sample" )
_snake_case : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="quick_gelu" , projection_dim=512 , )
_snake_case : Optional[int] = CLIPTextModel(lowercase_ )
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_snake_case : int = {
"unet": model.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def UpperCamelCase ( self , lowercase_ , lowercase_=0 ):
if str(lowercase_ ).startswith("mps" ):
_snake_case : Dict = torch.manual_seed(lowercase_ )
else:
_snake_case : Tuple = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
_snake_case : Optional[int] = {
"prompt": "A painting of a squirrel eating a burger",
"image": self.dummy_image.cpu(),
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def UpperCamelCase ( self ):
_snake_case : Tuple = "cpu"
_snake_case : List[Any] = self.get_dummy_components()
_snake_case : List[str] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
_snake_case : Union[str, Any] = self.get_dummy_inputs(lowercase_ )
_snake_case : Dict = pipe(**lowercase_ ).images
_snake_case : Dict = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
_snake_case : Union[str, Any] = np.array(
[0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055] )
_snake_case : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1e-3 )
def UpperCamelCase ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 )
def UpperCamelCase ( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 )
def UpperCamelCase ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def UpperCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=7e-3 )
def UpperCamelCase ( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 )
def UpperCamelCase ( self ):
super().test_save_load_local(expected_max_difference=3e-3 )
def UpperCamelCase ( self ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def UpperCamelCase ( self ):
_snake_case : Dict = [
"DDIMScheduler",
"DDPMScheduler",
"PNDMScheduler",
"HeunDiscreteScheduler",
"EulerAncestralDiscreteScheduler",
"KDPM2DiscreteScheduler",
"KDPM2AncestralDiscreteScheduler",
"DPMSolverSDEScheduler",
]
_snake_case : Dict = self.get_dummy_components()
_snake_case : Optional[Any] = self.pipeline_class(**lowercase_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
_snake_case : List[Any] = self.get_dummy_inputs(lowercase_ )
_snake_case : int = 2
_snake_case : List[str] = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
_snake_case : Union[str, Any] = getattr(lowercase_ , scheduler_enum.name )
_snake_case : Dict = scheduler_cls.from_config(pipe.scheduler.config )
_snake_case : Optional[int] = pipe(**lowercase_ )[0]
outputs.append(lowercase_ )
assert check_same_shape(lowercase_ )
@require_torch_gpu
@slow
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ):
_snake_case : Any = torch.manual_seed(33 )
_snake_case : Optional[Any] = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" , torch_dtype=torch.floataa )
pipe.to("cuda" )
_snake_case : int = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
_snake_case : int = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
_snake_case : Union[str, Any] = pipe(lowercase_ , generator=lowercase_ , output_type="latent" ).images
_snake_case : Dict = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="np" , ).images[0]
_snake_case : Optional[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" )
assert np.abs((expected_image - image).mean() ) < 5e-2
def UpperCamelCase ( self ):
_snake_case : Dict = torch.manual_seed(33 )
_snake_case : Union[str, Any] = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
_snake_case : Any = "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"
_snake_case : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" )
_snake_case : Any = upscaler(
prompt=lowercase_ , image=lowercase_ , num_inference_steps=20 , guidance_scale=0 , generator=lowercase_ , output_type="np" , ).images[0]
_snake_case : List[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" )
assert np.abs((expected_image - image).max() ) < 5e-2 | 580 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def a_ ( UpperCamelCase_ : int ) -> str:
"""simple docstring"""
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
lowerCamelCase = precision
lowerCamelCase = ceil(precision / 1_4 )
lowerCamelCase = 4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
lowerCamelCase = 1
lowerCamelCase = 1_3_5_9_1_4_0_9
lowerCamelCase = Decimal(UpperCamelCase_ )
for k in range(1 , UpperCamelCase_ ):
lowerCamelCase = factorial(6 * k ) // (factorial(3 * k ) * factorial(UpperCamelCase_ ) ** 3)
linear_term += 5_4_5_1_4_0_1_3_4
exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = 5_0
print(F'''The first {n} digits of pi is: {pi(n)}''')
| 246 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCAmelCase : Dict = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
_lowerCAmelCase : List[Any] = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
_lowerCAmelCase : Dict = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
_lowerCAmelCase : List[Any] = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
_lowerCAmelCase : List[Any] = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 1_4]),
('2H 5D 3C AS 5S', False, [1_4, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
_lowerCAmelCase : List[str] = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
_lowerCAmelCase : Optional[Any] = (
('JH AH TH KH QH', 2_3),
('JH 9H TH KH QH', 2_2),
('JC KH JS JD JH', 2_1),
('KH KC 3S 3H 3D', 2_0),
('8C 9C 5C 3C TC', 1_9),
('JS QS 9H TS KH', 1_8),
('7C 7S KH 2H 7H', 1_7),
('3C KH 5D 5S KH', 1_6),
('QH 8H KD JH 8S', 1_5),
('2D 6D 9D TH 7D', 1_4),
)
def a_ ( ) -> int:
"""simple docstring"""
lowerCamelCase , lowerCamelCase = randrange(len(UpperCamelCase_ ) ), randrange(len(UpperCamelCase_ ) )
lowerCamelCase = ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
lowerCamelCase , lowerCamelCase = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def a_ ( UpperCamelCase_ : int = 1_0_0 ) -> int:
"""simple docstring"""
return (generate_random_hand() for _ in range(UpperCamelCase_ ))
@pytest.mark.parametrize('hand, expected' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int] ) -> Dict:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ )._is_flush() == expected
@pytest.mark.parametrize('hand, expected' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[Any] ) -> List[Any]:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ )._is_straight() == expected
@pytest.mark.parametrize('hand, expected, card_values' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : Tuple , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Tuple ) -> Tuple:
"""simple docstring"""
lowerCamelCase = PokerHand(UpperCamelCase_ )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('hand, expected' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : Any , UpperCamelCase_ : str ) -> str:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ )._is_same_kind() == expected
@pytest.mark.parametrize('hand, expected' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : Any , UpperCamelCase_ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ )._hand_type == expected
@pytest.mark.parametrize('hand, other, expected' , UpperCamelCase_ )
def a_ ( UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ ).compare_with(PokerHand(UpperCamelCase_ ) ) == expected
@pytest.mark.parametrize('hand, other, expected' , generate_random_hands() )
def a_ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(UpperCamelCase_ ).compare_with(PokerHand(UpperCamelCase_ ) ) == expected
def a_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase = [PokerHand(UpperCamelCase_ ) for hand in SORTED_HANDS]
lowerCamelCase = poker_hands.copy()
shuffle(UpperCamelCase_ )
lowerCamelCase = chain(sorted(UpperCamelCase_ ) )
for index, hand in enumerate(UpperCamelCase_ ):
assert hand == poker_hands[index]
def a_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase = [PokerHand('2D AC 3H 4H 5S' ), PokerHand('2S 3H 4H 5S 6C' )]
pokerhands.sort(reverse=UpperCamelCase_ )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def a_ ( ) -> int:
"""simple docstring"""
lowerCamelCase = PokerHand('2C 4S AS 3D 5C' )
lowerCamelCase = True
lowerCamelCase = [5, 4, 3, 2, 1_4]
for _ in range(1_0 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def a_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase = 0
lowerCamelCase = os.path.abspath(os.path.dirname(UpperCamelCase_ ) )
lowerCamelCase = os.path.join(UpperCamelCase_ , 'poker_hands.txt' )
with open(UpperCamelCase_ ) as file_hand:
for line in file_hand:
lowerCamelCase = line[:1_4].strip()
lowerCamelCase = line[1_5:].strip()
lowerCamelCase , lowerCamelCase = PokerHand(UpperCamelCase_ ), PokerHand(UpperCamelCase_ )
lowerCamelCase = player.compare_with(UpperCamelCase_ )
if output == "Win":
answer += 1
assert answer == 3_7_6
| 246 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Union[str, Any] = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 570 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self : List[str] ):
UpperCAmelCase = torch.nn.Linear(10 , 10 )
UpperCAmelCase = torch.optim.SGD(model.parameters() , 0.1 )
UpperCAmelCase = Accelerator()
UpperCAmelCase = accelerator.prepare(a__ )
try:
pickle.loads(pickle.dumps(a__ ) )
except Exception as e:
self.fail(f"Accelerated optimizer pickling failed with {e}" )
AcceleratorState._reset_state()
| 570 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class __UpperCamelCase ( unittest.TestCase ):
def __init__( self : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : Any=2 , UpperCAmelCase : Any=56 , UpperCAmelCase : Dict=True , UpperCAmelCase : str=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : Tuple=99 , UpperCAmelCase : int=32 , UpperCAmelCase : Any=2 , UpperCAmelCase : Dict=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : Optional[Any]="gelu_new" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : List[str]=512 , UpperCAmelCase : List[Any]=16 , UpperCAmelCase : List[str]=2 , UpperCAmelCase : List[Any]=0.0_2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]="block_sparse" , UpperCAmelCase : Any=True , UpperCAmelCase : Union[str, Any]=False , UpperCAmelCase : List[Any]=2 , UpperCAmelCase : Optional[Any]=3 , ) -> int:
lowerCAmelCase :Optional[int] = parent
lowerCAmelCase :Dict = batch_size
lowerCAmelCase :Any = seq_length
lowerCAmelCase :Optional[int] = is_training
lowerCAmelCase :List[str] = use_attention_mask
lowerCAmelCase :Dict = use_token_type_ids
lowerCAmelCase :int = use_labels
lowerCAmelCase :Optional[int] = vocab_size
lowerCAmelCase :Any = hidden_size
lowerCAmelCase :Dict = num_hidden_layers
lowerCAmelCase :List[Any] = num_attention_heads
lowerCAmelCase :Optional[Any] = intermediate_size
lowerCAmelCase :int = hidden_act
lowerCAmelCase :Optional[Any] = hidden_dropout_prob
lowerCAmelCase :Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase :int = max_position_embeddings
lowerCAmelCase :Optional[Any] = type_vocab_size
lowerCAmelCase :Any = type_sequence_label_size
lowerCAmelCase :Tuple = initializer_range
lowerCAmelCase :Optional[int] = num_choices
lowerCAmelCase :Optional[int] = rescale_embeddings
lowerCAmelCase :Any = attention_type
lowerCAmelCase :str = use_bias
lowerCAmelCase :Any = block_size
lowerCAmelCase :str = num_random_blocks
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
lowerCAmelCase :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase :Tuple = None
if self.use_attention_mask:
lowerCAmelCase :List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase :Optional[int] = None
if self.use_token_type_ids:
lowerCAmelCase :str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase :Dict = BigBirdConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , attention_type=self.attention_type , block_size=self.block_size , num_random_blocks=self.num_random_blocks , use_bias=self.use_bias , rescale_embeddings=self.rescale_embeddings , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
lowerCAmelCase :Optional[int] = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :List[str] = config_and_inputs
lowerCAmelCase :Any = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'attention_mask': attention_mask,
}
return config, inputs_dict
@require_flax
class __UpperCamelCase ( UpperCamelCase , unittest.TestCase ):
lowercase_ : List[Any] = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
lowercase_ : Optional[int] = False
lowercase_ : Tuple = False
def UpperCAmelCase__ ( self : List[Any] ) -> Optional[int]:
lowerCAmelCase :List[Any] = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase__ ( self : Any ) -> Tuple:
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase__ ( self : str ) -> Tuple:
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase__ ( self : Union[str, Any] ) -> str:
super().test_hidden_states_output()
@slow
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
lowerCAmelCase :Dict = model_class_name.from_pretrained('google/bigbird-roberta-base' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def UpperCAmelCase__ ( self : Optional[int] ) -> Any:
lowerCAmelCase , lowerCAmelCase :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase :int = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
lowerCAmelCase :Tuple = model_class(UpperCAmelCase )
@jax.jit
def model_jitted(UpperCAmelCase : str , UpperCAmelCase : Any=None , **UpperCAmelCase : Optional[int] ):
return model(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , **UpperCAmelCase )
with self.subTest('JIT Enabled' ):
lowerCAmelCase :Optional[int] = model_jitted(**UpperCAmelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
lowerCAmelCase :int = model_jitted(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for jitted_output, output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase__ ( self : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str=1e-5 , UpperCAmelCase : Tuple="outputs" , UpperCAmelCase : Any=None ) -> Optional[int]:
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith('outputs.attentions' ):
return
else:
super().check_pt_flax_outputs(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) | 553 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def UpperCAmelCase ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def UpperCAmelCase ( a__ , a__ , a__ , a__ , a__=True ):
'''simple docstring'''
model.train()
lowerCAmelCase :Dict = model(a__ )
lowerCAmelCase :Union[str, Any] = F.mse_loss(a__ , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(a__ )
def UpperCAmelCase ( a__ , a__=False ):
'''simple docstring'''
set_seed(42 )
lowerCAmelCase :Any = RegressionModel()
lowerCAmelCase :List[str] = deepcopy(a__ )
lowerCAmelCase :str = RegressionDataset(length=80 )
lowerCAmelCase :Optional[Any] = DataLoader(a__ , batch_size=16 )
model.to(accelerator.device )
if sched:
lowerCAmelCase :List[Any] = AdamW(params=model.parameters() , lr=1e-3 )
lowerCAmelCase :Dict = AdamW(params=ddp_model.parameters() , lr=1e-3 )
lowerCAmelCase :Tuple = LambdaLR(a__ , lr_lambda=lambda a__ : epoch**0.65 )
lowerCAmelCase :Optional[Any] = LambdaLR(a__ , lr_lambda=lambda a__ : epoch**0.65 )
# Make a copy of `model`
if sched:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :int = accelerator.prepare(a__ , a__ , a__ , a__ )
else:
lowerCAmelCase , lowerCAmelCase :Optional[int] = accelerator.prepare(a__ , a__ )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def UpperCAmelCase ( a__ ):
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :Any = get_training_setup(a__ )
# Use a single batch
lowerCAmelCase , lowerCAmelCase :Any = next(iter(a__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCAmelCase , lowerCAmelCase :Optional[Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase , lowerCAmelCase :Optional[Any] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__ , a__ , a__ , a__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(a__ ):
step_model(a__ , a__ , a__ , a__ )
else:
# Sync grads
step_model(a__ , a__ , a__ , a__ )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(a__ , a__ , a__ , a__ )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowerCAmelCase :Any = ddp_input[torch.randperm(len(a__ ) )]
def UpperCAmelCase ( a__ ):
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :Dict = get_training_setup(a__ )
# Use a single batch
lowerCAmelCase , lowerCAmelCase :str = next(iter(a__ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
lowerCAmelCase , lowerCAmelCase :List[Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase , lowerCAmelCase :int = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__ , a__ , a__ , a__ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(a__ ):
step_model(a__ , a__ , a__ , a__ )
else:
# Sync grads
step_model(a__ , a__ , a__ , a__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowerCAmelCase :str = ddp_input[torch.randperm(len(a__ ) )]
def UpperCAmelCase ( a__=False , a__=False ):
'''simple docstring'''
lowerCAmelCase :str = Accelerator(
split_batches=a__ , dispatch_batches=a__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :Optional[Any] = get_training_setup(a__ )
for iteration, batch in enumerate(a__ ):
lowerCAmelCase , lowerCAmelCase :Union[str, Any] = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCAmelCase , lowerCAmelCase :Dict = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase , lowerCAmelCase :Any = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(a__ , a__ , a__ , a__ , a__ )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(a__ ):
step_model(a__ , a__ , a__ , a__ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
lowerCAmelCase :Optional[int] = ddp_input[torch.randperm(len(a__ ) )]
GradientState._reset_state()
def UpperCAmelCase ( a__=False , a__=False ):
'''simple docstring'''
lowerCAmelCase :Optional[int] = Accelerator(
split_batches=a__ , dispatch_batches=a__ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase :Union[str, Any] = get_training_setup(a__ , a__ )
for iteration, batch in enumerate(a__ ):
lowerCAmelCase , lowerCAmelCase :Optional[int] = batch.values()
# Gather the distributed inputs and targs for the base model
lowerCAmelCase , lowerCAmelCase :Union[str, Any] = accelerator.gather((ddp_input, ddp_target) )
lowerCAmelCase , lowerCAmelCase :Optional[Any] = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(a__ , a__ , a__ , a__ , a__ )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(a__ ):
step_model(a__ , a__ , a__ , a__ )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
lowerCAmelCase :int = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
if accelerator.num_processes > 1:
check_model_parameters(a__ , a__ , a__ , a__ )
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
GradientState._reset_state()
def UpperCAmelCase ( ):
'''simple docstring'''
lowerCAmelCase :int = Accelerator()
lowerCAmelCase :int = RegressionDataset(length=80 )
lowerCAmelCase :Optional[Any] = DataLoader(a__ , batch_size=16 )
lowerCAmelCase :Any = RegressionDataset(length=96 )
lowerCAmelCase :Dict = DataLoader(a__ , batch_size=16 )
lowerCAmelCase , lowerCAmelCase :Tuple = accelerator.prepare(a__ , a__ )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(a__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
if iteration < len(a__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(a__ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
if batch_num < len(a__ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def UpperCAmelCase ( ):
'''simple docstring'''
lowerCAmelCase :List[str] = Accelerator()
lowerCAmelCase :Optional[int] = accelerator.state
if state.local_process_index == 0:
print('**Test `accumulate` gradient accumulation with dataloader break**' )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print('**Test NOOP `no_sync` context manager**' )
test_noop_sync(a__ )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print('**Test Distributed `no_sync` context manager**' )
test_distributed_sync(a__ )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation, ' , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(a__ , a__ )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version('<' , '2.0' ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation with optimizer and scheduler, ' , '`split_batches=False`, `dispatch_batches=False`**' , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
'**Test `accumulate` gradient accumulation with optimizer and scheduler, ' , F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(a__ , a__ )
def UpperCAmelCase ( a__ ):
'''simple docstring'''
main()
if __name__ == "__main__":
main() | 553 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCAmelCase_ (self ):
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
@property
def UpperCAmelCase_ (self ):
torch.manual_seed(0 )
UpperCamelCase__ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , )
return model
@property
def UpperCAmelCase_ (self ):
torch.manual_seed(0 )
UpperCamelCase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.dummy_uncond_unet
UpperCamelCase__ = DDIMScheduler()
UpperCamelCase__ = self.dummy_vq_model
UpperCamelCase__ = LDMPipeline(unet=SCREAMING_SNAKE_CASE_ , vqvae=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
ldm.to(SCREAMING_SNAKE_CASE_ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = ldm(generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , output_type="""numpy""" ).images
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = ldm(generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , output_type="""numpy""" , return_dict=SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase__ = image[0, -3:, -3:, -1]
UpperCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase__ = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
UpperCamelCase__ = 1E-2 if torch_device != """mps""" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ (self ):
UpperCamelCase__ = LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""" )
ldm.to(SCREAMING_SNAKE_CASE_ )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = ldm(generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=5 , output_type="""numpy""" ).images
UpperCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
UpperCamelCase__ = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
UpperCamelCase__ = 1E-2 if torch_device != """mps""" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 86 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=5_12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = seq_length
UpperCamelCase__ = is_training
UpperCamelCase__ = use_input_mask
UpperCamelCase__ = use_token_type_ids
UpperCamelCase__ = use_labels
UpperCamelCase__ = vocab_size
UpperCamelCase__ = hidden_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = hidden_act
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_probs_dropout_prob
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = type_vocab_size
UpperCamelCase__ = type_sequence_label_size
UpperCamelCase__ = initializer_range
UpperCamelCase__ = num_labels
UpperCamelCase__ = num_choices
UpperCamelCase__ = scope
def UpperCAmelCase_ (self ):
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase__ = None
if self.use_input_mask:
UpperCamelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase__ = None
if self.use_token_type_ids:
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = None
if self.use_labels:
UpperCamelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ (self ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase__ = BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
# create attention mask
UpperCamelCase__ = torch.ones(input_ids.shape , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.seq_length // 2
UpperCamelCase__ = 0
# first forward pass
UpperCamelCase__ , UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCamelCase__ = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
UpperCamelCase__ = ids_tensor((1,) , SCREAMING_SNAKE_CASE_ ).item() + 1
UpperCamelCase__ = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
UpperCamelCase__ = random_other_next_tokens
# append to next input_ids and attn_mask
UpperCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )] , dim=1 , )
# get two different outputs
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
# select random slice
UpperCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ = output_from_no_past[:, -1, random_slice_idx].detach()
UpperCamelCase__ = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(config=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase__ = torch.ones(input_ids.shape , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
# first forward pass
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , use_cache=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ , UpperCamelCase__ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase__ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
UpperCamelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase__ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""last_hidden_state"""]
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ )[
"""last_hidden_state"""
]
# select random slice
UpperCamelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase__ = BioGptForCausalLM(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = BioGptModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self.num_labels
UpperCamelCase__ = BioGptForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.prepare_config_and_inputs()
(
(
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) , (
UpperCamelCase__
) ,
) = config_and_inputs
UpperCamelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ = (BioGptForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (
{
"""feature-extraction""": BioGptModel,
"""text-classification""": BioGptForSequenceClassification,
"""text-generation""": BioGptForCausalLM,
"""token-classification""": BioGptForTokenClassification,
"""zero-shot""": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def UpperCAmelCase_ (self ):
self.config_tester.run_common_tests()
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase__ = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_ , gradient_checkpointing=SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = """left"""
# Define PAD Token = EOS Token = 50256
UpperCamelCase__ = tokenizer.eos_token
UpperCamelCase__ = model.config.eos_token_id
# use different length sentences to test batching
UpperCamelCase__ = [
"""Hello, my dog is a little""",
"""Today, I""",
]
UpperCamelCase__ = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(
input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=inputs["""attention_mask"""].to(SCREAMING_SNAKE_CASE_ ) , )
UpperCamelCase__ = tokenizer(sentences[0] , return_tensors="""pt""" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
UpperCamelCase__ = tokenizer(sentences[1] , return_tensors="""pt""" ).input_ids.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(input_ids=SCREAMING_SNAKE_CASE_ , max_length=model.config.max_length - num_paddings )
UpperCamelCase__ = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer.decode(output_non_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = tokenizer.decode(output_padded[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = [
"""Hello, my dog is a little bit bigger than a little bit.""",
"""Today, I have a good idea of how to use the information""",
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , [non_padded_sentence, padded_sentence] )
@slow
def UpperCAmelCase_ (self ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ = BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ = 3
UpperCamelCase__ = input_dict["""input_ids"""]
UpperCamelCase__ = input_ids.ne(1 ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase__ = BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def UpperCAmelCase_ (self ):
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ = 3
UpperCamelCase__ = """multi_label_classification"""
UpperCamelCase__ = input_dict["""input_ids"""]
UpperCamelCase__ = input_ids.ne(1 ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase__ = BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class __A( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = torch.tensor([[2, 48_05, 9, 6_56, 21]] )
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase__ = 4_23_84
UpperCamelCase__ = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def UpperCAmelCase_ (self ):
UpperCamelCase__ = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
UpperCamelCase__ = BioGptForCausalLM.from_pretrained("""microsoft/biogpt""" )
model.to(SCREAMING_SNAKE_CASE_ )
torch.manual_seed(0 )
UpperCamelCase__ = tokenizer("""COVID-19 is""" , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = model.generate(
**SCREAMING_SNAKE_CASE_ , min_length=1_00 , max_length=10_24 , num_beams=5 , early_stopping=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase__ = tokenizer.decode(output_ids[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = (
"""COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
""" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
""" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
""" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
""" more than 800,000 deaths."""
)
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 86 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case : Optional[Any] = False
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : int , lowerCamelCase : Optional[Any]=32 ) -> Dict:
set_seed(0 )
__snake_case : int = UNetaDModel(sample_size=lowerCamelCase , in_channels=3 , out_channels=3 )
__snake_case : List[Any] = torch.optim.SGD(model.parameters() , lr=0.00_01 )
return model, optimizer
@slow
def __snake_case ( self : str ) -> List[Any]:
__snake_case : int = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
__snake_case : Optional[Any] = DDPMScheduler(
num_train_timesteps=1000 , beta_start=0.00_01 , beta_end=0.02 , beta_schedule="linear" , clip_sample=lowerCamelCase , )
__snake_case : Tuple = DDIMScheduler(
num_train_timesteps=1000 , beta_start=0.00_01 , beta_end=0.02 , beta_schedule="linear" , clip_sample=lowerCamelCase , )
assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps
# shared batches for DDPM and DDIM
set_seed(0 )
__snake_case : Tuple = [torch.randn((4, 3, 32, 32) ).clip(-1 , 1 ).to(lowerCamelCase ) for _ in range(4 )]
__snake_case : str = [torch.randn((4, 3, 32, 32) ).to(lowerCamelCase ) for _ in range(4 )]
__snake_case : List[Any] = [torch.randint(0 , 1000 , (4,) ).long().to(lowerCamelCase ) for _ in range(4 )]
# train with a DDPM scheduler
__snake_case , __snake_case : str = self.get_model_optimizer(resolution=32 )
model.train().to(lowerCamelCase )
for i in range(4 ):
optimizer.zero_grad()
__snake_case : Optional[Any] = ddpm_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
__snake_case : List[str] = model(lowerCamelCase , timesteps[i] ).sample
__snake_case : Tuple = torch.nn.functional.mse_loss(lowerCamelCase , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
# recreate the model and optimizer, and retry with DDIM
__snake_case , __snake_case : Optional[int] = self.get_model_optimizer(resolution=32 )
model.train().to(lowerCamelCase )
for i in range(4 ):
optimizer.zero_grad()
__snake_case : str = ddim_scheduler.add_noise(clean_images[i] , noise[i] , timesteps[i] )
__snake_case : Any = model(lowerCamelCase , timesteps[i] ).sample
__snake_case : Tuple = torch.nn.functional.mse_loss(lowerCamelCase , noise[i] )
loss.backward()
optimizer.step()
del model, optimizer
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1E-5 ) )
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1E-5 ) )
| 81 |
from typing import Dict, Optional
import numpy as np
import datasets
__A : Any = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''
__A : int = '''
Args:
predictions (`List[ndarray]`):
List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
references (`List[ndarray]`):
List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
num_labels (`int`):
Number of classes (categories).
ignore_index (`int`):
Index that will be ignored during evaluation.
nan_to_num (`int`, *optional*):
If specified, NaN values will be replaced by the number defined by the user.
label_map (`dict`, *optional*):
If specified, dictionary mapping old label indices to new label indices.
reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
Returns:
`Dict[str, float | ndarray]` comprising various elements:
- *mean_iou* (`float`):
Mean Intersection-over-Union (IoU averaged over all categories).
- *mean_accuracy* (`float`):
Mean accuracy (averaged over all categories).
- *overall_accuracy* (`float`):
Overall accuracy on all images.
- *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
Per category accuracy.
- *per_category_iou* (`ndarray` of shape `(num_labels,)`):
Per category IoU.
Examples:
>>> import numpy as np
>>> mean_iou = datasets.load_metric("mean_iou")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
'''
__A : Tuple = '''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase = None, _UpperCAmelCase = False, ) -> List[str]:
'''simple docstring'''
if label_map is not None:
for old_id, new_id in label_map.items():
lowerCAmelCase : Union[str, Any] = new_id
# turn into Numpy arrays
lowerCAmelCase : int = np.array(_UpperCAmelCase )
lowerCAmelCase : Union[str, Any] = np.array(_UpperCAmelCase )
if reduce_labels:
lowerCAmelCase : Union[str, Any] = 255
lowerCAmelCase : Any = label - 1
lowerCAmelCase : Tuple = 255
lowerCAmelCase : Any = label != ignore_index
lowerCAmelCase : Dict = np.not_equal(_UpperCAmelCase, _UpperCAmelCase )
lowerCAmelCase : Optional[int] = pred_label[mask]
lowerCAmelCase : Optional[int] = np.array(_UpperCAmelCase )[mask]
lowerCAmelCase : str = pred_label[pred_label == label]
lowerCAmelCase : Optional[Any] = np.histogram(_UpperCAmelCase, bins=_UpperCAmelCase, range=(0, num_labels - 1) )[0]
lowerCAmelCase : str = np.histogram(_UpperCAmelCase, bins=_UpperCAmelCase, range=(0, num_labels - 1) )[0]
lowerCAmelCase : Optional[Any] = np.histogram(_UpperCAmelCase, bins=_UpperCAmelCase, range=(0, num_labels - 1) )[0]
lowerCAmelCase : List[str] = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase = None, _UpperCAmelCase = False, ) -> Any:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = np.zeros((num_labels,), dtype=np.floataa )
lowerCAmelCase : Dict = np.zeros((num_labels,), dtype=np.floataa )
lowerCAmelCase : int = np.zeros((num_labels,), dtype=np.floataa )
lowerCAmelCase : int = np.zeros((num_labels,), dtype=np.floataa )
for result, gt_seg_map in zip(_UpperCAmelCase, _UpperCAmelCase ):
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Tuple = intersect_and_union(
_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase = None, _UpperCAmelCase = None, _UpperCAmelCase = False, ) -> Any:
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Dict = total_intersect_and_union(
_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )
# compute metrics
lowerCAmelCase : str = {}
lowerCAmelCase : Dict = total_area_intersect.sum() / total_area_label.sum()
lowerCAmelCase : Optional[Any] = total_area_intersect / total_area_union
lowerCAmelCase : Optional[int] = total_area_intersect / total_area_label
lowerCAmelCase : Union[str, Any] = np.nanmean(_UpperCAmelCase )
lowerCAmelCase : Any = np.nanmean(_UpperCAmelCase )
lowerCAmelCase : Dict = all_acc
lowerCAmelCase : Tuple = iou
lowerCAmelCase : Optional[int] = acc
if nan_to_num is not None:
lowerCAmelCase : int = {metric: np.nan_to_num(_UpperCAmelCase, nan=_UpperCAmelCase ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def lowercase__ ( self : str ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
} ) , reference_urls=[
'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
] , )
def lowercase__ ( self : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : bool , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[Dict[int, int]] = None , UpperCAmelCase_ : bool = False , ):
lowerCAmelCase : Optional[int] = mean_iou(
results=UpperCAmelCase_ , gt_seg_maps=UpperCAmelCase_ , num_labels=UpperCAmelCase_ , ignore_index=UpperCAmelCase_ , nan_to_num=UpperCAmelCase_ , label_map=UpperCAmelCase_ , reduce_labels=UpperCAmelCase_ , )
return iou_result
| 343 | 0 |
import torch
from transformers import AutoModel
class _lowerCamelCase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , snake_case : Optional[int]="sayef/fsner-bert-base-uncased" ):
super(__lowerCAmelCase , self ).__init__()
__UpperCamelCase = AutoModel.from_pretrained(__lowerCAmelCase , return_dict=__lowerCAmelCase )
__UpperCamelCase = torch.nn.CosineSimilarity(3 , 1E-08 )
__UpperCamelCase = torch.nn.Softmax(dim=1 )
def snake_case ( self : List[str] , **snake_case : Dict ):
return self.bert(**__lowerCAmelCase ).last_hidden_state
def snake_case ( self : Any , snake_case : Dict ):
return token_embeddings.sum(2 , keepdim=__lowerCAmelCase )
def snake_case ( self : Optional[Any] , snake_case : List[Any] , snake_case : Optional[Any] , snake_case : Dict=1 ):
return self.softmax(T * self.cos(__lowerCAmelCase , __lowerCAmelCase ) )
def snake_case ( self : str , snake_case : str , snake_case : str ):
__UpperCamelCase = W_supports['''sizes'''].tolist()
__UpperCamelCase = W_supports['''start_token_id'''].item()
__UpperCamelCase = W_supports['''end_token_id'''].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
__UpperCamelCase = self.BERT(**__lowerCAmelCase )
__UpperCamelCase = self.BERT(**__lowerCAmelCase )
__UpperCamelCase = None
__UpperCamelCase = None
__UpperCamelCase = W_supports['''input_ids'''] == start_token_id
__UpperCamelCase = W_supports['''input_ids'''] == end_token_id
for i, size in enumerate(__lowerCAmelCase ):
if i == 0:
__UpperCamelCase = 0
else:
__UpperCamelCase = support_sizes[i - 1]
__UpperCamelCase = S[s : s + size][start_token_masks[s : s + size]]
__UpperCamelCase = S[s : s + size][end_token_masks[s : s + size]]
__UpperCamelCase = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
__UpperCamelCase = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
__UpperCamelCase = torch.vstack((p_starts, p_start) )
__UpperCamelCase = torch.vstack((p_ends, p_end) )
else:
__UpperCamelCase = p_start
__UpperCamelCase = p_end
return p_starts, p_ends
| 707 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = False ) -> str:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ):
__UpperCamelCase = F"Expected string as input, found {type(lowercase_ )}"
raise ValueError(lowercase_ )
if not isinstance(lowercase_ , lowercase_ ):
__UpperCamelCase = F"Expected boolean as use_pascal parameter, found {type(lowercase_ )}"
raise ValueError(lowercase_ )
__UpperCamelCase = input_str.split('''_''' )
__UpperCamelCase = 0 if use_pascal else 1
__UpperCamelCase = words[start_index:]
__UpperCamelCase = [word[0].upper() + word[1:] for word in words_to_capitalize]
__UpperCamelCase = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 375 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class UpperCamelCase__ ( __lowercase ):
"""simple docstring"""
__magic_name__ = "switch_transformers"
__magic_name__ = ["past_key_values"]
__magic_name__ = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , snake_case__=3_2128 , snake_case__=768 , snake_case__=64 , snake_case__=2048 , snake_case__=64 , snake_case__=12 , snake_case__=3 , snake_case__=12 , snake_case__=3 , snake_case__=12 , snake_case__=8 , snake_case__=False , snake_case__=0.01 , snake_case__="float32" , snake_case__=False , snake_case__=32 , snake_case__=128 , snake_case__=0.1 , snake_case__=1E-6 , snake_case__=0.001 , snake_case__=0.001 , snake_case__=1.0 , snake_case__="relu" , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=0 , snake_case__=1 , **snake_case__ , ):
'''simple docstring'''
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Tuple = d_model
_lowerCAmelCase : Dict = d_kv
_lowerCAmelCase : str = d_ff
_lowerCAmelCase : int = num_sparse_encoder_layers
_lowerCAmelCase : Dict = num_layers
_lowerCAmelCase : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_lowerCAmelCase : Dict = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
_lowerCAmelCase : int = self.num_layers // self.num_sparse_encoder_layers
else:
_lowerCAmelCase : Optional[int] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
_lowerCAmelCase : Optional[Any] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
_lowerCAmelCase : Dict = self.num_decoder_layers # HACK: this will create 0 sparse layers
_lowerCAmelCase : Any = num_heads
_lowerCAmelCase : List[Any] = num_experts
_lowerCAmelCase : List[str] = expert_capacity
_lowerCAmelCase : List[str] = router_bias
_lowerCAmelCase : Optional[Any] = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
_lowerCAmelCase : List[str] = router_dtype
_lowerCAmelCase : Any = router_ignore_padding_tokens
_lowerCAmelCase : Optional[Any] = relative_attention_num_buckets
_lowerCAmelCase : Optional[int] = relative_attention_max_distance
_lowerCAmelCase : List[Any] = dropout_rate
_lowerCAmelCase : Optional[int] = layer_norm_epsilon
_lowerCAmelCase : Union[str, Any] = initializer_factor
_lowerCAmelCase : int = feed_forward_proj
_lowerCAmelCase : List[str] = use_cache
_lowerCAmelCase : Optional[int] = add_router_probs
_lowerCAmelCase : Optional[int] = router_z_loss_coef
_lowerCAmelCase : List[str] = router_aux_loss_coef
_lowerCAmelCase : Union[str, Any] = self.feed_forward_proj.split('-' )
_lowerCAmelCase : int = act_info[-1]
_lowerCAmelCase : int = act_info[0] == '''gated'''
if len(_A ) > 1 and act_info[0] != "gated" or len(_A ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_lowerCAmelCase : Optional[Any] = '''gelu_new'''
super().__init__(
pad_token_id=_A , eos_token_id=_A , is_encoder_decoder=_A , **_A , )
| 444 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class A_ ( __lowercase ):
'''simple docstring'''
def __init__( self , *_A , **_A) -> None:
"""simple docstring"""
warnings.warn(
'''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use PerceiverImageProcessor instead.''' , _A , )
super().__init__(*_A , **_A)
| 485 | 0 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__A : int = parse(importlib.metadata.version('torch'))
def lowerCAmelCase_ ( a : Union[str, Version] , a : str , a : str ):
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}''' )
a__ = STR_OPERATION_TO_FUNC[operation]
if isinstance(a , a ):
a__ = parse(importlib.metadata.version(a ) )
return operation(a , parse(a ) )
def lowerCAmelCase_ ( a : str , a : str ):
return compare_versions(a , a , a )
| 126 |
'''simple docstring'''
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
a__ = val
a__ = None
a__ = None
def lowercase__ ( self , _a ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
a__ = Node(_a )
else:
self.left.insert(_a )
elif val > self.val:
if self.right is None:
a__ = Node(_a )
else:
self.right.insert(_a )
else:
a__ = val
def lowerCAmelCase_ ( a : Dict , a : Union[str, Any] ):
# Recursive traversal
if root:
inorder(root.left , a )
res.append(root.val )
inorder(root.right , a )
def lowerCAmelCase_ ( a : List[str] ):
# Build BST
if len(a ) == 0:
return arr
a__ = Node(arr[0] )
for i in range(1 , len(a ) ):
root.insert(arr[i] )
# Traverse BST in order.
a__ = []
inorder(a , a )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 126 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class __magic_name__ ( __a ):
"""simple docstring"""
lowerCAmelCase : Dict = '''mobilenet_v2'''
def __init__( self : List[str] , _lowercase : List[str]=3 , _lowercase : Union[str, Any]=224 , _lowercase : Dict=1.0 , _lowercase : List[Any]=8 , _lowercase : Optional[int]=8 , _lowercase : List[Any]=6 , _lowercase : int=32 , _lowercase : List[Any]=True , _lowercase : List[Any]=True , _lowercase : Optional[Any]="relu6" , _lowercase : Tuple=True , _lowercase : Optional[int]=0.8 , _lowercase : List[str]=0.02 , _lowercase : List[Any]=0.001 , _lowercase : Dict=255 , **_lowercase : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**_lowercase )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
_UpperCamelCase: List[str] = num_channels
_UpperCamelCase: Optional[Any] = image_size
_UpperCamelCase: List[str] = depth_multiplier
_UpperCamelCase: List[str] = depth_divisible_by
_UpperCamelCase: Optional[Any] = min_depth
_UpperCamelCase: str = expand_ratio
_UpperCamelCase: Union[str, Any] = output_stride
_UpperCamelCase: str = first_layer_is_expansion
_UpperCamelCase: Any = finegrained_output
_UpperCamelCase: Optional[int] = hidden_act
_UpperCamelCase: int = tf_padding
_UpperCamelCase: Any = classifier_dropout_prob
_UpperCamelCase: Optional[int] = initializer_range
_UpperCamelCase: Optional[int] = layer_norm_eps
_UpperCamelCase: int = semantic_loss_ignore_index
class __magic_name__ ( __a ):
"""simple docstring"""
lowerCAmelCase : List[str] = version.parse('''1.11''' )
@property
def lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return 1E-4 | 271 | from __future__ import annotations
def lowerCAmelCase_ ( lowercase: str , lowercase: list[str] | None = None , lowercase: dict[str, float] | None = None , lowercase: bool = False , ) -> tuple[int, float, str]:
'''simple docstring'''
_UpperCamelCase: Any = cipher_alphabet or [chr(lowercase ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
_UpperCamelCase: Any = {
'''a''': 0.08497,
'''b''': 0.01492,
'''c''': 0.02202,
'''d''': 0.04253,
'''e''': 0.11162,
'''f''': 0.02228,
'''g''': 0.02015,
'''h''': 0.06094,
'''i''': 0.07546,
'''j''': 0.00153,
'''k''': 0.01292,
'''l''': 0.04025,
'''m''': 0.02406,
'''n''': 0.06749,
'''o''': 0.07507,
'''p''': 0.01929,
'''q''': 0.00095,
'''r''': 0.07587,
'''s''': 0.06327,
'''t''': 0.09356,
'''u''': 0.02758,
'''v''': 0.00978,
'''w''': 0.02560,
'''x''': 0.00150,
'''y''': 0.01994,
'''z''': 0.00077,
}
else:
# Custom frequencies dictionary
_UpperCamelCase: str = frequencies_dict
if not case_sensitive:
_UpperCamelCase: Union[str, Any] = ciphertext.lower()
# Chi squared statistic values
_UpperCamelCase: dict[int, tuple[float, str]] = {}
# cycle through all of the shifts
for shift in range(len(lowercase ) ):
_UpperCamelCase: Tuple = ''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
_UpperCamelCase: Optional[int] = (alphabet_letters.index(letter.lower() ) - shift) % len(
lowercase )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
_UpperCamelCase: Optional[int] = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
_UpperCamelCase: Optional[int] = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
_UpperCamelCase: Any = decrypted_with_shift.lower().count(lowercase )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_UpperCamelCase: int = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_UpperCamelCase: int = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
_UpperCamelCase: List[Any] = decrypted_with_shift.count(lowercase )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
_UpperCamelCase: Union[str, Any] = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
_UpperCamelCase: List[str] = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
_UpperCamelCase: List[Any] = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(lowercase: int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
_UpperCamelCase: int = min(
lowercase , key=lowercase , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) ,
): str = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
) | 271 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( _lowercase , _lowercase , unittest.TestCase):
snake_case__ = StableDiffusionDiffEditPipeline
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
snake_case__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
snake_case__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
snake_case__ = frozenset([])
def _UpperCamelCase ( self : Tuple ) -> List[Any]:
torch.manual_seed(0 )
_UpperCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__UpperCamelCase , )
_UpperCamelCase = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=__UpperCamelCase , set_alpha_to_one=__UpperCamelCase , )
_UpperCamelCase = DDIMInverseScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=__UpperCamelCase , set_alpha_to_zero=__UpperCamelCase , )
torch.manual_seed(0 )
_UpperCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
_UpperCamelCase = CLIPTextModel(__UpperCamelCase )
_UpperCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_UpperCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''inverse_scheduler''': inverse_scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _UpperCamelCase ( self : str , __UpperCamelCase : Dict , __UpperCamelCase : str=0 ) -> str:
_UpperCamelCase = floats_tensor((1, 16, 16) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
_UpperCamelCase = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
if str(__UpperCamelCase ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCamelCase = {
'''prompt''': '''a dog and a newt''',
'''mask_image''': mask,
'''image_latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Any , __UpperCamelCase : List[str]=0 ) -> Any:
_UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
_UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCamelCase = Image.fromarray(np.uinta(__UpperCamelCase ) ).convert('''RGB''' )
if str(__UpperCamelCase ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCamelCase = {
'''image''': image,
'''source_prompt''': '''a cat and a frog''',
'''target_prompt''': '''a dog and a newt''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''num_maps_per_mask''': 2,
'''mask_encode_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple=0 ) -> Any:
_UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
_UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCamelCase = Image.fromarray(np.uinta(__UpperCamelCase ) ).convert('''RGB''' )
if str(__UpperCamelCase ).startswith('''mps''' ):
_UpperCamelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCamelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCamelCase = {
'''image''': image,
'''prompt''': '''a cat and a frog''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''decode_latents''': True,
'''output_type''': '''numpy''',
}
return inputs
def _UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
if not hasattr(self.pipeline_class , '''_optional_components''' ):
return
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
_UpperCamelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCamelCase = pipe(**__UpperCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__UpperCamelCase )
_UpperCamelCase = self.pipeline_class.from_pretrained(__UpperCamelCase )
pipe_loaded.to(__UpperCamelCase )
pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__UpperCamelCase , __UpperCamelCase ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
_UpperCamelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCamelCase = pipe_loaded(**__UpperCamelCase )[0]
_UpperCamelCase = np.abs(output - output_loaded ).max()
self.assertLess(__UpperCamelCase , 1E-4 )
def _UpperCamelCase ( self : Any ) -> Any:
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = self.get_dummy_mask_inputs(__UpperCamelCase )
_UpperCamelCase = pipe.generate_mask(**__UpperCamelCase )
_UpperCamelCase = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
_UpperCamelCase = np.array([0] * 9 )
_UpperCamelCase = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = self.get_dummy_inversion_inputs(__UpperCamelCase )
_UpperCamelCase = pipe.invert(**__UpperCamelCase ).images
_UpperCamelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
_UpperCamelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCamelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
def _UpperCamelCase ( self : Dict ) -> Any:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def _UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
_UpperCamelCase = '''cpu'''
_UpperCamelCase = self.get_dummy_components()
_UpperCamelCase = {'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
_UpperCamelCase = DPMSolverMultistepScheduler(**__UpperCamelCase )
_UpperCamelCase = DPMSolverMultistepInverseScheduler(**__UpperCamelCase )
_UpperCamelCase = self.pipeline_class(**__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = self.get_dummy_inversion_inputs(__UpperCamelCase )
_UpperCamelCase = pipe.invert(**__UpperCamelCase ).images
_UpperCamelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
_UpperCamelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCamelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__UpperCamelCase , 1E-3 )
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : Optional[Any] ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _UpperCamelCase ( cls : Union[str, Any] ) -> str:
_UpperCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )
_UpperCamelCase = raw_image.convert('''RGB''' ).resize((768, 768) )
_UpperCamelCase = raw_image
def _UpperCamelCase ( self : Dict ) -> Tuple:
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=__UpperCamelCase , torch_dtype=torch.floataa )
_UpperCamelCase = DDIMScheduler.from_config(pipe.scheduler.config )
_UpperCamelCase = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = '''a bowl of fruit'''
_UpperCamelCase = '''a bowl of pears'''
_UpperCamelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=__UpperCamelCase , target_prompt=__UpperCamelCase , generator=__UpperCamelCase , )
_UpperCamelCase = pipe.invert(
prompt=__UpperCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=__UpperCamelCase ).latents
_UpperCamelCase = pipe(
prompt=__UpperCamelCase , mask_image=__UpperCamelCase , image_latents=__UpperCamelCase , generator=__UpperCamelCase , negative_prompt=__UpperCamelCase , inpaint_strength=0.7 , output_type='''numpy''' , ).images[0]
_UpperCamelCase = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def _UpperCamelCase ( self : List[str] ) -> Dict:
_UpperCamelCase = torch.manual_seed(0 )
_UpperCamelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=__UpperCamelCase , torch_dtype=torch.floataa )
_UpperCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_UpperCamelCase = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCamelCase = '''a bowl of fruit'''
_UpperCamelCase = '''a bowl of pears'''
_UpperCamelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=__UpperCamelCase , target_prompt=__UpperCamelCase , generator=__UpperCamelCase , )
_UpperCamelCase = pipe.invert(
prompt=__UpperCamelCase , image=self.raw_image , inpaint_strength=0.7 , generator=__UpperCamelCase , num_inference_steps=25 , ).latents
_UpperCamelCase = pipe(
prompt=__UpperCamelCase , mask_image=__UpperCamelCase , image_latents=__UpperCamelCase , generator=__UpperCamelCase , negative_prompt=__UpperCamelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type='''numpy''' , ).images[0]
_UpperCamelCase = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 709 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''torch''', '''scipy''']
def __init__( self : List[Any] , *__UpperCamelCase : int , **__UpperCamelCase : Any ) -> Any:
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def _UpperCamelCase ( cls : Tuple , *__UpperCamelCase : Optional[Any] , **__UpperCamelCase : List[Any] ) -> str:
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def _UpperCamelCase ( cls : str , *__UpperCamelCase : Any , **__UpperCamelCase : int ) -> int:
requires_backends(cls , ['''torch''', '''scipy'''] )
| 342 | 0 |
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase_ : List[str] = '''docs/source/en/_toctree.yml'''
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> Union[str, Any]:
__A : Union[str, Any] = defaultdict(a__ )
__A : Dict = []
__A : Dict = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(a__ )
__A : Optional[int] = new_doc_list
__A : Optional[int] = [key for key, value in counts.items() if value > 1]
__A : Tuple = []
for duplicate_key in duplicates:
__A : Union[str, Any] = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(a__ ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
__A : str = sorted(a__ ,key=lambda a__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(a__ ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(a__ )
# Sort
return overview_doc
def __SCREAMING_SNAKE_CASE ( a__ : Tuple=False ) -> List[str]:
with open(a__ ,encoding="""utf-8""" ) as f:
__A : Optional[Any] = yaml.safe_load(f.read() )
# Get to the API doc
__A : List[str] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__A : Tuple = content[api_idx]["""sections"""]
# Then to the model doc
__A : List[Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
__A : List[Any] = api_doc[scheduler_idx]["""sections"""]
__A : Tuple = clean_doc_toc(a__ )
__A : Tuple = False
if new_scheduler_doc != scheduler_doc:
__A : List[str] = True
if overwrite:
__A : List[str] = new_scheduler_doc
if diff:
if overwrite:
__A : Optional[int] = api_doc
with open(a__ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(yaml.dump(a__ ,allow_unicode=a__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def __SCREAMING_SNAKE_CASE ( a__ : Any=False ) -> Any:
with open(a__ ,encoding="""utf-8""" ) as f:
__A : Union[str, Any] = yaml.safe_load(f.read() )
# Get to the API doc
__A : Union[str, Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__A : Dict = content[api_idx]["""sections"""]
# Then to the model doc
__A : Dict = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
__A : Optional[Any] = False
__A : List[str] = api_doc[pipeline_idx]["""sections"""]
__A : Optional[int] = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
__A : List[Any] = pipeline_doc["""section"""]
__A : int = clean_doc_toc(a__ )
if overwrite:
__A : Dict = new_sub_pipeline_doc
new_pipeline_docs.append(a__ )
# sort overall pipeline doc
__A : Tuple = clean_doc_toc(a__ )
if new_pipeline_docs != pipeline_docs:
__A : List[str] = True
if overwrite:
__A : List[Any] = new_pipeline_docs
if diff:
if overwrite:
__A : Optional[Any] = api_doc
with open(a__ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(yaml.dump(a__ ,allow_unicode=a__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
UpperCAmelCase_ : Tuple = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
UpperCAmelCase_ : Tuple = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 17 |
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : Any ) -> Union[str, Any]:
UpperCamelCase : int = [1]
for i in range(2 , snake_case__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : List[Any] = []
UpperCamelCase : List[Any] = list(range(snake_case__ ) )
# Find permutation
while factorials:
UpperCamelCase : int = factorials.pop()
UpperCamelCase , UpperCamelCase : int = divmod(snake_case__ , snake_case__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Dict = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 444 |
import os
# Precomputes a list of the 100 first triangular numbers
a_ : str = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(_UpperCAmelCase))
SCREAMING_SNAKE_CASE = os.path.join(_UpperCAmelCase , 'words.txt')
SCREAMING_SNAKE_CASE = ''
with open(_UpperCAmelCase) as f:
SCREAMING_SNAKE_CASE = f.readline()
SCREAMING_SNAKE_CASE = [word.strip('"') for word in words.strip('\r\n').split(',')]
SCREAMING_SNAKE_CASE = [
word
for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_UpperCAmelCase)
if __name__ == "__main__":
print(solution())
| 444 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
a : Optional[int] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
a : Any = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
a : List[Any] = BeautifulSoup(res.text, 'html.parser')
a : Dict = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(F'''https://google.com{link.get("href")}''')
| 556 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__ : str = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__ : Dict = re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__ : Optional[Any] = re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
SCREAMING_SNAKE_CASE__ : Optional[int] = re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
SCREAMING_SNAKE_CASE__ : Optional[Any] = re.compile(R'^\s*try:')
# Catches a line with else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(R'^\s*else:')
def a__ ( snake_case__ : Union[str, Any] ):
if _re_test_backend.search(snake_case__ ) is None:
return None
_UpperCAmelCase : str = [b[0] for b in _re_backend.findall(snake_case__ )]
backends.sort()
return "_and_".join(snake_case__ )
def a__ ( snake_case__ : Optional[int] ):
with open(snake_case__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_UpperCAmelCase : Optional[Any] = f.readlines()
_UpperCAmelCase : int = 0
while line_index < len(snake_case__ ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(snake_case__ ):
return None
# First grab the objects without a specific backend in _import_structure
_UpperCAmelCase : int = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
_UpperCAmelCase : int = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(snake_case__ ):
_UpperCAmelCase : Optional[Any] = _re_one_line_import_struct.search(snake_case__ ).groups()[0]
_UpperCAmelCase : str = re.findall("""\[([^\]]+)\]""" , snake_case__ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
_UpperCAmelCase : int = _re_import_struct_key_value.search(snake_case__ )
if single_line_import_search is not None:
_UpperCAmelCase : Dict = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(snake_case__ ) > 0]
objects.extend(snake_case__ )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
_UpperCAmelCase : str = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
_UpperCAmelCase : Tuple = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCAmelCase : List[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCAmelCase : List[Any] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
_UpperCAmelCase : Optional[int] = lines[line_index]
if _re_import_struct_add_one.search(snake_case__ ) is not None:
objects.append(_re_import_struct_add_one.search(snake_case__ ).groups()[0] )
elif _re_import_struct_add_many.search(snake_case__ ) is not None:
_UpperCAmelCase : str = _re_import_struct_add_many.search(snake_case__ ).groups()[0].split(""", """ )
_UpperCAmelCase : Optional[Any] = [obj[1:-1] for obj in imports if len(snake_case__ ) > 0]
objects.extend(snake_case__ )
elif _re_between_brackets.search(snake_case__ ) is not None:
_UpperCAmelCase : str = _re_between_brackets.search(snake_case__ ).groups()[0].split(""", """ )
_UpperCAmelCase : List[str] = [obj[1:-1] for obj in imports if len(snake_case__ ) > 0]
objects.extend(snake_case__ )
elif _re_quote_object.search(snake_case__ ) is not None:
objects.append(_re_quote_object.search(snake_case__ ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
_UpperCAmelCase : str = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_UpperCAmelCase : Optional[Any] = []
while (
line_index < len(snake_case__ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
_UpperCAmelCase : Union[str, Any] = lines[line_index]
_UpperCAmelCase : str = _re_import.search(snake_case__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
_UpperCAmelCase : int = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(snake_case__ ):
# If the line is an if is_backend_available, we grab all objects associated.
_UpperCAmelCase : str = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCAmelCase : Optional[int] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCAmelCase : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
_UpperCAmelCase : Union[str, Any] = lines[line_index]
_UpperCAmelCase : Any = _re_import.search(snake_case__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
_UpperCAmelCase : str = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a__ ( snake_case__ : Any , snake_case__ : Optional[int] ):
def find_duplicates(snake_case__ : Dict ):
return [k for k, v in collections.Counter(snake_case__ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_UpperCAmelCase : int = []
for key in import_dict_objects.keys():
_UpperCAmelCase : Any = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_UpperCAmelCase : Optional[int] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_UpperCAmelCase : Optional[int] = """base imports""" if key == """none""" else f'''{key} backend'''
errors.append(f'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def a__ ( ):
_UpperCAmelCase : Any = []
for root, _, files in os.walk(snake_case__ ):
if "__init__.py" in files:
_UpperCAmelCase : Optional[Any] = os.path.join(snake_case__ , """__init__.py""" )
_UpperCAmelCase : List[str] = parse_init(snake_case__ )
if objects is not None:
_UpperCAmelCase : int = analyze_results(*snake_case__ )
if len(snake_case__ ) > 0:
_UpperCAmelCase : Union[str, Any] = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append("""\n""".join(snake_case__ ) )
if len(snake_case__ ) > 0:
raise ValueError("""\n\n""".join(snake_case__ ) )
def a__ ( ):
_UpperCAmelCase : Tuple = []
for path, directories, files in os.walk(snake_case__ ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(snake_case__ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(snake_case__ ) / folder).glob("""*.py""" ) ) ) == 0:
continue
_UpperCAmelCase : Dict = str((Path(snake_case__ ) / folder).relative_to(snake_case__ ) )
_UpperCAmelCase : Union[str, Any] = short_path.replace(os.path.sep , """.""" )
submodules.append(snake_case__ )
for fname in files:
if fname == "__init__.py":
continue
_UpperCAmelCase : Optional[Any] = str((Path(snake_case__ ) / fname).relative_to(snake_case__ ) )
_UpperCAmelCase : Optional[Any] = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(snake_case__ )
return submodules
SCREAMING_SNAKE_CASE__ : List[Any] = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def a__ ( ):
# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase : int = importlib.util.spec_from_file_location(
"""transformers""" , os.path.join(snake_case__ , """__init__.py""" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
_UpperCAmelCase : Optional[int] = spec.loader.load_module()
_UpperCAmelCase : int = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(snake_case__ ) > 0:
_UpperCAmelCase : Dict = """\n""".join(f'''- {module}''' for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registered in the main init of Transformers:\n"""
f'''{list_of_modules}\n'''
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 643 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
UpperCamelCase_ = {
"vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
"merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}
UpperCamelCase_ = {
"ctrl": 256,
}
UpperCamelCase_ = {
"Pregnancy": 168629,
"Christianity": 7675,
"Explain": 106423,
"Fitness": 63440,
"Saving": 63163,
"Ask": 27171,
"Ass": 95985,
"Joke": 163509,
"Questions": 45622,
"Thoughts": 49605,
"Retail": 52342,
"Feminism": 164338,
"Writing": 11992,
"Atheism": 192263,
"Netflix": 48616,
"Computing": 39639,
"Opinion": 43213,
"Alone": 44967,
"Funny": 58917,
"Gaming": 40358,
"Human": 4088,
"India": 1331,
"Joker": 77138,
"Diet": 36206,
"Legal": 11859,
"Norman": 4939,
"Tip": 72689,
"Weight": 52343,
"Movies": 46273,
"Running": 23425,
"Science": 2090,
"Horror": 37793,
"Confession": 60572,
"Finance": 12250,
"Politics": 16360,
"Scary": 191985,
"Support": 12654,
"Technologies": 32516,
"Teenage": 66160,
"Event": 32769,
"Learned": 67460,
"Notion": 182770,
"Wikipedia": 37583,
"Books": 6665,
"Extract": 76050,
"Confessions": 102701,
"Conspiracy": 75932,
"Links": 63674,
"Narcissus": 150425,
"Relationship": 54766,
"Relationships": 134796,
"Reviews": 41671,
"News": 4256,
"Translation": 26820,
"multilingual": 128406,
}
def lowerCAmelCase__ ( a_ : List[Any] ) -> List[str]:
UpperCAmelCase__ : int = set()
UpperCAmelCase__ : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : Dict = char
UpperCAmelCase__ : Optional[int] = set(a_ )
return pairs
class __UpperCAmelCase ( UpperCamelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : Optional[int] = CONTROL_CODES
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase="<unk>" , **_UpperCAmelCase ):
super().__init__(unk_token=_UpperCAmelCase , **_UpperCAmelCase )
with open(_UpperCAmelCase , encoding='''utf-8''' ) as vocab_handle:
UpperCAmelCase__ : Union[str, Any] = json.load(_UpperCAmelCase )
UpperCAmelCase__ : Dict = {v: k for k, v in self.encoder.items()}
with open(_UpperCAmelCase , encoding='''utf-8''' ) as merges_handle:
UpperCAmelCase__ : int = merges_handle.read().split('''\n''' )[1:-1]
UpperCAmelCase__ : Optional[Any] = [tuple(merge.split() ) for merge in merges]
UpperCAmelCase__ : Optional[Any] = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
UpperCAmelCase__ : int = {}
@property
def lowerCamelCase ( self ):
return len(self.encoder )
def lowerCamelCase ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase ( self , _UpperCAmelCase ):
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : Union[str, Any] = tuple(_UpperCAmelCase )
UpperCAmelCase__ : Optional[Any] = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
UpperCAmelCase__ : Tuple = get_pairs(_UpperCAmelCase )
if not pairs:
return token
while True:
UpperCAmelCase__ : Tuple = min(_UpperCAmelCase , key=lambda _UpperCAmelCase : self.bpe_ranks.get(_UpperCAmelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ : str = bigram
UpperCAmelCase__ : List[str] = []
UpperCAmelCase__ : Any = 0
while i < len(_UpperCAmelCase ):
try:
UpperCAmelCase__ : Tuple = word.index(_UpperCAmelCase , _UpperCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
UpperCAmelCase__ : int = j
if word[i] == first and i < len(_UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
UpperCAmelCase__ : Optional[int] = tuple(_UpperCAmelCase )
UpperCAmelCase__ : List[str] = new_word
if len(_UpperCAmelCase ) == 1:
break
else:
UpperCAmelCase__ : Union[str, Any] = get_pairs(_UpperCAmelCase )
UpperCAmelCase__ : List[str] = '''@@ '''.join(_UpperCAmelCase )
UpperCAmelCase__ : List[Any] = word[:-4]
UpperCAmelCase__ : Optional[Any] = word
return word
def lowerCamelCase ( self , _UpperCAmelCase ):
UpperCAmelCase__ : int = []
UpperCAmelCase__ : List[Any] = re.findall(R'''\S+\n?''' , _UpperCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_UpperCAmelCase ).split(''' ''' ) ) )
return split_tokens
def lowerCamelCase ( self , _UpperCAmelCase ):
return self.encoder.get(_UpperCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCamelCase ( self , _UpperCAmelCase ):
return self.decoder.get(_UpperCAmelCase , self.unk_token )
def lowerCamelCase ( self , _UpperCAmelCase ):
UpperCAmelCase__ : Any = ''' '''.join(_UpperCAmelCase ).replace('''@@ ''' , '''''' ).strip()
return out_string
def lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ : Optional[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase__ : Union[str, Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(_UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_UpperCAmelCase , ensure_ascii=_UpperCAmelCase ) + '''\n''' )
UpperCAmelCase__ : str = 0
with open(_UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
UpperCAmelCase__ : Dict = token_index
writer.write(''' '''.join(_UpperCAmelCase ) + '''\n''' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far) | 599 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=7 , _UpperCAmelCase=3 , _UpperCAmelCase=18 , _UpperCAmelCase=30 , _UpperCAmelCase=400 , _UpperCAmelCase=True , _UpperCAmelCase=None , _UpperCAmelCase=True , _UpperCAmelCase=[0.5, 0.5, 0.5] , _UpperCAmelCase=[0.5, 0.5, 0.5] , ):
UpperCAmelCase__ : Any = size if size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Tuple = image_size
UpperCAmelCase__ : List[str] = min_resolution
UpperCAmelCase__ : List[str] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Dict = size
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : List[str] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def lowerCamelCase ( self ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __UpperCAmelCase ( UpperCamelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = DPTImageProcessor if is_vision_available() else None
def lowerCamelCase ( self ):
UpperCAmelCase__ : Optional[int] = DPTImageProcessingTester(self )
@property
def lowerCamelCase ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase ( self ):
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''image_std''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_UpperCAmelCase , '''size''' ) )
def lowerCamelCase ( self ):
UpperCAmelCase__ : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
UpperCAmelCase__ : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def lowerCamelCase ( self ):
# Initialize image_processing
UpperCAmelCase__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , Image.Image )
# Test not batched input
UpperCAmelCase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : List[str] = image_processing(_UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def lowerCamelCase ( self ):
# Initialize image_processing
UpperCAmelCase__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , numpify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , np.ndarray )
# Test not batched input
UpperCAmelCase__ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : int = image_processing(_UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def lowerCamelCase ( self ):
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_UpperCAmelCase , torchify=_UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(_UpperCAmelCase , torch.Tensor )
# Test not batched input
UpperCAmelCase__ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(_UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , ) | 599 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : Optional[Any] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : List[Any] = "transfo-xl"
_SCREAMING_SNAKE_CASE : Any = ["mems"]
_SCREAMING_SNAKE_CASE : int = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , SCREAMING_SNAKE_CASE_ : List[Any]=26_7735 , SCREAMING_SNAKE_CASE_ : Any=[2_0000, 4_0000, 20_0000] , SCREAMING_SNAKE_CASE_ : Any=1024 , SCREAMING_SNAKE_CASE_ : Any=1024 , SCREAMING_SNAKE_CASE_ : Optional[int]=16 , SCREAMING_SNAKE_CASE_ : List[str]=64 , SCREAMING_SNAKE_CASE_ : Tuple=4096 , SCREAMING_SNAKE_CASE_ : Any=4 , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : List[Any]=18 , SCREAMING_SNAKE_CASE_ : int=1600 , SCREAMING_SNAKE_CASE_ : Optional[int]=1000 , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Any=0 , SCREAMING_SNAKE_CASE_ : Optional[int]=-1 , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : str=0.1 , SCREAMING_SNAKE_CASE_ : Tuple=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : List[str]="normal" , SCREAMING_SNAKE_CASE_ : Any=0.0_1 , SCREAMING_SNAKE_CASE_ : Tuple=0.0_1 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.0_2 , SCREAMING_SNAKE_CASE_ : int=1e-5 , SCREAMING_SNAKE_CASE_ : Optional[int]=0 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] , ) -> Optional[int]:
__snake_case = vocab_size
__snake_case = []
self.cutoffs.extend(SCREAMING_SNAKE_CASE_ )
if proj_share_all_but_first:
__snake_case = [False] + [True] * len(self.cutoffs )
else:
__snake_case = [False] + [False] * len(self.cutoffs )
__snake_case = d_model
__snake_case = d_embed
__snake_case = d_head
__snake_case = d_inner
__snake_case = div_val
__snake_case = pre_lnorm
__snake_case = n_layer
__snake_case = n_head
__snake_case = mem_len
__snake_case = same_length
__snake_case = attn_type
__snake_case = clamp_len
__snake_case = sample_softmax
__snake_case = adaptive
__snake_case = dropout
__snake_case = dropatt
__snake_case = untie_r
__snake_case = init
__snake_case = init_range
__snake_case = proj_init_std
__snake_case = init_std
__snake_case = layer_norm_epsilon
super().__init__(eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@property
def a ( self : Optional[int] ) -> str:
# Message copied from Transformer-XL documentation
logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def a ( self : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ) -> List[str]:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 56 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _a (lowercase__ : str , lowercase__ : str , lowercase__ : Optional[str] = None ) -> str:
"""simple docstring"""
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# old versions of hfh don't url-encode the file path
__snake_case = quote(lowercase__ )
return hfh.hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' , revision=lowercase__ )
| 56 | 1 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class snake_case ( UpperCAmelCase ):
@slow
@require_torch
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
a : Optional[Any] = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny' , 'prajjwal1/bert-tiny' )
a : List[str] = BertTokenizer.from_pretrained('bert-base-uncased' )
a : int = bertabert.config.encoder.vocab_size
a : List[str] = tokenizer.sep_token_id
a : Optional[Any] = tokenizer.cls_token_id
a : Optional[Any] = 1_2_8
a : Any = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='train[:1%]' )
a : Optional[Any] = datasets.load_dataset('cnn_dailymail' , '3.0.0' , split='validation[:1%]' )
a : Dict = train_dataset.select(range(3_2 ) )
a : Any = val_dataset.select(range(1_6 ) )
a : int = 4
def _map_to_encoder_decoder_inputs(A : Optional[Any] ):
# Tokenizer will automatically set [BOS] <text> [EOS]
a : Dict = tokenizer(batch['article'] , padding='max_length' , truncation=A , max_length=5_1_2 )
a : Optional[int] = tokenizer(batch['highlights'] , padding='max_length' , truncation=A , max_length=1_2_8 )
a : int = inputs.input_ids
a : Any = inputs.attention_mask
a : Any = outputs.input_ids
a : Optional[Any] = outputs.input_ids.copy()
a : List[Any] = [
[-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
]
a : Dict = outputs.attention_mask
assert all(len(A ) == 5_1_2 for x in inputs.input_ids )
assert all(len(A ) == 1_2_8 for x in outputs.input_ids )
return batch
def _compute_metrics(A : Optional[Any] ):
a : Optional[int] = pred.label_ids
a : List[str] = pred.predictions
# all unnecessary tokens are removed
a : Union[str, Any] = tokenizer.batch_decode(A , skip_special_tokens=A )
a : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
a : Union[str, Any] = sum([int(pred_str[i] == label_str[i] ) for i in range(len(A ) )] ) / len(A )
return {"accuracy": accuracy}
# map train dataset
a : Dict = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=A , batch_size=A , remove_columns=['article', 'highlights'] , )
train_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
# same for validation dataset
a : Any = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=A , batch_size=A , remove_columns=['article', 'highlights'] , )
val_dataset.set_format(
type='torch' , columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels'] , )
a : Tuple = self.get_auto_remove_tmp_dir()
a : str = SeqaSeqTrainingArguments(
output_dir=A , per_device_train_batch_size=A , per_device_eval_batch_size=A , predict_with_generate=A , evaluation_strategy='steps' , do_train=A , do_eval=A , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
a : Dict = SeqaSeqTrainer(
model=A , args=A , compute_metrics=_compute_metrics , train_dataset=A , eval_dataset=A , tokenizer=A , )
# start training
trainer.train()
| 118 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( UpperCAmelCase ):
__magic_name__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
__magic_name__ = '''CIDAS/clipseg-rd64-refined'''
__magic_name__ = '''image_segmenter'''
__magic_name__ = CLIPSegForImageSegmentation
__magic_name__ = ['''image''', '''text''']
__magic_name__ = ['''image''']
def __init__( self : str , *A : Any , **A : Any ):
'''simple docstring'''
requires_backends(self , ['vision'] )
super().__init__(*A , **A )
def lowerCamelCase__ ( self : Any , A : "Image" , A : str ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=A , return_tensors='pt' )
def lowerCamelCase__ ( self : str , A : List[Any] ):
'''simple docstring'''
with torch.no_grad():
a : Optional[Any] = self.model(**A ).logits
return logits
def lowerCamelCase__ ( self : List[Any] , A : List[str] ):
'''simple docstring'''
a : List[str] = outputs.cpu().detach().numpy()
a : List[Any] = 0
a : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 118 | 1 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
__snake_case : Tuple = TypeVar('_T')
class lowerCamelCase ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Iterable[_T] | None = None ) -> None:
'''simple docstring'''
A__ : list[_T] =list(iterable or [] )
A__ : list[_T] =[]
def __len__( self : Union[str, Any] ) -> int:
'''simple docstring'''
return len(self._stacka ) + len(self._stacka )
def __repr__( self : Any ) -> str:
'''simple docstring'''
return f"Queue({tuple(self._stacka[::-1] + self._stacka )})"
def lowercase__ ( self : Any , lowerCAmelCase_ : _T ) -> None:
'''simple docstring'''
self._stacka.append(lowerCAmelCase_ )
def lowercase__ ( self : int ) -> _T:
'''simple docstring'''
A__ : Any =self._stacka.pop
A__ : Optional[Any] =self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError("""Queue is empty""" )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 215 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : Dict, __snake_case : Union[str, Any], __snake_case : Optional[Any], __snake_case : int, __snake_case : int, __snake_case : Tuple ) -> Dict:
"""simple docstring"""
if index == r:
for j in range(__snake_case ):
print(data[j], end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
A__ : Optional[int] =arr[i]
combination_util(__snake_case, __snake_case, __snake_case, index + 1, __snake_case, i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def __lowerCamelCase ( __snake_case : Any, __snake_case : Dict, __snake_case : str ) -> str:
"""simple docstring"""
A__ : Union[str, Any] =[0] * r
# Print all combination using temporary array 'data[]'
combination_util(__snake_case, __snake_case, __snake_case, 0, __snake_case, 0 )
if __name__ == "__main__":
# Driver code to check the function above
__snake_case : List[Any] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 215 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class __UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
__a : str =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def __snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
lowerCAmelCase = hf_hub_download(
repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
lowerCAmelCase = VideoClassificationPipeline(model=UpperCAmelCase_ , image_processor=UpperCAmelCase_ , top_k=2 )
lowerCAmelCase = [
example_video_filepath,
'''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
]
return video_classifier, examples
def __snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ ):
for example in examples:
lowerCAmelCase = video_classifier(UpperCAmelCase_ )
self.assertEqual(
UpperCAmelCase_ , [
{'''score''': ANY(UpperCAmelCase_ ), '''label''': ANY(UpperCAmelCase_ )},
{'''score''': ANY(UpperCAmelCase_ ), '''label''': ANY(UpperCAmelCase_ )},
] , )
@require_torch
def __snake_case ( self ):
lowerCAmelCase = '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
lowerCAmelCase = VideoMAEFeatureExtractor(
size={'''shortest_edge''': 10} , crop_size={'''height''': 10, '''width''': 10} )
lowerCAmelCase = pipeline(
'''video-classification''' , model=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ , frame_sampling_rate=4 )
lowerCAmelCase = hf_hub_download(repo_id='''nateraw/video-demo''' , filename='''archery.mp4''' , repo_type='''dataset''' )
lowerCAmelCase = video_classifier(UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}] , )
lowerCAmelCase = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
] , )
@require_tf
def __snake_case ( self ):
pass
| 33 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __UpperCamelCase ( __UpperCAmelCase ):
'''simple docstring'''
def __snake_case ( self ):
lowerCAmelCase = tempfile.mkdtemp()
lowerCAmelCase = 8
# DPR tok
lowerCAmelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowerCAmelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
lowerCAmelCase = os.path.join(UpperCAmelCase_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
lowerCAmelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
lowerCAmelCase = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
lowerCAmelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
lowerCAmelCase = {'''unk_token''': '''<unk>'''}
lowerCAmelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
lowerCAmelCase = os.path.join(UpperCAmelCase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase = os.path.join(UpperCAmelCase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(UpperCAmelCase_ ) )
def __snake_case ( self ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def __snake_case ( self ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def __snake_case ( self ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def __snake_case ( self ):
shutil.rmtree(self.tmpdirname )
def __snake_case ( self ):
lowerCAmelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def __snake_case ( self ):
lowerCAmelCase = self.get_dummy_dataset()
lowerCAmelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
lowerCAmelCase = dataset
lowerCAmelCase = RagRetriever(
UpperCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def __snake_case ( self , UpperCAmelCase_ ):
lowerCAmelCase = self.get_dummy_dataset()
lowerCAmelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
lowerCAmelCase = os.path.join(self.tmpdirname , '''dataset''' )
lowerCAmelCase = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
lowerCAmelCase = RagRetriever(
UpperCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
lowerCAmelCase = RagRetriever(
UpperCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , UpperCAmelCase_ ) , )
return retriever
def __snake_case ( self ):
lowerCAmelCase = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
lowerCAmelCase = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
lowerCAmelCase = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(UpperCAmelCase_ , open(UpperCAmelCase_ , '''wb''' ) )
lowerCAmelCase = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
lowerCAmelCase = RagRetriever(
UpperCAmelCase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def __snake_case ( self ):
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_canonical_hf_index_retriever()
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=UpperCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , UpperCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __snake_case ( self ):
lowerCAmelCase = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
lowerCAmelCase = self.get_dummy_dataset()
retriever.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase = RagRetriever.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def __snake_case ( self ):
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=UpperCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , UpperCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __snake_case ( self ):
lowerCAmelCase = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase = RagRetriever.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def __snake_case ( self ):
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=UpperCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , UpperCAmelCase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __snake_case ( self ):
lowerCAmelCase = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase = RagRetriever.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
def __snake_case ( self ):
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_legacy_index_retriever()
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=UpperCAmelCase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(UpperCAmelCase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , UpperCAmelCase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __snake_case ( self ):
lowerCAmelCase = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase = RagRetriever.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever.retrieve(UpperCAmelCase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def __snake_case ( self ):
import torch
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_canonical_hf_index_retriever()
lowerCAmelCase = [[5, 7], [10, 11]]
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever(UpperCAmelCase_ , UpperCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=UpperCAmelCase_ )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , np.ndarray )
lowerCAmelCase = retriever(
UpperCAmelCase_ , UpperCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=UpperCAmelCase_ , return_tensors='''pt''' , )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def __snake_case ( self ):
lowerCAmelCase = self.get_dpr_ctx_encoder_tokenizer()
lowerCAmelCase = 1
lowerCAmelCase = self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_ )
retriever.set_ctx_encoder_tokenizer(UpperCAmelCase_ )
lowerCAmelCase = [[5, 7], [10, 11]]
lowerCAmelCase = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase = retriever(UpperCAmelCase_ , UpperCAmelCase_ , prefix=retriever.config.generator.prefix , n_docs=UpperCAmelCase_ )
self.assertEqual(
len(UpperCAmelCase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , UpperCAmelCase_ ) # check for doc token related keys in dictionary.
| 33 | 1 |
'''simple docstring'''
from __future__ import annotations
A_ = list[tuple[int, int]]
A_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
A_ = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> str:
'''simple docstring'''
lowerCamelCase_ = pos_x
lowerCamelCase_ = pos_y
lowerCamelCase_ = (pos_y, pos_x)
lowerCamelCase_ = goal_x
lowerCamelCase_ = goal_y
lowerCamelCase_ = g_cost
lowerCamelCase_ = parent
lowerCamelCase_ = self.calculate_heuristic()
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = abs(self.pos_x - self.goal_x )
lowerCamelCase_ = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
return self.f_cost < other.f_cost
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowercase__ )
lowerCamelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , lowercase__ )
lowerCamelCase_ = [self.start]
lowerCamelCase_ = []
lowerCamelCase_ = False
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowerCamelCase_ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
lowerCamelCase_ = True
return self.retrace_path(lowercase__ )
self.closed_nodes.append(lowercase__ )
lowerCamelCase_ = self.get_successors(lowercase__ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowercase__ )
else:
# retrieve the best current path
lowerCamelCase_ = self.open_nodes.pop(self.open_nodes.index(lowercase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowercase__ )
else:
self.open_nodes.append(lowercase__ )
if not self.reached:
return [self.start.pos]
return None
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = []
for action in delta:
lowerCamelCase_ = parent.pos_x + action[1]
lowerCamelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowercase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowercase__ , lowercase__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowercase__ , ) )
return successors
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
lowerCamelCase_ = node
lowerCamelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowerCamelCase_ = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
A_ = (0, 0)
A_ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print("------")
A_ = GreedyBestFirst(init, goal)
A_ = greedy_bf.search()
if path:
for pos_x, pos_y in path:
A_ = 2
for elem in grid:
print(elem)
| 42 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple ):
"""simple docstring"""
a_ : Dict = Mock()
a_ : List[str] = conn, Mock()
a_ : Optional[int] = iter([1, None] )
a_ : List[str] = lambda UpperCamelCase__ : next(UpperCamelCase__ )
# ===== invoke =====
send_file(filename="""mytext.txt""" , testing=UpperCamelCase__ )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 442 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Optional[int] =prime_factors(_lowerCamelCase )
if is_square_free(_lowerCamelCase ):
return -1 if len(_lowerCamelCase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 363 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
UpperCamelCase = logging.get_logger(__name__)
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
return [
int(1_000 * (box[0] / width) ),
int(1_000 * (box[1] / height) ),
int(1_000 * (box[2] / width) ),
int(1_000 * (box[3] / height) ),
]
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple:
_lowercase : Optional[int] = tesseract_config if tesseract_config is not None else ''
# apply OCR
_lowercase : Any = to_pil_image(SCREAMING_SNAKE_CASE )
_lowercase , _lowercase : Any = pil_image.size
_lowercase : Dict = pytesseract.image_to_data(SCREAMING_SNAKE_CASE , lang=SCREAMING_SNAKE_CASE , output_type='dict' , config=SCREAMING_SNAKE_CASE )
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase : List[str] = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
_lowercase : Optional[Any] = [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE ) if not word.strip()]
_lowercase : Union[str, Any] = [word for idx, word in enumerate(SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
_lowercase : Optional[Any] = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
_lowercase : int = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
_lowercase : List[str] = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
_lowercase : Optional[Any] = [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
_lowercase : Any = []
for x, y, w, h in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_lowercase : Optional[Any] = [x, y, x + w, y + h]
actual_boxes.append(SCREAMING_SNAKE_CASE )
# finally, normalize the bounding boxes
_lowercase : Union[str, Any] = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Union[str, Any] = ["pixel_values"]
def __init__( self , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = "" , **_lowerCAmelCase , ):
super().__init__(**_lowerCAmelCase )
_lowercase : Optional[Any] = size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
_lowercase : int = get_size_dict(_lowerCAmelCase )
_lowercase : Dict = do_resize
_lowercase : Any = size
_lowercase : Tuple = resample
_lowercase : Optional[int] = apply_ocr
_lowercase : Optional[int] = ocr_lang
_lowercase : str = tesseract_config
def __a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = None , **_lowerCAmelCase , ):
_lowercase : Any = get_size_dict(_lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : List[Any] = (size['height'], size['width'])
return resize(_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def __a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , **_lowerCAmelCase , ):
_lowercase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
_lowercase : Tuple = size if size is not None else self.size
_lowercase : Optional[int] = get_size_dict(_lowerCAmelCase )
_lowercase : Union[str, Any] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
_lowercase : str = ocr_lang if ocr_lang is not None else self.ocr_lang
_lowercase : List[Any] = tesseract_config if tesseract_config is not None else self.tesseract_config
_lowercase : Tuple = make_list_of_images(_lowerCAmelCase )
if not valid_images(_lowerCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
# All transformations expect numpy arrays.
_lowercase : Union[str, Any] = [to_numpy_array(_lowerCAmelCase ) for image in images]
if apply_ocr:
requires_backends(self , 'pytesseract' )
_lowercase : Any = []
_lowercase : Any = []
for image in images:
_lowercase , _lowercase : int = apply_tesseract(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
words_batch.append(_lowerCAmelCase )
boxes_batch.append(_lowerCAmelCase )
if do_resize:
_lowercase : List[Any] = [self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
_lowercase : List[str] = [flip_channel_order(_lowerCAmelCase ) for image in images]
_lowercase : int = [to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
_lowercase : Optional[Any] = BatchFeature(data={'pixel_values': images} , tensor_type=_lowerCAmelCase )
if apply_ocr:
_lowercase : Optional[Any] = words_batch
_lowercase : Dict = boxes_batch
return data
| 66 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase(a : int = 100_0000 , a : int = 10 ) -> int:
_SCREAMING_SNAKE_CASE =defaultdict(a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_SCREAMING_SNAKE_CASE =max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_SCREAMING_SNAKE_CASE =1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(a , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f"{solution() = }")
| 255 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
__A ="bert-base-cased"
__A ="google/pegasus-xsum"
__A =[" Sam ate lunch today.", "Sams lunch ingredients."]
__A =["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__A ="patrickvonplaten/t5-tiny-random"
__A ="sshleifer/bart-tiny-random"
__A ="sshleifer/tiny-mbart"
__A ="sshleifer/tiny-marian-en-de"
def a ( _UpperCAmelCase : Path , _UpperCAmelCase : list ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = '''\n'''.join(_UpperCAmelCase )
Path(_UpperCAmelCase ).open('''w''' ).writelines(_UpperCAmelCase )
def a ( _UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(_UpperCAmelCase , f'{split}.source' ) , _UpperCAmelCase )
_dump_articles(os.path.join(_UpperCAmelCase , f'{split}.target' ) , _UpperCAmelCase )
return tmp_dir
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def snake_case__ ( self : List[Any] , a_ : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Any = AutoTokenizer.from_pretrained(a_ )
__UpperCAmelCase : Any = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__UpperCAmelCase : List[Any] = max(len(tokenizer.encode(a_ ) ) for a in ARTICLES )
__UpperCAmelCase : Dict = max(len(tokenizer.encode(a_ ) ) for a in SUMMARIES )
__UpperCAmelCase : List[Any] = 4
__UpperCAmelCase : Optional[int] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
__UpperCAmelCase : Tuple = SeqaSeqDataset(
a_ , data_dir=a_ , type_path='''train''' , max_source_length=a_ , max_target_length=a_ , src_lang=a_ , tgt_lang=a_ , )
__UpperCAmelCase : Optional[int] = DataLoader(a_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(a_ , a_ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__UpperCAmelCase : str = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def snake_case__ ( self : Union[str, Any] , a_ : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : int = AutoTokenizer.from_pretrained(a_ )
__UpperCAmelCase : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__UpperCAmelCase : Optional[Any] = max(len(tokenizer.encode(a_ ) ) for a in ARTICLES )
__UpperCAmelCase : List[Any] = max(len(tokenizer.encode(a_ ) ) for a in SUMMARIES )
__UpperCAmelCase : int = 4
__UpperCAmelCase : List[str] = LegacySeqaSeqDataset(
a_ , data_dir=a_ , type_path='''train''' , max_source_length=20 , max_target_length=a_ , )
__UpperCAmelCase : Optional[int] = DataLoader(a_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def snake_case__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
__UpperCAmelCase : Dict = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__UpperCAmelCase : Dict = tmp_dir.joinpath('''train.source''' ).open().readlines()
__UpperCAmelCase : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(a_ , a_ , 1_28 , a_ )
__UpperCAmelCase : str = {x.name for x in tmp_dir.iterdir()}
__UpperCAmelCase : Tuple = {x.name for x in save_dir.iterdir()}
__UpperCAmelCase : int = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(a_ ) < len(a_ )
assert len(a_ ) == 1
assert len(packed_examples[0] ) == sum(len(a_ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def snake_case__ ( self : Tuple ):
'''simple docstring'''
if not FAIRSEQ_AVAILABLE:
return
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = self._get_dataset(max_len=64 )
__UpperCAmelCase : int = 64
__UpperCAmelCase : Dict = ds.make_dynamic_sampler(a_ , required_batch_size_multiple=a_ )
__UpperCAmelCase : Optional[int] = [len(a_ ) for x in batch_sampler]
assert len(set(a_ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(a_ ) == len(a_ ) # no dropped or added examples
__UpperCAmelCase : Union[str, Any] = DataLoader(a_ , batch_sampler=a_ , collate_fn=ds.collate_fn , num_workers=2 )
__UpperCAmelCase : Optional[int] = []
__UpperCAmelCase : Optional[int] = []
for batch in data_loader:
__UpperCAmelCase : int = batch['''input_ids'''].shape
__UpperCAmelCase : List[str] = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__UpperCAmelCase : List[Any] = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(a_ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(a_ )
assert num_src_per_batch[0] == max(a_ )
if failures:
raise AssertionError(F'too many tokens in {len(a_ )} batches' )
def snake_case__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = self._get_dataset(max_len=5_12 )
__UpperCAmelCase : Dict = 2
__UpperCAmelCase : str = ds.make_sortish_sampler(a_ , shuffle=a_ )
__UpperCAmelCase : List[Any] = DataLoader(a_ , batch_size=a_ , collate_fn=ds.collate_fn , num_workers=2 )
__UpperCAmelCase : str = DataLoader(a_ , batch_size=a_ , collate_fn=ds.collate_fn , num_workers=2 , sampler=a_ )
__UpperCAmelCase : Any = tokenizer.pad_token_id
def count_pad_tokens(a_ : List[str] , a_ : str="input_ids" ):
return [batch[k].eq(a_ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(a_ , k='''labels''' ) ) < sum(count_pad_tokens(a_ , k='''labels''' ) )
assert sum(count_pad_tokens(a_ ) ) < sum(count_pad_tokens(a_ ) )
assert len(a_ ) == len(a_ )
def snake_case__ ( self : Tuple , a_ : Optional[int]=10_00 , a_ : Optional[Any]=1_28 ):
'''simple docstring'''
if os.getenv('''USE_REAL_DATA''' , a_ ):
__UpperCAmelCase : Optional[Any] = '''examples/seq2seq/wmt_en_ro'''
__UpperCAmelCase : List[Any] = max_len * 2 * 64
if not Path(a_ ).joinpath('''train.len''' ).exists():
save_len_file(a_ , a_ )
else:
__UpperCAmelCase : Any = '''examples/seq2seq/test_data/wmt_en_ro'''
__UpperCAmelCase : List[Any] = max_len * 4
save_len_file(a_ , a_ )
__UpperCAmelCase : int = AutoTokenizer.from_pretrained(a_ )
__UpperCAmelCase : Dict = SeqaSeqDataset(
a_ , data_dir=a_ , type_path='''train''' , max_source_length=a_ , max_target_length=a_ , n_obs=a_ , )
return ds, max_tokens, tokenizer
def snake_case__ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[str] = self._get_dataset()
__UpperCAmelCase : Any = set(DistributedSortishSampler(a_ , 2_56 , num_replicas=2 , rank=0 , add_extra_examples=a_ ) )
__UpperCAmelCase : Any = set(DistributedSortishSampler(a_ , 2_56 , num_replicas=2 , rank=1 , add_extra_examples=a_ ) )
assert idsa.intersection(a_ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def snake_case__ ( self : int , a_ : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained(a_ , use_fast=a_ )
if tok_name == MBART_TINY:
__UpperCAmelCase : Any = SeqaSeqDataset(
a_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
__UpperCAmelCase : Any = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__UpperCAmelCase : Optional[Any] = SeqaSeqDataset(
a_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
__UpperCAmelCase : Any = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(a_ ) == 1 if tok_name == BART_TINY else len(a_ ) == 0
| 241 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = 42
def __init__( self : Optional[int] , a_ : UNetaDModel , a_ : KarrasVeScheduler ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=a_ , scheduler=a_ )
@torch.no_grad()
def __call__( self : Optional[Any] , a_ : int = 1 , a_ : int = 50 , a_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a_ : Optional[str] = "pil" , a_ : bool = True , **a_ : List[Any] , ):
'''simple docstring'''
__UpperCAmelCase : Any = self.unet.config.sample_size
__UpperCAmelCase : int = (batch_size, 3, img_size, img_size)
__UpperCAmelCase : Optional[Any] = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
__UpperCAmelCase : str = randn_tensor(a_ , generator=a_ , device=self.device ) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(a_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# here sigma_t == t_i from the paper
__UpperCAmelCase : str = self.scheduler.schedule[t]
__UpperCAmelCase : str = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
__UpperCAmelCase , __UpperCAmelCase : List[str] = self.scheduler.add_noise_to_input(a_ , a_ , generator=a_ )
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
__UpperCAmelCase : str = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
__UpperCAmelCase : Optional[Any] = self.scheduler.step(a_ , a_ , a_ , a_ )
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
__UpperCAmelCase : Tuple = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample
__UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(
a_ , a_ , a_ , a_ , step_output.prev_sample , step_output['''derivative'''] , )
__UpperCAmelCase : List[Any] = step_output.prev_sample
__UpperCAmelCase : str = (sample / 2 + 0.5).clamp(0 , 1 )
__UpperCAmelCase : Optional[int] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__UpperCAmelCase : Tuple = self.numpy_to_pil(a_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a_ )
| 241 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCamelCase( __lowerCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[str] = KandinskyVaaPriorPipeline
__SCREAMING_SNAKE_CASE : int = ['''prompt''']
__SCREAMING_SNAKE_CASE : Tuple = ['''prompt''', '''negative_prompt''']
__SCREAMING_SNAKE_CASE : Any = [
'''num_images_per_prompt''',
'''generator''',
'''num_inference_steps''',
'''latents''',
'''negative_prompt''',
'''guidance_scale''',
'''output_type''',
'''return_dict''',
]
__SCREAMING_SNAKE_CASE : Tuple = False
@property
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
return 3_2
@property
def __lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
return 3_2
@property
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return 1_0_0
@property
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__ )
@property
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Union[str, Any] = {
'num_attention_heads': 2,
'attention_head_dim': 1_2,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
__a : Optional[Any] = PriorTransformer(**SCREAMING_SNAKE_CASE__ )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
__a : Tuple = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Dict = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=2_2_4 , projection_dim=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1_4 , )
__a : int = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE__ )
return model
@property
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
__a : Optional[Any] = CLIPImageProcessor(
crop_size=2_2_4 , do_center_crop=SCREAMING_SNAKE_CASE__ , do_normalize=SCREAMING_SNAKE_CASE__ , do_resize=SCREAMING_SNAKE_CASE__ , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=2_2_4 , )
return image_processor
def __lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
__a : Union[str, Any] = self.dummy_prior
__a : int = self.dummy_image_encoder
__a : Optional[Any] = self.dummy_text_encoder
__a : Tuple = self.dummy_tokenizer
__a : Dict = self.dummy_image_processor
__a : Tuple = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1_0_0_0 , clip_sample=SCREAMING_SNAKE_CASE__ , clip_sample_range=10.0 , )
__a : Any = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any=0 ):
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE__ ).startswith('mps' ):
__a : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
__a : str = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
__a : Tuple = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
__a : Union[str, Any] = 'cpu'
__a : str = self.get_dummy_components()
__a : Optional[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
__a : List[Any] = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) )
__a : List[Any] = output.image_embeds
__a : Optional[int] = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ ) , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
__a : List[Any] = image[0, -1_0:]
__a : Union[str, Any] = image_from_tuple[0, -1_0:]
assert image.shape == (1, 3_2)
__a : Optional[int] = np.array(
[-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : str = torch_device == 'cpu'
__a : Tuple = True
__a : Optional[Any] = False
self._test_inference_batch_single_identical(
test_max_difference=SCREAMING_SNAKE_CASE__ , relax_max_difference=SCREAMING_SNAKE_CASE__ , test_mean_pixel_difference=SCREAMING_SNAKE_CASE__ , )
@skip_mps
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
__a : Optional[int] = torch_device == 'cpu'
__a : Any = False
self._test_attention_slicing_forward_pass(
test_max_difference=SCREAMING_SNAKE_CASE__ , test_mean_pixel_difference=SCREAMING_SNAKE_CASE__ , )
| 47 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
lowerCAmelCase_ : Optional[int] = logging.getLogger(__name__)
@dataclass
class lowerCamelCase_ :
_lowerCAmelCase : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowerCAmelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_lowerCAmelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_lowerCAmelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
_lowerCAmelCase : bool = field(
default=snake_case_ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
_lowerCAmelCase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
_lowerCAmelCase : bool = field(
default=snake_case_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
@dataclass
class lowerCamelCase_ :
_lowerCAmelCase : Optional[str] = field(default=snake_case_ , metadata={'help': 'The input training data file (a text file).'} )
_lowerCAmelCase : Optional[str] = field(
default=snake_case_ , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
_lowerCAmelCase : bool = field(
default=snake_case_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
_lowerCAmelCase : Optional[int] = field(
default=snake_case_ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
_lowerCAmelCase : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'The maximum total input sequence length after tokenization. If passed, sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_lowerCAmelCase : bool = field(
default=snake_case_ , metadata={
'help': (
'Whether to pad all samples to the maximum sentence length. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch. More '
'efficient on GPU but very bad for TPU.'
)
} , )
_lowerCAmelCase : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
_lowerCAmelCase : Optional[int] = field(
default=snake_case_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def __lowercase ( self : Tuple ):
"""simple docstring"""
if self.train_file is not None:
SCREAMING_SNAKE_CASE : List[str] = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
SCREAMING_SNAKE_CASE : List[Any] = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class lowerCamelCase_ :
_lowerCAmelCase : PreTrainedTokenizerBase
_lowerCAmelCase : Union[bool, str, PaddingStrategy] = True
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[int] = None
def __call__( self : List[Any] , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = '''label''' if '''label''' in features[0].keys() else '''labels'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [feature.pop(lowerCAmelCase__ ) for feature in features]
SCREAMING_SNAKE_CASE : List[Any] = len(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = len(features[0]['''input_ids'''] )
SCREAMING_SNAKE_CASE : str = [
[{k: v[i] for k, v in feature.items()} for i in range(lowerCAmelCase__ )] for feature in features
]
SCREAMING_SNAKE_CASE : Optional[int] = list(chain(*lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Dict = self.tokenizer.pad(
lowerCAmelCase__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
SCREAMING_SNAKE_CASE : int = {k: v.view(lowerCAmelCase__ , lowerCAmelCase__ , -1 ) for k, v in batch.items()}
# Add back labels
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(lowerCAmelCase__ , dtype=torch.intaa )
return batch
def UpperCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , A , A )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Tuple = training_args.get_process_log_level()
logger.setLevel(A )
datasets.utils.logging.set_verbosity(A )
transformers.utils.logging.set_verbosity(A )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE : Dict = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE : Optional[int] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if data_args.train_file is not None:
SCREAMING_SNAKE_CASE : Dict = data_args.train_file
if data_args.validation_file is not None:
SCREAMING_SNAKE_CASE : Dict = data_args.validation_file
SCREAMING_SNAKE_CASE : List[str] = data_args.train_file.split('''.''' )[-1]
SCREAMING_SNAKE_CASE : Optional[int] = load_dataset(
A , data_files=A , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : Tuple = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : int = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
SCREAMING_SNAKE_CASE : Optional[Any] = [F"""ending{i}""" for i in range(4 )]
SCREAMING_SNAKE_CASE : Any = '''sent1'''
SCREAMING_SNAKE_CASE : Tuple = '''sent2'''
if data_args.max_seq_length is None:
SCREAMING_SNAKE_CASE : List[str] = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
SCREAMING_SNAKE_CASE : List[str] = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
SCREAMING_SNAKE_CASE : Tuple = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(A : Any ):
SCREAMING_SNAKE_CASE : int = [[context] * 4 for context in examples[context_name]]
SCREAMING_SNAKE_CASE : str = examples[question_header_name]
SCREAMING_SNAKE_CASE : List[Any] = [
[F"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(A )
]
# Flatten out
SCREAMING_SNAKE_CASE : List[str] = list(chain(*A ) )
SCREAMING_SNAKE_CASE : int = list(chain(*A ) )
# Tokenize
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(
A , A , truncation=A , max_length=A , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(A ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
SCREAMING_SNAKE_CASE : str = raw_datasets['''train''']
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = min(len(A ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE : Optional[Any] = train_dataset.select(range(A ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE : str = train_dataset.map(
A , batched=A , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
SCREAMING_SNAKE_CASE : str = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE : List[Any] = min(len(A ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE : int = eval_dataset.select(range(A ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
SCREAMING_SNAKE_CASE : Tuple = eval_dataset.map(
A , batched=A , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
SCREAMING_SNAKE_CASE : Optional[int] = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=A , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(A : str ):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = eval_predictions
SCREAMING_SNAKE_CASE : Any = np.argmax(A , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
SCREAMING_SNAKE_CASE : Union[str, Any] = Trainer(
model=A , args=A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=A , data_collator=A , compute_metrics=A , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE : List[Any] = last_checkpoint
SCREAMING_SNAKE_CASE : List[Any] = trainer.train(resume_from_checkpoint=A )
trainer.save_model() # Saves the tokenizer too for easy upload
SCREAMING_SNAKE_CASE : List[str] = train_result.metrics
SCREAMING_SNAKE_CASE : str = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(A )
)
SCREAMING_SNAKE_CASE : Dict = min(A , len(A ) )
trainer.log_metrics('''train''' , A )
trainer.save_metrics('''train''' , A )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
SCREAMING_SNAKE_CASE : Optional[int] = trainer.evaluate()
SCREAMING_SNAKE_CASE : int = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(A )
SCREAMING_SNAKE_CASE : List[str] = min(A , len(A ) )
trainer.log_metrics('''eval''' , A )
trainer.save_metrics('''eval''' , A )
SCREAMING_SNAKE_CASE : Dict = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**A )
else:
trainer.create_model_card(**A )
def UpperCAmelCase ( A : str ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 527 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
assert column_title.isupper()
lowerCAmelCase__ : Optional[int] = 0
lowerCAmelCase__ : int = len(SCREAMING_SNAKE_CASE_ ) - 1
lowerCAmelCase__ : Union[str, Any] = 0
while index >= 0:
lowerCAmelCase__ : int = (ord(column_title[index] ) - 64) * pow(26 , SCREAMING_SNAKE_CASE_ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 712 |
import unittest
from transformers import DonutProcessor
lowerCamelCase__ = """naver-clova-ix/donut-base"""
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = DonutProcessor.from_pretrained(a )
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = {
'name': 'John Doe',
'age': '99',
'city': 'Atlanta',
'state': 'GA',
'zip': '30301',
'phone': '123-4567',
'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
}
lowerCAmelCase__ : Union[str, Any] = (
'<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
'<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
'<s_nicknames><s_nickname>Johnny</s_nickname>'
'<sep/><s_nickname>JD</s_nickname></s_nicknames>'
)
lowerCAmelCase__ : Optional[Any] = self.processor.tokenajson(a )
self.assertDictEqual(a , a ) | 69 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = [0, 2, 4, 6, 8]
_SCREAMING_SNAKE_CASE : Tuple = [1, 3, 5, 7, 9]
def UpperCamelCase_( snake_case : Dict , snake_case : Any , snake_case : Union[str, Any] , snake_case : Any ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 1_0
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
snake_case_ = 0
for digit in range(1_0 ):
snake_case_ = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 1_0 , snake_case , snake_case )
return result
snake_case_ = 0
for digita in range(1_0 ):
snake_case_ = digita
if (remainder + digita) % 2 == 0:
snake_case_ = ODD_DIGITS
else:
snake_case_ = EVEN_DIGITS
for digita in other_parity_digits:
snake_case_ = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 1_0 , snake_case , snake_case , )
return result
def UpperCamelCase_( snake_case : Optional[Any] = 9 ):
'''simple docstring'''
snake_case_ = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(snake_case , 0 , [0] * length , snake_case )
return result
if __name__ == "__main__":
print(F"{solution() = }")
| 400 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
# TF training parameters
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
SCREAMING_SNAKE_CASE__ : Optional[int] = False
def lowercase ( SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
return TrainCommand(SCREAMING_SNAKE_CASE )
class a_ ( SCREAMING_SNAKE_CASE__ ):
@staticmethod
def A_( SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=SCREAMING_SNAKE_CASE , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=SCREAMING_SNAKE_CASE , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=SCREAMING_SNAKE_CASE , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=SCREAMING_SNAKE_CASE , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=SCREAMING_SNAKE_CASE , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=SCREAMING_SNAKE_CASE , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=SCREAMING_SNAKE_CASE , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=SCREAMING_SNAKE_CASE , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=SCREAMING_SNAKE_CASE , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=SCREAMING_SNAKE_CASE , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=SCREAMING_SNAKE_CASE , default=3e-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=SCREAMING_SNAKE_CASE , default=1e-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=SCREAMING_SNAKE_CASE )
def __init__( self , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = logging.get_logger('transformers-cli/training' )
SCREAMING_SNAKE_CASE_ = 'tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ = args.output
SCREAMING_SNAKE_CASE_ = args.column_label
SCREAMING_SNAKE_CASE_ = args.column_text
SCREAMING_SNAKE_CASE_ = args.column_id
self.logger.info(f'Loading {args.task} pipeline for {args.model}' )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE_ = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f'Loading dataset from {args.train_data}' )
SCREAMING_SNAKE_CASE_ = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE_ = None
if args.validation_data:
self.logger.info(f'Loading validation dataset from {args.validation_data}' )
SCREAMING_SNAKE_CASE_ = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE_ = args.validation_split
SCREAMING_SNAKE_CASE_ = args.train_batch_size
SCREAMING_SNAKE_CASE_ = args.valid_batch_size
SCREAMING_SNAKE_CASE_ = args.learning_rate
SCREAMING_SNAKE_CASE_ = args.adam_epsilon
def A_( self ) -> Dict:
"""simple docstring"""
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def A_( self ) -> Optional[int]:
"""simple docstring"""
raise NotImplementedError
def A_( self ) -> Dict:
"""simple docstring"""
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 205 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCAmelCase_ ( __UpperCAmelCase: List[Any] , __UpperCAmelCase: List[Any]=False ) -> Dict:
UpperCamelCase__ : str = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCamelCase__ : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def lowerCAmelCase_ ( __UpperCAmelCase: List[str] , __UpperCAmelCase: List[str] , __UpperCAmelCase: str=False ) -> Tuple:
for i in range(config.num_hidden_layers ):
if base_model:
UpperCamelCase__ : int = ''''''
else:
UpperCamelCase__ : str = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase__ : Any = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
UpperCamelCase__ : Optional[int] = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : Any = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase__ : Any = in_proj_bias[: config.hidden_size]
UpperCamelCase__ : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase__ : int = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase__ : Tuple = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __UpperCAmelCase: List[str] ) -> List[Any]:
UpperCamelCase__ : str = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[str] , __UpperCAmelCase: int ) -> str:
UpperCamelCase__ : Optional[Any] = dct.pop(__UpperCAmelCase )
UpperCamelCase__ : Optional[Any] = val
def lowerCAmelCase_ ( ) -> int:
UpperCamelCase__ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ : Union[str, Any] = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __UpperCAmelCase: Tuple , __UpperCAmelCase: Any ) -> Tuple:
UpperCamelCase__ : Dict = ViTConfig()
UpperCamelCase__ : Tuple = False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
UpperCamelCase__ : Any = True
UpperCamelCase__ : str = int(vit_name[-12:-10] )
UpperCamelCase__ : List[Any] = int(vit_name[-9:-6] )
else:
UpperCamelCase__ : Tuple = 1000
UpperCamelCase__ : Optional[int] = '''huggingface/label-files'''
UpperCamelCase__ : str = '''imagenet-1k-id2label.json'''
UpperCamelCase__ : List[str] = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCamelCase__ : Tuple = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ : List[Any] = idalabel
UpperCamelCase__ : Union[str, Any] = {v: k for k, v in idalabel.items()}
UpperCamelCase__ : Dict = int(vit_name[-6:-4] )
UpperCamelCase__ : Optional[int] = int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith('''tiny''' ):
UpperCamelCase__ : List[str] = 192
UpperCamelCase__ : int = 768
UpperCamelCase__ : Any = 12
UpperCamelCase__ : Optional[int] = 3
elif vit_name[9:].startswith('''small''' ):
UpperCamelCase__ : List[Any] = 384
UpperCamelCase__ : Union[str, Any] = 1536
UpperCamelCase__ : Optional[int] = 12
UpperCamelCase__ : List[Any] = 6
else:
pass
else:
if vit_name[4:].startswith('''small''' ):
UpperCamelCase__ : str = 768
UpperCamelCase__ : int = 2304
UpperCamelCase__ : List[str] = 8
UpperCamelCase__ : Optional[int] = 8
elif vit_name[4:].startswith('''base''' ):
pass
elif vit_name[4:].startswith('''large''' ):
UpperCamelCase__ : Optional[Any] = 1024
UpperCamelCase__ : int = 4096
UpperCamelCase__ : List[Any] = 24
UpperCamelCase__ : Any = 16
elif vit_name[4:].startswith('''huge''' ):
UpperCamelCase__ : Any = 1280
UpperCamelCase__ : int = 5120
UpperCamelCase__ : Union[str, Any] = 32
UpperCamelCase__ : Optional[int] = 16
# load original model from timm
UpperCamelCase__ : Union[str, Any] = timm.create_model(__UpperCAmelCase , pretrained=__UpperCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
UpperCamelCase__ : Dict = timm_model.state_dict()
if base_model:
remove_classification_head_(__UpperCAmelCase )
UpperCamelCase__ : Tuple = create_rename_keys(__UpperCAmelCase , __UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_q_k_v(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# load HuggingFace model
if vit_name[-5:] == "in21k":
UpperCamelCase__ : List[Any] = ViTModel(__UpperCAmelCase ).eval()
else:
UpperCamelCase__ : List[Any] = ViTForImageClassification(__UpperCAmelCase ).eval()
model.load_state_dict(__UpperCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
UpperCamelCase__ : List[Any] = DeiTImageProcessor(size=config.image_size )
else:
UpperCamelCase__ : Any = ViTImageProcessor(size=config.image_size )
UpperCamelCase__ : Optional[int] = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCamelCase__ : Any = encoding['''pixel_values''']
UpperCamelCase__ : Dict = model(__UpperCAmelCase )
if base_model:
UpperCamelCase__ : List[Any] = timm_model.forward_features(__UpperCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(__UpperCAmelCase , outputs.pooler_output , atol=1e-3 )
else:
UpperCamelCase__ : Optional[int] = timm_model(__UpperCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__UpperCAmelCase , outputs.logits , atol=1e-3 )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
UpperCAmelCase_ = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 369 |
from manim import *
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Any = Rectangle(height=0.5, width=0.5 )
UpperCamelCase__ : Any = Rectangle(height=0.25, width=0.25 )
UpperCamelCase__ : Dict = Rectangle(height=0.46, width=0.46 ).set_stroke(width=0 )
UpperCamelCase__ : List[Any] = [mem.copy() for i in range(6 )]
UpperCamelCase__ : Optional[int] = [mem.copy() for i in range(6 )]
UpperCamelCase__ : str = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : List[str] = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Tuple = VGroup(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : List[str] = Text('''CPU''', font_size=24 )
UpperCamelCase__ : Any = Group(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0.5, aligned_edge=__magic_name__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__magic_name__ )
UpperCamelCase__ : int = [mem.copy() for i in range(4 )]
UpperCamelCase__ : str = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Tuple = Text('''GPU''', font_size=24 )
UpperCamelCase__ : str = Group(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0.5, aligned_edge=__magic_name__ )
gpu.move_to([-1, -1, 0] )
self.add(__magic_name__ )
UpperCamelCase__ : str = [mem.copy() for i in range(6 )]
UpperCamelCase__ : Tuple = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Tuple = Text('''Model''', font_size=24 )
UpperCamelCase__ : Tuple = Group(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0.5, aligned_edge=__magic_name__ )
model.move_to([3, -1.0, 0] )
self.add(__magic_name__ )
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Any = []
UpperCamelCase__ : List[Any] = []
for i, rect in enumerate(__magic_name__ ):
rect.set_stroke(__magic_name__ )
UpperCamelCase__ : Tuple = Rectangle(height=0.46 / 4, width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__magic_name__, opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ), buff=0.02, direction=__magic_name__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0], direction=__magic_name__, buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1], direction=__magic_name__, buff=0.0 )
self.add(__magic_name__ )
model_cpu_arr.append(__magic_name__ )
self.add(*__magic_name__, *__magic_name__, *__magic_name__ )
UpperCamelCase__ : Tuple = [mem.copy() for i in range(6 )]
UpperCamelCase__ : Dict = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : List[str] = Text('''Loaded Checkpoint''', font_size=24 )
UpperCamelCase__ : Optional[Any] = Group(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0.5, aligned_edge=__magic_name__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(__magic_name__ )
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : List[str] = []
for i, rect in enumerate(__magic_name__ ):
UpperCamelCase__ : Optional[int] = fill.copy().set_fill(__magic_name__, opacity=0.7 )
target.move_to(__magic_name__ )
ckpt_arr.append(__magic_name__ )
UpperCamelCase__ : int = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__magic_name__ )
self.add(*__magic_name__, *__magic_name__ )
UpperCamelCase__ : List[str] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCamelCase__ : List[str] = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model", font_size=18, )
key_text.move_to([-5, 2.4, 0] )
self.add(__magic_name__, __magic_name__ )
UpperCamelCase__ : Any = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint", font_size=18, )
blue_text.next_to(__magic_name__, DOWN * 2.4, aligned_edge=key_text.get_left() )
self.add(__magic_name__ )
UpperCamelCase__ : Dict = MarkupText(
f"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.", font_size=24, )
step_a.move_to([2, 2, 0] )
UpperCamelCase__ : Any = [meta_mem.copy() for i in range(6 )]
UpperCamelCase__ : int = [meta_mem.copy() for i in range(6 )]
UpperCamelCase__ : Optional[int] = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Union[str, Any] = VGroup(*__magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Union[str, Any] = VGroup(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0 )
UpperCamelCase__ : Any = Text('''Disk''', font_size=24 )
UpperCamelCase__ : List[Any] = Group(__magic_name__, __magic_name__ ).arrange(__magic_name__, buff=0.5, aligned_edge=__magic_name__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(__magic_name__, run_time=3 ), Write(__magic_name__, run_time=1 ), Create(__magic_name__, run_time=1 ) )
UpperCamelCase__ : Union[str, Any] = []
for i, rect in enumerate(__magic_name__ ):
UpperCamelCase__ : Union[str, Any] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__magic_name__, run_time=1.5 ) )
self.play(*__magic_name__ )
self.play(FadeOut(__magic_name__ ) )
UpperCamelCase__ : Optional[int] = MarkupText(f"Then, the checkpoint is removed from memory\nthrough garbage collection.", font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__magic_name__, run_time=3 ) )
self.play(
FadeOut(__magic_name__, __magic_name__, *__magic_name__, *__magic_name__ ), )
self.wait()
| 369 | 1 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def UpperCAmelCase__ ( __snake_case ) -> int:
return EnvironmentCommand()
class _snake_case ( lowerCamelCase ):
"""simple docstring"""
@staticmethod
def lowercase_ ( a ) -> List[Any]:
"""simple docstring"""
_A = parser.add_parser('''env''' )
download_parser.set_defaults(func=a )
def lowercase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
_A = huggingface_hub.__version__
_A = '''not installed'''
_A = '''NA'''
if is_torch_available():
import torch
_A = torch.__version__
_A = torch.cuda.is_available()
_A = '''not installed'''
if is_transformers_available():
import transformers
_A = transformers.__version__
_A = '''not installed'''
if is_accelerate_available():
import accelerate
_A = accelerate.__version__
_A = '''not installed'''
if is_xformers_available():
import xformers
_A = xformers.__version__
_A = {
'''`diffusers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
'''Huggingface_hub version''': hub_version,
'''Transformers version''': transformers_version,
'''Accelerate version''': accelerate_version,
'''xFormers version''': xformers_version,
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(a ) )
return info
@staticmethod
def lowercase_ ( a ) -> Union[str, Any]:
"""simple docstring"""
return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n" | 317 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''' ,'''False''' ) ) is not True ,reason='''Skipping test because should only be run when releasing minor transformers version''' ,)
@pytest.mark.usefixtures('''sm_env''' )
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.g4dn.xlarge''',
'''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
},
] )
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> Dict:
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding='''utf-8''' , check=a , )
assert hasattr(self , '''env''' )
def lowercase_ ( self , a=1 ) -> Optional[int]:
"""simple docstring"""
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f'''{self.env.base_job_name}-single''' , instance_count=a , instance_type=self.instance_type , debugger_hook_config=a , hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version='''py36''' , )
def lowercase_ ( self , a ) -> Any:
"""simple docstring"""
TrainingJobAnalytics(a ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )
def lowercase_ ( self ) -> Optional[int]:
"""simple docstring"""
_A = self.create_estimator()
# run training
estimator.fit()
# result dataframe
_A = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_A = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
_A = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_A = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'''{estimator.latest_training_job.name}.json''' , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , a ) | 317 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase__ ( unittest.TestCase ):
__UpperCamelCase = inspect.getfile(accelerate.test_utils )
__UpperCamelCase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_cli.py"""] )
__UpperCamelCase = ["""accelerate""", """launch"""]
__UpperCamelCase = Path.home() / """.cache/huggingface/accelerate"""
__UpperCamelCase = """default_config.yaml"""
__UpperCamelCase = config_folder / config_file
__UpperCamelCase = config_folder / """_default_config.yaml"""
__UpperCamelCase = Path("""tests/test_configs""" )
@classmethod
def UpperCAmelCase__ ( cls ):
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def UpperCAmelCase__ ( cls ):
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : int = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def UpperCAmelCase__ ( self ):
for config in sorted(self.test_config_path.glob("""**/*.yaml""" ) ):
with self.subTest(config_file=_lowercase ):
execute_subprocess_async(
self.base_cmd + ["""--config_file""", str(_lowercase ), self.test_file_path] , env=os.environ.copy() )
def UpperCAmelCase__ ( self ):
execute_subprocess_async(["""accelerate""", """test"""] , env=os.environ.copy() )
class lowercase__ ( unittest.TestCase ):
__UpperCamelCase = """test-tpu"""
__UpperCamelCase = """us-central1-a"""
__UpperCamelCase = """ls"""
__UpperCamelCase = ["""accelerate""", """tpu-config"""]
__UpperCamelCase = """cd /usr/share"""
__UpperCamelCase = """tests/test_samples/test_command_file.sh"""
__UpperCamelCase = """Running gcloud compute tpus tpu-vm ssh"""
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command""",
self.command,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Optional[Any] = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] , return_stdout=_lowercase )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Tuple = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--command""",
self.command,
"""--command""",
"""echo \"Hello World\"""",
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Optional[Any] = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command_file""",
self.command_file,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Tuple = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Any = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--install_accelerate""",
"""--accelerate_version""",
"""12.0.0""",
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
| 440 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Tuple = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 440 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
UpperCAmelCase = []
create_all_state(1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , [] , SCREAMING_SNAKE_CASE )
return result
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : list[list[int]] , ):
if level == 0:
total_list.append(current_list[:] )
return
for i in range(SCREAMING_SNAKE_CASE , total_number - level + 2 ):
current_list.append(SCREAMING_SNAKE_CASE )
create_all_state(i + 1 , SCREAMING_SNAKE_CASE , level - 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
current_list.pop()
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : list[list[int]] ):
for i in total_list:
print(*SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_a : Dict = 4
_a : Dict = 2
_a : Optional[Any] = generate_all_combinations(n, k)
print_all_state(total_list)
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : Dict = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class lowercase_ ( a ):
'''simple docstring'''
__lowerCAmelCase : List[Any] = "cvt"
def __init__( self , a_=3 , a_=[7, 3, 3] , a_=[4, 2, 2] , a_=[2, 1, 1] , a_=[6_4, 1_9_2, 3_8_4] , a_=[1, 3, 6] , a_=[1, 2, 1_0] , a_=[4.0, 4.0, 4.0] , a_=[0.0, 0.0, 0.0] , a_=[0.0, 0.0, 0.0] , a_=[0.0, 0.0, 0.1] , a_=[True, True, True] , a_=[False, False, True] , a_=["dw_bn", "dw_bn", "dw_bn"] , a_=[3, 3, 3] , a_=[1, 1, 1] , a_=[2, 2, 2] , a_=[1, 1, 1] , a_=[1, 1, 1] , a_=0.02 , a_=1E-12 , **a_ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**a_ )
UpperCAmelCase = num_channels
UpperCAmelCase = patch_sizes
UpperCAmelCase = patch_stride
UpperCAmelCase = patch_padding
UpperCAmelCase = embed_dim
UpperCAmelCase = num_heads
UpperCAmelCase = depth
UpperCAmelCase = mlp_ratio
UpperCAmelCase = attention_drop_rate
UpperCAmelCase = drop_rate
UpperCAmelCase = drop_path_rate
UpperCAmelCase = qkv_bias
UpperCAmelCase = cls_token
UpperCAmelCase = qkv_projection_method
UpperCAmelCase = kernel_qkv
UpperCAmelCase = padding_kv
UpperCAmelCase = stride_kv
UpperCAmelCase = padding_q
UpperCAmelCase = stride_q
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
| 447 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case : Union[str, Any] = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
class _UpperCAmelCase ( lowercase_ , unittest.TestCase ):
UpperCamelCase = BartphoTokenizer
UpperCamelCase = False
UpperCamelCase = True
def lowerCamelCase ( self :Dict ):
super().setUp()
A = ["▁This", "▁is", "▁a", "▁t", "est"]
A = dict(zip(__UpperCamelCase , range(len(__UpperCamelCase ) ) ) )
A = {"unk_token": "<unk>"}
A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["monolingual_vocab_file"] )
with open(self.monolingual_vocab_file , "w" , encoding="utf-8" ) as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n" )
A = BartphoTokenizer(__UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self :List[Any] , **__UpperCamelCase :List[str] ):
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **__UpperCamelCase )
def lowerCamelCase ( self :Union[str, Any] , __UpperCamelCase :int ):
A = "This is a là test"
A = "This is a<unk><unk> test"
return input_text, output_text
def lowerCamelCase ( self :Tuple ):
A = BartphoTokenizer(__UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
A = "This is a là test"
A = "▁This ▁is ▁a ▁l à ▁t est".split()
A = tokenizer.tokenize(__UpperCamelCase )
self.assertListEqual(__UpperCamelCase , __UpperCamelCase )
A = tokens + [tokenizer.unk_token]
A = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , __UpperCamelCase )
| 524 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : Optional[Any] = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
),
}
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = '''van'''
def __init__( self :Optional[int] , __UpperCamelCase :Tuple=2_24 , __UpperCamelCase :Tuple=3 , __UpperCamelCase :int=[7, 3, 3, 3] , __UpperCamelCase :List[str]=[4, 2, 2, 2] , __UpperCamelCase :str=[64, 1_28, 3_20, 5_12] , __UpperCamelCase :Union[str, Any]=[3, 3, 12, 3] , __UpperCamelCase :Dict=[8, 8, 4, 4] , __UpperCamelCase :List[Any]="gelu" , __UpperCamelCase :str=0.02 , __UpperCamelCase :str=1e-6 , __UpperCamelCase :Tuple=1e-2 , __UpperCamelCase :Optional[Any]=0.0 , __UpperCamelCase :List[Any]=0.0 , **__UpperCamelCase :List[str] , ):
super().__init__(**__UpperCamelCase )
A = image_size
A = num_channels
A = patch_sizes
A = strides
A = hidden_sizes
A = depths
A = mlp_ratios
A = hidden_act
A = initializer_range
A = layer_norm_eps
A = layer_scale_init_value
A = drop_path_rate
A = dropout_rate
| 524 | 1 |
"""simple docstring"""
def UpperCAmelCase ( snake_case : int ):
if n_term == "":
return []
_lowerCAmelCase:Dict = []
for temp in range(int(snake_case ) ):
series.append(F'1/{temp + 1}' if series else '''1''' )
return series
if __name__ == "__main__":
UpperCamelCase__ = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 227 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
A__ : List[str] =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__ : Union[str, Any] =cvtColor(img, COLOR_BGR2GRAY)
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = cn.convert_to_negative(lowerCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(lowerCAmelCase , 1_10 ) ).startswith(
"""<PIL.Image.Image image mode=RGB size=100x100 at""" )
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = imread("""digital_image_processing/image_data/lena_small.jpg""" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_lowerCAmelCase = canny.canny(lowerCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
assert gg.gaussian_filter(lowerCAmelCase , 5 , sigma=0.9 ).all()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
_lowerCAmelCase = conv.img_convolve(lowerCAmelCase , lowerCAmelCase ).astype(lowerCAmelCase )
assert res.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
assert med.median_filter(lowerCAmelCase , 3 ).any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = sob.sobel_filter(lowerCAmelCase )
assert grad.any() and theta.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = sp.make_sepia(lowerCAmelCase , 20 )
assert sepia.all()
def UpperCamelCase__ ( lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" ):
"""simple docstring"""
_lowerCAmelCase = bs.Burkes(imread(lowerCAmelCase , 1 ) , 1_20 )
burkes.process()
assert burkes.output_img.any()
def UpperCamelCase__ ( lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" , ):
"""simple docstring"""
_lowerCAmelCase = rs.NearestNeighbour(imread(lowerCAmelCase , 1 ) , 4_00 , 2_00 )
nn.process()
assert nn.output.any()
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = """digital_image_processing/image_data/lena.jpg"""
# Reading the image and converting it to grayscale.
_lowerCAmelCase = imread(lowerCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = image[x_coordinate][y_coordinate]
_lowerCAmelCase = lbp.get_neighbors_pixel(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_lowerCAmelCase = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_lowerCAmelCase = lbp.local_binary_value(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
assert lbp_image.any()
| 207 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''IBertForMaskedLM''',
'''IBertForMultipleChoice''',
'''IBertForQuestionAnswering''',
'''IBertForSequenceClassification''',
'''IBertForTokenClassification''',
'''IBertModel''',
'''IBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 721 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
def __A ( self : Optional[Any] ):
lowerCAmelCase_ : Dict =self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCamelCase_ , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(UpperCamelCase_ , '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(UpperCamelCase_ , '''num_attention_heads''' ) )
class _snake_case :
"""simple docstring"""
def __init__( self : str , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any]=13 , UpperCamelCase_ : Union[str, Any]=32 , UpperCamelCase_ : Dict=2 , UpperCamelCase_ : str=3 , UpperCamelCase_ : int=640 , UpperCamelCase_ : List[Any]=4 , UpperCamelCase_ : int="silu" , UpperCamelCase_ : Tuple=3 , UpperCamelCase_ : Tuple=32 , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : Any=0.1 , UpperCamelCase_ : int=0.1 , UpperCamelCase_ : Any=0.0_2 , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : Any=True , UpperCamelCase_ : List[Any]=10 , UpperCamelCase_ : List[Any]=None , ):
lowerCAmelCase_ : List[str] =parent
lowerCAmelCase_ : Tuple =batch_size
lowerCAmelCase_ : Tuple =image_size
lowerCAmelCase_ : Any =patch_size
lowerCAmelCase_ : Any =num_channels
lowerCAmelCase_ : Dict =last_hidden_size
lowerCAmelCase_ : Optional[int] =num_attention_heads
lowerCAmelCase_ : str =hidden_act
lowerCAmelCase_ : Dict =conv_kernel_size
lowerCAmelCase_ : int =output_stride
lowerCAmelCase_ : Tuple =hidden_dropout_prob
lowerCAmelCase_ : Optional[Any] =attention_probs_dropout_prob
lowerCAmelCase_ : List[str] =classifier_dropout_prob
lowerCAmelCase_ : int =use_labels
lowerCAmelCase_ : Dict =is_training
lowerCAmelCase_ : Any =num_labels
lowerCAmelCase_ : Optional[Any] =initializer_range
lowerCAmelCase_ : List[str] =scope
def __A ( self : int ):
lowerCAmelCase_ : Any =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Any =None
lowerCAmelCase_ : Optional[Any] =None
if self.use_labels:
lowerCAmelCase_ : Optional[int] =ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_ : Optional[Any] =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowerCAmelCase_ : str =self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Optional[Any] ):
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __A ( self : Optional[int] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Any , UpperCamelCase_ : Any ):
lowerCAmelCase_ : Optional[Any] =MobileViTModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
lowerCAmelCase_ : Optional[Any] =model(UpperCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __A ( self : Dict , UpperCamelCase_ : List[str] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : Any ):
lowerCAmelCase_ : List[str] =self.num_labels
lowerCAmelCase_ : Tuple =MobileViTForImageClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
lowerCAmelCase_ : str =model(UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self : Any , UpperCamelCase_ : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : List[str] , UpperCamelCase_ : Tuple ):
lowerCAmelCase_ : Optional[int] =self.num_labels
lowerCAmelCase_ : Any =MobileViTForSemanticSegmentation(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
lowerCAmelCase_ : Union[str, Any] =model(UpperCamelCase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowerCAmelCase_ : Union[str, Any] =model(UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __A ( self : Dict ):
lowerCAmelCase_ : Any =self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : int =config_and_inputs
lowerCAmelCase_ : Tuple ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : Optional[Any] = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
_UpperCamelCase : List[str] = (
{
'''feature-extraction''': MobileViTModel,
'''image-classification''': MobileViTForImageClassification,
'''image-segmentation''': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_UpperCamelCase : str = False
_UpperCamelCase : Any = False
_UpperCamelCase : Dict = False
_UpperCamelCase : Any = False
def __A ( self : List[str] ):
lowerCAmelCase_ : str =MobileViTModelTester(self )
lowerCAmelCase_ : int =MobileViTConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ )
def __A ( self : Dict ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def __A ( self : int ):
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def __A ( self : Optional[int] ):
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def __A ( self : List[str] ):
pass
def __A ( self : Tuple ):
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] =model_class(UpperCamelCase_ )
lowerCAmelCase_ : List[str] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[str] =[*signature.parameters.keys()]
lowerCAmelCase_ : Tuple =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A ( self : Tuple ):
pass
def __A ( self : Optional[int] ):
lowerCAmelCase_ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def __A ( self : Dict ):
def check_hidden_states_output(UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] ):
lowerCAmelCase_ : Union[str, Any] =model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase_ : List[str] =model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
lowerCAmelCase_ : Dict =outputs.hidden_states
lowerCAmelCase_ : Union[str, Any] =5
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
lowerCAmelCase_ : Union[str, Any] =2
for i in range(len(UpperCamelCase_ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Any =True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : List[str] =True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def __A ( self : int ):
lowerCAmelCase_ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
def __A ( self : Optional[Any] ):
lowerCAmelCase_ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_ )
@slow
def __A ( self : List[Any] ):
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Any =MobileViTModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def SCREAMING_SNAKE_CASE__ ( ):
lowerCAmelCase_ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : Dict ):
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def __A ( self : Union[str, Any] ):
lowerCAmelCase_ : Tuple =MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(UpperCamelCase_ )
lowerCAmelCase_ : Optional[Any] =self.default_image_processor
lowerCAmelCase_ : Optional[int] =prepare_img()
lowerCAmelCase_ : Union[str, Any] =image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : int =model(**UpperCamelCase_ )
# verify the logits
lowerCAmelCase_ : Optional[int] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
lowerCAmelCase_ : List[Any] =torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] ).to(UpperCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1E-4 ) )
@slow
def __A ( self : Union[str, Any] ):
lowerCAmelCase_ : List[str] =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCAmelCase_ : Tuple =model.to(UpperCamelCase_ )
lowerCAmelCase_ : Any =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCAmelCase_ : int =prepare_img()
lowerCAmelCase_ : Optional[int] =image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] =model(**UpperCamelCase_ )
lowerCAmelCase_ : List[str] =outputs.logits
# verify the logits
lowerCAmelCase_ : Dict =torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , UpperCamelCase_ )
lowerCAmelCase_ : List[str] =torch.tensor(
[
[[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
[[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
[[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
] , device=UpperCamelCase_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , UpperCamelCase_ , atol=1E-4 ) )
@slow
def __A ( self : Tuple ):
lowerCAmelCase_ : Optional[int] =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCAmelCase_ : str =model.to(UpperCamelCase_ )
lowerCAmelCase_ : int =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
lowerCAmelCase_ : Union[str, Any] =prepare_img()
lowerCAmelCase_ : str =image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : Optional[int] =model(**UpperCamelCase_ )
lowerCAmelCase_ : str =outputs.logits.detach().cpu()
lowerCAmelCase_ : Any =image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ , target_sizes=[(50, 60)] )
lowerCAmelCase_ : Optional[int] =torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , UpperCamelCase_ )
lowerCAmelCase_ : Tuple =image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ )
lowerCAmelCase_ : List[str] =torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , UpperCamelCase_ )
| 305 | 0 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__lowerCamelCase : Optional[int] = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: \"Dataset Card for X\" # First-level markdown heading\n allow_empty: false\n allow_empty_text: true\n subsections:\n - name: \"Table of Contents\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Dataset Description\"\n allow_empty: false\n allow_empty_text: false\n subsections:\n - name: \"Dataset Summary\"\n allow_empty: false\n allow_empty_text: false\n subsections: null\n - name: \"Supported Tasks and Leaderboards\"\n allow_empty: true\n allow_empty_text: true\n subsections: null\n - name: Languages\n allow_empty: false\n allow_empty_text: true\n subsections: null\n"
)
__lowerCamelCase : Dict = {
"name": "root",
"text": "",
"is_empty_text": True,
"subsections": [
{
"name": "Dataset Card for My Dataset",
"text": "",
"is_empty_text": True,
"subsections": [
{"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
{
"name": "Dataset Description",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Dataset Summary",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [],
},
{
"name": "Supported Tasks and Leaderboards",
"text": "",
"is_empty_text": True,
"subsections": [],
},
{"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
],
},
],
}
],
}
__lowerCamelCase : Union[str, Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : Optional[Any] = {
"name": "root",
"text": "",
"is_empty_text": True,
"subsections": [
{
"name": "Dataset Card for My Dataset",
"text": "",
"is_empty_text": True,
"subsections": [
{"name": "Table of Contents", "text": "Some text here.", "is_empty_text": False, "subsections": []},
{
"name": "Dataset Description",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Dataset Summary",
"text": "Some text here.",
"is_empty_text": False,
"subsections": [
{
"name": "Extra Ignored Subsection",
"text": "",
"is_empty_text": True,
"subsections": [],
}
],
},
{
"name": "Supported Tasks and Leaderboards",
"text": "",
"is_empty_text": True,
"subsections": [],
},
{"name": "Languages", "text": "Language Text", "is_empty_text": False, "subsections": []},
],
},
],
}
],
}
__lowerCamelCase : Optional[Any] = "\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : List[str] = (
"The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."
)
__lowerCamelCase : Tuple = "\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : Tuple = (
"The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."
)
__lowerCamelCase : Optional[int] = "\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : Optional[Any] = "The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."
__lowerCamelCase : int = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : Union[str, Any] = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."
__lowerCamelCase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n"
__lowerCamelCase : Union[str, Any] = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."
__lowerCamelCase : Optional[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n"
__lowerCamelCase : Any = "The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."
__lowerCamelCase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n"
__lowerCamelCase : Tuple = "The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."
__lowerCamelCase : Optional[int] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : List[Any] = "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."
__lowerCamelCase : List[Any] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n"
__lowerCamelCase : List[str] = "The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."
__lowerCamelCase : List[str] = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : Dict = "The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."
__lowerCamelCase : Optional[int] = ""
__lowerCamelCase : Dict = "The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."
__lowerCamelCase : Any = "\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n"
__lowerCamelCase : List[Any] = "The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."
@pytest.mark.parametrize(
"readme_md, expected_dict" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> int:
assert ReadMe.from_string(lowerCamelCase_ , lowerCamelCase_ ).to_dict() == expected_dict
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
with pytest.raises(lowerCamelCase_ , match=re.escape(expected_error.format(path="root" ) ) ):
UpperCAmelCase = ReadMe.from_string(lowerCamelCase_ , lowerCamelCase_ )
readme.validate()
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
with pytest.raises(lowerCamelCase_ , match=re.escape(expected_error.format(path="root" ) ) ):
ReadMe.from_string(lowerCamelCase_ , lowerCamelCase_ )
@pytest.mark.parametrize(
"readme_md," , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def lowerCamelCase_(lowerCamelCase_ ) -> Union[str, Any]:
ReadMe.from_string(lowerCamelCase_ , lowerCamelCase_ , suppress_parsing_errors=lowerCamelCase_ )
@pytest.mark.parametrize(
"readme_md, expected_dict" , [
(README_CORRECT, CORRECT_DICT),
(README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase = Path(lowerCamelCase_ ) / "README.md"
with open(lowerCamelCase_ , "w+" ) as readme_file:
readme_file.write(lowerCamelCase_ )
UpperCAmelCase = ReadMe.from_readme(lowerCamelCase_ , lowerCamelCase_ ).to_dict()
assert out["name"] == path
assert out["text"] == ""
assert out["is_empty_text"]
assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
(README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
(README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
(README_EMPTY, EXPECTED_ERROR_README_EMPTY),
(README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
(README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
(README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
(README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
(README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
(README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
(README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase = Path(lowerCamelCase_ ) / "README.md"
with open(lowerCamelCase_ , "w+" ) as readme_file:
readme_file.write(lowerCamelCase_ )
UpperCAmelCase = expected_error.format(path=lowerCamelCase_ )
with pytest.raises(lowerCamelCase_ , match=re.escape(lowerCamelCase_ ) ):
UpperCAmelCase = ReadMe.from_readme(lowerCamelCase_ , lowerCamelCase_ )
readme.validate()
@pytest.mark.parametrize(
"readme_md, expected_error" , [
(README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
] , )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Any:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase = Path(lowerCamelCase_ ) / "README.md"
with open(lowerCamelCase_ , "w+" ) as readme_file:
readme_file.write(lowerCamelCase_ )
UpperCAmelCase = expected_error.format(path=lowerCamelCase_ )
with pytest.raises(lowerCamelCase_ , match=re.escape(lowerCamelCase_ ) ):
ReadMe.from_readme(lowerCamelCase_ , lowerCamelCase_ )
@pytest.mark.parametrize(
"readme_md," , [
(README_MULTIPLE_SAME_HEADING_1),
] , )
def lowerCamelCase_(lowerCamelCase_ ) -> List[str]:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase = Path(lowerCamelCase_ ) / "README.md"
with open(lowerCamelCase_ , "w+" ) as readme_file:
readme_file.write(lowerCamelCase_ )
ReadMe.from_readme(lowerCamelCase_ , lowerCamelCase_ , suppress_parsing_errors=lowerCamelCase_ )
| 323 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__lowerCamelCase : List[Any] = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
__lowerCamelCase : int = subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode("utf-8").split()
__lowerCamelCase : Optional[int] = "|".join(sys.argv[1:])
__lowerCamelCase : Dict = re.compile(rF'''^({joined_dirs}).*?\.py$''')
__lowerCamelCase : List[Any] = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 323 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : int ) ->None:
UpperCAmelCase_ = num_of_nodes
UpperCAmelCase_ = []
UpperCAmelCase_ = {}
def lowerCAmelCase__ ( self : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) ->None:
self.m_edges.append([u_node, v_node, weight] )
def lowerCAmelCase__ ( self : Tuple , UpperCAmelCase__ : int ) ->int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowerCAmelCase__ ( self : int , UpperCAmelCase__ : int ) ->None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
UpperCAmelCase_ = self.find_component(UpperCAmelCase__ )
def lowerCAmelCase__ ( self : Tuple , UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) ->None:
if component_size[u_node] <= component_size[v_node]:
UpperCAmelCase_ = v_node
component_size[v_node] += component_size[u_node]
self.set_component(UpperCAmelCase__ )
elif component_size[u_node] >= component_size[v_node]:
UpperCAmelCase_ = self.find_component(UpperCAmelCase__ )
component_size[u_node] += component_size[v_node]
self.set_component(UpperCAmelCase__ )
def lowerCAmelCase__ ( self : int ) ->None:
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
UpperCAmelCase_ = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = edge
UpperCAmelCase_ = self.m_component[u]
UpperCAmelCase_ = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
UpperCAmelCase_ = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = edge
UpperCAmelCase_ = self.m_component[u]
UpperCAmelCase_ = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
UpperCAmelCase_ = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def __lowerCamelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ : Union[str, Any] = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Union[str, Any] = ["MobileViTFeatureExtractor"]
lowercase__ : List[Any] = ["MobileViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Dict = [
"MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileViTForImageClassification",
"MobileViTForSemanticSegmentation",
"MobileViTModel",
"MobileViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Optional[int] = [
"TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileViTForImageClassification",
"TFMobileViTForSemanticSegmentation",
"TFMobileViTModel",
"TFMobileViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
lowercase__ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 43 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCAmelCase__ ( __magic_name__ , __magic_name__ , __magic_name__ ):
@register_to_config
def __init__( self : List[Any] , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : float , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : str , snake_case__ : bool = False , ):
'''simple docstring'''
super().__init__()
UpperCAmelCase__ : Optional[int] = nn.Embedding(snake_case__ , snake_case__ )
UpperCAmelCase__ : List[Any] = nn.Embedding(snake_case__ , snake_case__ )
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = nn.Dropout(p=snake_case__ )
UpperCAmelCase__ : Optional[int] = TaConfig(
vocab_size=snake_case__ , d_model=snake_case__ , num_heads=snake_case__ , d_kv=snake_case__ , d_ff=snake_case__ , dropout_rate=snake_case__ , feed_forward_proj=snake_case__ , is_decoder=snake_case__ , is_encoder_decoder=snake_case__ , )
UpperCAmelCase__ : Tuple = nn.ModuleList()
for lyr_num in range(snake_case__ ):
UpperCAmelCase__ : Tuple = TaBlock(snake_case__ )
self.encoders.append(snake_case__ )
UpperCAmelCase__ : str = TaLayerNorm(snake_case__ )
UpperCAmelCase__ : Tuple = nn.Dropout(p=snake_case__ )
def __a ( self : int , snake_case__ : List[str] , snake_case__ : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.token_embedder(snake_case__ )
UpperCAmelCase__ : Optional[int] = encoder_input_tokens.shape[1]
UpperCAmelCase__ : List[Any] = torch.arange(snake_case__ , device=encoder_input_tokens.device )
x += self.position_encoding(snake_case__ )
UpperCAmelCase__ : Union[str, Any] = self.dropout_pre(snake_case__ )
# inverted the attention mask
UpperCAmelCase__ : List[Any] = encoder_input_tokens.size()
UpperCAmelCase__ : Tuple = self.get_extended_attention_mask(snake_case__ , snake_case__ )
for lyr in self.encoders:
UpperCAmelCase__ : Any = lyr(snake_case__ , snake_case__ )[0]
UpperCAmelCase__ : Any = self.layer_norm(snake_case__ )
return self.dropout_post(snake_case__ ), encoder_inputs_mask
| 438 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : int )-> str:
'''simple docstring'''
if not isinstance(snake_case , snake_case ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(snake_case , snake_case ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
UpperCAmelCase__ : str = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(snake_case )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 438 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = (CMStochasticIterativeScheduler,)
__snake_case = 1_0
def UpperCamelCase_ ( self , **__lowerCamelCase ) -> str:
_SCREAMING_SNAKE_CASE : Optional[int] = {
"num_train_timesteps": 2_0_1,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
config.update(**__lowerCamelCase )
return config
def UpperCamelCase_ ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = 1_0
_SCREAMING_SNAKE_CASE : int = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : int = self.scheduler_classes[0](**__lowerCamelCase )
scheduler.set_timesteps(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler.timesteps[0]
_SCREAMING_SNAKE_CASE : int = scheduler.timesteps[1]
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_sample
_SCREAMING_SNAKE_CASE : int = 0.1 * sample
_SCREAMING_SNAKE_CASE : List[str] = scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ).prev_sample
_SCREAMING_SNAKE_CASE : str = scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase_ ( self ) -> List[str]:
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[str]:
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : List[Any] = scheduler_class(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = 1
scheduler.set_timesteps(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler.timesteps
_SCREAMING_SNAKE_CASE : int = torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_model()
_SCREAMING_SNAKE_CASE : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(__lowerCamelCase ):
# 1. scale model input
_SCREAMING_SNAKE_CASE : Tuple = scheduler.scale_model_input(__lowerCamelCase , __lowerCamelCase )
# 2. predict noise residual
_SCREAMING_SNAKE_CASE : List[str] = model(__lowerCamelCase , __lowerCamelCase )
# 3. predict previous sample x_t-1
_SCREAMING_SNAKE_CASE : int = scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , generator=__lowerCamelCase ).prev_sample
_SCREAMING_SNAKE_CASE : Dict = pred_prev_sample
_SCREAMING_SNAKE_CASE : List[str] = torch.sum(torch.abs(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : List[str] = torch.mean(torch.abs(__lowerCamelCase ) )
assert abs(result_sum.item() - 192.7614 ) < 1E-2
assert abs(result_mean.item() - 0.2510 ) < 1E-3
def UpperCamelCase_ ( self ) -> int:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE : Tuple = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : int = scheduler_class(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = [1_0_6, 0]
scheduler.set_timesteps(timesteps=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = scheduler.timesteps
_SCREAMING_SNAKE_CASE : int = torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_model()
_SCREAMING_SNAKE_CASE : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
_SCREAMING_SNAKE_CASE : Dict = scheduler.scale_model_input(__lowerCamelCase , __lowerCamelCase )
# 2. predict noise residual
_SCREAMING_SNAKE_CASE : Union[str, Any] = model(__lowerCamelCase , __lowerCamelCase )
# 3. predict previous sample x_t-1
_SCREAMING_SNAKE_CASE : Optional[int] = scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , generator=__lowerCamelCase ).prev_sample
_SCREAMING_SNAKE_CASE : Any = pred_prev_sample
_SCREAMING_SNAKE_CASE : List[str] = torch.sum(torch.abs(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : str = torch.mean(torch.abs(__lowerCamelCase ) )
assert abs(result_sum.item() - 347.6357 ) < 1E-2
assert abs(result_mean.item() - 0.4527 ) < 1E-3
def UpperCamelCase_ ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE : List[str] = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : int = scheduler_class(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = [3_9, 3_0, 1_2, 1_5, 0]
with self.assertRaises(__lowerCamelCase , msg="`timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE : Tuple = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE : List[Any] = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : Any = scheduler_class(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = [3_9, 3_0, 1_2, 1, 0]
_SCREAMING_SNAKE_CASE : Any = len(__lowerCamelCase )
with self.assertRaises(__lowerCamelCase , msg="Can only pass one of `num_inference_steps` or `timesteps`." ):
scheduler.set_timesteps(num_inference_steps=__lowerCamelCase , timesteps=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> str:
_SCREAMING_SNAKE_CASE : Dict = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__lowerCamelCase , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=__lowerCamelCase ) | 381 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ ={
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ =[
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
UpperCamelCase__ =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 381 | 1 |
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