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def __lowercase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case, snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
__magic_name__ :List[Any] = str(abs(snake_case ) )
__magic_name__ :Dict = [list(snake_case ) for char in range(len(snake_case ) )]
for index in range(len(snake_case ) ):
num_transpositions[index].pop(snake_case )
return max(
int(''''''.join(list(snake_case ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __lowerCamelCase (_a ):
_lowercase = ["""image_processor"""]
_lowercase = """SamImageProcessor"""
def __init__( self: Dict,A_: Union[str, Any] ):
'''simple docstring'''
super().__init__(A_ )
__UpperCamelCase = self.image_processor
__UpperCamelCase = -10
__UpperCamelCase = self.image_processor.size['longest_edge']
def __call__( self: Optional[Any],A_: Optional[int]=None,A_: int=None,A_: str=None,A_: str=None,A_: Optional[Union[str, TensorType]] = None,**A_: Optional[int],):
'''simple docstring'''
__UpperCamelCase = self.image_processor(
A_,return_tensors=A_,**A_,)
# pop arguments that are not used in the foward but used nevertheless
__UpperCamelCase = encoding_image_processor['original_sizes']
if hasattr(A_,'numpy' ): # Checks if Torch or TF tensor
__UpperCamelCase = original_sizes.numpy()
__UpperCamelCase, __UpperCamelCase, __UpperCamelCase = self._check_and_preprocess_points(
input_points=A_,input_labels=A_,input_boxes=A_,)
__UpperCamelCase = self._normalize_and_convert(
A_,A_,input_points=A_,input_labels=A_,input_boxes=A_,return_tensors=A_,)
return encoding_image_processor
def snake_case_ ( self: Tuple,A_: Any,A_: str,A_: Dict=None,A_: Dict=None,A_: int=None,A_: List[Any]="pt",):
'''simple docstring'''
if input_points is not None:
if len(A_ ) != len(A_ ):
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,A_,original_sizes[0] ) for point in input_points
]
else:
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,A_,A_ )
for point, original_size in zip(A_,A_ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__UpperCamelCase, __UpperCamelCase = self._pad_points_and_labels(A_,A_ )
__UpperCamelCase = np.array(A_ )
if input_labels is not None:
__UpperCamelCase = np.array(A_ )
if input_boxes is not None:
if len(A_ ) != len(A_ ):
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,A_,original_sizes[0],is_bounding_box=A_ )
for box in input_boxes
]
else:
__UpperCamelCase = [
self._normalize_coordinates(self.target_size,A_,A_,is_bounding_box=A_ )
for box, original_size in zip(A_,A_ )
]
__UpperCamelCase = np.array(A_ )
if input_boxes is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(A_ )
# boxes batch size of 1 by default
__UpperCamelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(A_ )
# boxes batch size of 1 by default
__UpperCamelCase = tf.expand_dims(A_,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'input_boxes': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(A_ )
# point batch size of 1 by default
__UpperCamelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(A_ )
# point batch size of 1 by default
__UpperCamelCase = tf.expand_dims(A_,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'input_points': input_points} )
if input_labels is not None:
if return_tensors == "pt":
__UpperCamelCase = torch.from_numpy(A_ )
# point batch size of 1 by default
__UpperCamelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__UpperCamelCase = tf.convert_to_tensor(A_ )
# point batch size of 1 by default
__UpperCamelCase = tf.expand_dims(A_,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'input_labels': input_labels} )
return encoding_image_processor
def snake_case_ ( self: List[str],A_: int,A_: Optional[Any] ):
'''simple docstring'''
__UpperCamelCase = max([point.shape[0] for point in input_points] )
__UpperCamelCase = []
for i, point in enumerate(A_ ):
if point.shape[0] != expected_nb_points:
__UpperCamelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__UpperCamelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(A_ )
__UpperCamelCase = processed_input_points
return input_points, input_labels
def snake_case_ ( self: Optional[int],A_: int,A_: np.ndarray,A_: Union[str, Any],A_: int=False ):
'''simple docstring'''
__UpperCamelCase, __UpperCamelCase = original_size
__UpperCamelCase, __UpperCamelCase = self.image_processor._get_preprocess_shape(A_,longest_edge=A_ )
__UpperCamelCase = deepcopy(A_ ).astype(A_ )
if is_bounding_box:
__UpperCamelCase = coords.reshape(-1,2,2 )
__UpperCamelCase = coords[..., 0] * (new_w / old_w)
__UpperCamelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__UpperCamelCase = coords.reshape(-1,4 )
return coords
def snake_case_ ( self: Dict,A_: Optional[Any]=None,A_: List[str]=None,A_: Tuple=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(A_,'numpy' ): # Checks for TF or Torch tensor
__UpperCamelCase = input_points.numpy().tolist()
if not isinstance(A_,A_ ) or not isinstance(input_points[0],A_ ):
raise ValueError('Input points must be a list of list of floating points.' )
__UpperCamelCase = [np.array(A_ ) for input_point in input_points]
else:
__UpperCamelCase = None
if input_labels is not None:
if hasattr(A_,'numpy' ):
__UpperCamelCase = input_labels.numpy().tolist()
if not isinstance(A_,A_ ) or not isinstance(input_labels[0],A_ ):
raise ValueError('Input labels must be a list of list integers.' )
__UpperCamelCase = [np.array(A_ ) for label in input_labels]
else:
__UpperCamelCase = None
if input_boxes is not None:
if hasattr(A_,'numpy' ):
__UpperCamelCase = input_boxes.numpy().tolist()
if (
not isinstance(A_,A_ )
or not isinstance(input_boxes[0],A_ )
or not isinstance(input_boxes[0][0],A_ )
):
raise ValueError('Input boxes must be a list of list of list of floating points.' )
__UpperCamelCase = [np.array(A_ ).astype(np.floataa ) for box in input_boxes]
else:
__UpperCamelCase = None
return input_points, input_labels, input_boxes
@property
def snake_case_ ( self: List[str] ):
'''simple docstring'''
__UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(A_ ) )
def snake_case_ ( self: int,*A_: int,**A_: Tuple ):
'''simple docstring'''
return self.image_processor.post_process_masks(*A_,**A_ )
| 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Any = "xlnet"
a__ : Dict = ["mems"]
a__ : List[str] = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , __lowerCAmelCase : Dict=3_20_00 , __lowerCAmelCase : List[str]=10_24 , __lowerCAmelCase : Dict=24 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=40_96 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]="bi" , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Optional[Any]=5_12 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Any="last" , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Tuple="tanh" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : str=5 , __lowerCAmelCase : str=5 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=2 , **__lowerCAmelCase : List[str] , ) -> Tuple:
_A = vocab_size
_A = d_model
_A = n_layer
_A = n_head
if d_model % n_head != 0:
raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __lowerCAmelCase , )
_A = kwargs['''use_cache''']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def snake_case_ ( self : Optional[Any] ) -> Union[str, Any]:
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 snake_case_ ( self : Tuple , __lowerCAmelCase : Optional[Any] ) -> Dict:
# 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.''' )
| 2 | 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 DeformableDetrImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
def __init__( self , A_ , A_=7 , A_=3 , A_=30 , A_=400 , A_=True , A_=None , A_=True , A_=[0.5, 0.5, 0.5] , A_=[0.5, 0.5, 0.5] , A_=True , A_=1 / 255 , A_=True , )-> int:
'''simple docstring'''
UpperCamelCase = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_pad
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
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 , A_ , A_=False )-> Union[str, Any]:
'''simple docstring'''
if not batched:
UpperCamelCase = image_inputs[0]
if isinstance(A_ , Image.Image ):
UpperCamelCase , UpperCamelCase = image.size
else:
UpperCamelCase , UpperCamelCase = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase = int(self.size['shortest_edge'] * h / w )
UpperCamelCase = self.size['shortest_edge']
elif w > h:
UpperCamelCase = self.size['shortest_edge']
UpperCamelCase = int(self.size['shortest_edge'] * w / h )
else:
UpperCamelCase = self.size['shortest_edge']
UpperCamelCase = self.size['shortest_edge']
else:
UpperCamelCase = []
for image in image_inputs:
UpperCamelCase , UpperCamelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase = max(A_ , key=lambda A_ : item[0] )[0]
UpperCamelCase = max(A_ , key=lambda A_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( snake_case_ , unittest.TestCase):
lowerCAmelCase_ = DeformableDetrImageProcessor if is_vision_available() else None
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase = DeformableDetrImageProcessingTester(self )
@property
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_ , 'image_mean' ) )
self.assertTrue(hasattr(A_ , 'image_std' ) )
self.assertTrue(hasattr(A_ , 'do_normalize' ) )
self.assertTrue(hasattr(A_ , 'do_resize' ) )
self.assertTrue(hasattr(A_ , 'do_rescale' ) )
self.assertTrue(hasattr(A_ , 'do_pad' ) )
self.assertTrue(hasattr(A_ , 'size' ) )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase = 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 , A_ )
UpperCamelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=A_ )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , A_ )
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , Image.Image )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
UpperCamelCase = image_processing(A_ , 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 )-> List[Any]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , np.ndarray )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(A_ , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(A_ , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
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:
'''simple docstring'''
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'image_id': 39769, 'annotations': target}
# encode them
UpperCamelCase = DeformableDetrImageProcessor()
UpperCamelCase = image_processing(images=A_ , annotations=A_ , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , A_ )
UpperCamelCase = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , A_ , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , A_ ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , A_ )
UpperCamelCase = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , A_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , A_ ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , A_ ) )
# verify class_labels
UpperCamelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , A_ ) )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , A_ ) )
# verify size
UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , A_ ) )
@slow
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}
UpperCamelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
UpperCamelCase = DeformableDetrImageProcessor(format='coco_panoptic' )
UpperCamelCase = image_processing(images=A_ , annotations=A_ , masks_path=A_ , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , A_ )
UpperCamelCase = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , A_ , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , A_ ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , A_ )
UpperCamelCase = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , A_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , A_ ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , A_ ) )
# verify class_labels
UpperCamelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , A_ ) )
# verify masks
UpperCamelCase = 822873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , A_ )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , A_ ) )
# verify size
UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , A_ ) )
| 3 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(_snake_case ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_snake_case ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_snake_case ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(_UpperCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(_UpperCAmelCase , _UpperCAmelCase ).lstrip('./' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Union[str, Any] ):
return F'{i * " "}*' if i else "\n##"
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : str ):
lowerCAmelCase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(_UpperCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(_UpperCAmelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
lowerCAmelCase = ''
for filepath in sorted(good_file_paths(_UpperCAmelCase ) ):
lowerCAmelCase ,lowerCAmelCase = os.path.split(_UpperCAmelCase )
if filepath != old_path:
lowerCAmelCase = print_path(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = (filepath.count(os.sep ) + 1) if filepath else 0
lowerCAmelCase = F'{filepath}/{filename}'.replace(' ' , '%20' )
lowerCAmelCase = os.path.splitext(filename.replace('_' , ' ' ).title() )[0]
print(F'{md_prefix(_UpperCAmelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md('''.''')
| 4 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_snake_case ) == 1:
return True
_A = series[1] - series[0]
for index in range(len(_snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> float:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
_A = 0
for val in series:
answer += val
return answer / len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :list[int | float] , __lowerCamelCase :int , __lowerCamelCase :int ):
if len(__lowerCamelCase ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(__lowerCamelCase )
or left < -len(__lowerCamelCase )
or right >= len(__lowerCamelCase )
or right < -len(__lowerCamelCase )
):
raise IndexError("""list index out of range""" )
if left == right:
return nums[left]
_lowerCAmelCase = (left + right) >> 1 # the middle
_lowerCAmelCase = find_max(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) # find max in range[left, mid]
_lowerCAmelCase = find_max(__lowerCamelCase , mid + 1 , __lowerCamelCase ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 5 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_snake_case ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
_A = QuantumRegister(_snake_case , '''qr''' )
_A = ClassicalRegister(_snake_case , '''cr''' )
_A = QuantumCircuit(_snake_case , _snake_case )
_A = number_of_qubits
for i in range(_snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _snake_case , _snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_snake_case , _snake_case )
# simulate with 10000 shots
_A = Aer.get_backend('''qasm_simulator''' )
_A = execute(_snake_case , _snake_case , shots=10_000 )
return job.result().get_counts(_snake_case )
if __name__ == "__main__":
print(
f'Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'
)
| 2 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
_lowerCamelCase = logging.get_logger(__name__)
# General docstring
_lowerCamelCase = 'PoolFormerConfig'
# Base docstring
_lowerCamelCase = 'sail/poolformer_s12'
_lowerCamelCase = [1, 512, 7, 7]
# Image classification docstring
_lowerCamelCase = 'sail/poolformer_s12'
_lowerCamelCase = 'tabby, tabby cat'
_lowerCamelCase = [
'sail/poolformer_s12',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Optional[Any] , UpperCamelCase__: float = 0.0 , UpperCamelCase__: bool = False ):
if drop_prob == 0.0 or not training:
return input
SCREAMING_SNAKE_CASE__ = 1 - drop_prob
SCREAMING_SNAKE_CASE__ = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
SCREAMING_SNAKE_CASE__ = keep_prob + torch.rand(UpperCamelCase__ , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
SCREAMING_SNAKE_CASE__ = input.div(UpperCamelCase__ ) * random_tensor
return output
class UpperCamelCase_ ( nn.Module ):
def __init__( self :Optional[Any] , __A :Optional[float] = None ) -> None:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = drop_prob
def _snake_case ( self :Any , __A :torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
return drop_path(__A , self.drop_prob , self.training )
def _snake_case ( self :Dict ) -> str:
"""simple docstring"""
return "p={}".format(self.drop_prob )
class UpperCamelCase_ ( nn.Module ):
def __init__( self :Dict , __A :Optional[Any] , __A :Dict , __A :List[str] , __A :Optional[Any] , __A :Tuple , __A :Optional[Any]=None ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = patch_size if isinstance(__A , collections.abc.Iterable ) else (patch_size, patch_size)
SCREAMING_SNAKE_CASE__ = stride if isinstance(__A , collections.abc.Iterable ) else (stride, stride)
SCREAMING_SNAKE_CASE__ = padding if isinstance(__A , collections.abc.Iterable ) else (padding, padding)
SCREAMING_SNAKE_CASE__ = nn.Convad(__A , __A , kernel_size=__A , stride=__A , padding=__A )
SCREAMING_SNAKE_CASE__ = norm_layer(__A ) if norm_layer else nn.Identity()
def _snake_case ( self :Dict , __A :Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.projection(__A )
SCREAMING_SNAKE_CASE__ = self.norm(__A )
return embeddings
class UpperCamelCase_ ( nn.GroupNorm ):
def __init__( self :Dict , __A :Tuple , **__A :Union[str, Any] ) -> Dict:
"""simple docstring"""
super().__init__(1 , __A , **__A )
class UpperCamelCase_ ( nn.Module ):
def __init__( self :List[str] , __A :Optional[int] ) -> Any:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = nn.AvgPoolad(__A , stride=1 , padding=pool_size // 2 , count_include_pad=__A )
def _snake_case ( self :Any , __A :Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return self.pool(__A ) - hidden_states
class UpperCamelCase_ ( nn.Module ):
def __init__( self :Optional[Any] , __A :Tuple , __A :Dict , __A :int , __A :Any ) -> str:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = nn.Convad(__A , __A , 1 )
SCREAMING_SNAKE_CASE__ = nn.Convad(__A , __A , 1 )
SCREAMING_SNAKE_CASE__ = PoolFormerDropPath(__A )
if isinstance(config.hidden_act , __A ):
SCREAMING_SNAKE_CASE__ = ACTaFN[config.hidden_act]
else:
SCREAMING_SNAKE_CASE__ = config.hidden_act
def _snake_case ( self :Union[str, Any] , __A :Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.conva(__A )
SCREAMING_SNAKE_CASE__ = self.act_fn(__A )
SCREAMING_SNAKE_CASE__ = self.drop(__A )
SCREAMING_SNAKE_CASE__ = self.conva(__A )
SCREAMING_SNAKE_CASE__ = self.drop(__A )
return hidden_states
class UpperCamelCase_ ( nn.Module ):
def __init__( self :Any , __A :str , __A :List[str] , __A :Tuple , __A :Dict , __A :Union[str, Any] , __A :int ) -> Optional[int]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = PoolFormerPooling(__A )
SCREAMING_SNAKE_CASE__ = PoolFormerOutput(__A , __A , __A , __A )
SCREAMING_SNAKE_CASE__ = PoolFormerGroupNorm(__A )
SCREAMING_SNAKE_CASE__ = PoolFormerGroupNorm(__A )
# Useful for training neural nets
SCREAMING_SNAKE_CASE__ = PoolFormerDropPath(__A ) if drop_path > 0.0 else nn.Identity()
SCREAMING_SNAKE_CASE__ = config.use_layer_scale
if config.use_layer_scale:
SCREAMING_SNAKE_CASE__ = nn.Parameter(
config.layer_scale_init_value * torch.ones((__A) ) , requires_grad=__A )
SCREAMING_SNAKE_CASE__ = nn.Parameter(
config.layer_scale_init_value * torch.ones((__A) ) , requires_grad=__A )
def _snake_case ( self :Optional[Any] , __A :Optional[int] ) -> str:
"""simple docstring"""
if self.use_layer_scale:
SCREAMING_SNAKE_CASE__ = self.pooling(self.before_norm(__A ) )
SCREAMING_SNAKE_CASE__ = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
SCREAMING_SNAKE_CASE__ = hidden_states + self.drop_path(__A )
SCREAMING_SNAKE_CASE__ = ()
SCREAMING_SNAKE_CASE__ = self.output(self.after_norm(__A ) )
SCREAMING_SNAKE_CASE__ = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
SCREAMING_SNAKE_CASE__ = hidden_states + self.drop_path(__A )
SCREAMING_SNAKE_CASE__ = (output,) + outputs
return outputs
else:
SCREAMING_SNAKE_CASE__ = self.drop_path(self.pooling(self.before_norm(__A ) ) )
# First residual connection
SCREAMING_SNAKE_CASE__ = pooling_output + hidden_states
SCREAMING_SNAKE_CASE__ = ()
# Second residual connection inside the PoolFormerOutput block
SCREAMING_SNAKE_CASE__ = self.drop_path(self.output(self.after_norm(__A ) ) )
SCREAMING_SNAKE_CASE__ = hidden_states + layer_output
SCREAMING_SNAKE_CASE__ = (output,) + outputs
return outputs
class UpperCamelCase_ ( nn.Module ):
def __init__( self :Union[str, Any] , __A :List[Any] ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = config
# stochastic depth decay rule
SCREAMING_SNAKE_CASE__ = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
SCREAMING_SNAKE_CASE__ = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
SCREAMING_SNAKE_CASE__ = nn.ModuleList(__A )
# Transformer blocks
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
SCREAMING_SNAKE_CASE__ = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
__A , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(__A ) )
SCREAMING_SNAKE_CASE__ = nn.ModuleList(__A )
def _snake_case ( self :str , __A :Tuple , __A :Dict=False , __A :Tuple=True ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = () if output_hidden_states else None
SCREAMING_SNAKE_CASE__ = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = layers
# Get patch embeddings from hidden_states
SCREAMING_SNAKE_CASE__ = embedding_layer(__A )
# Send the embeddings through the blocks
for _, blk in enumerate(__A ):
SCREAMING_SNAKE_CASE__ = blk(__A )
SCREAMING_SNAKE_CASE__ = layer_outputs[0]
if output_hidden_states:
SCREAMING_SNAKE_CASE__ = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__A , hidden_states=__A )
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = PoolFormerConfig
lowerCamelCase_ = "poolformer"
lowerCamelCase_ = "pixel_values"
lowerCamelCase_ = True
def _snake_case ( self :Optional[Any] , __A :Tuple ) -> Dict:
"""simple docstring"""
if isinstance(__A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__A , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def _snake_case ( self :str , __A :Optional[Any] , __A :Union[str, Any]=False ) -> Any:
"""simple docstring"""
if isinstance(__A , __A ):
SCREAMING_SNAKE_CASE__ = value
_lowerCamelCase = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
_lowerCamelCase = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n'
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , UpperCamelCase__ , )
class UpperCamelCase_ ( UpperCamelCase__ ):
def __init__( self :Union[str, Any] , __A :Any ) -> int:
"""simple docstring"""
super().__init__(__A )
SCREAMING_SNAKE_CASE__ = config
SCREAMING_SNAKE_CASE__ = PoolFormerEncoder(__A )
# Initialize weights and apply final processing
self.post_init()
def _snake_case ( self :Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(__A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__A , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _snake_case ( self :Dict , __A :Optional[torch.FloatTensor] = None , __A :Optional[bool] = None , __A :Optional[bool] = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""" )
SCREAMING_SNAKE_CASE__ = self.encoder(
__A , output_hidden_states=__A , return_dict=__A , )
SCREAMING_SNAKE_CASE__ = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=__A , hidden_states=encoder_outputs.hidden_states , )
class UpperCamelCase_ ( nn.Module ):
def __init__( self :int , __A :Optional[int] ) -> Tuple:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ = nn.Linear(config.hidden_size , config.hidden_size )
def _snake_case ( self :List[Any] , __A :Dict ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.dense(__A )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " , UpperCamelCase__ , )
class UpperCamelCase_ ( UpperCamelCase__ ):
def __init__( self :str , __A :Union[str, Any] ) -> int:
"""simple docstring"""
super().__init__(__A )
SCREAMING_SNAKE_CASE__ = config.num_labels
SCREAMING_SNAKE_CASE__ = PoolFormerModel(__A )
# Final norm
SCREAMING_SNAKE_CASE__ = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
SCREAMING_SNAKE_CASE__ = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _snake_case ( self :int , __A :Optional[torch.FloatTensor] = None , __A :Optional[torch.LongTensor] = None , __A :Optional[bool] = None , __A :Optional[bool] = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ = self.poolformer(
__A , output_hidden_states=__A , return_dict=__A , )
SCREAMING_SNAKE_CASE__ = outputs[0]
SCREAMING_SNAKE_CASE__ = self.classifier(self.norm(__A ).mean([-2, -1] ) )
SCREAMING_SNAKE_CASE__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
SCREAMING_SNAKE_CASE__ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
SCREAMING_SNAKE_CASE__ = """single_label_classification"""
else:
SCREAMING_SNAKE_CASE__ = """multi_label_classification"""
if self.config.problem_type == "regression":
SCREAMING_SNAKE_CASE__ = MSELoss()
if self.num_labels == 1:
SCREAMING_SNAKE_CASE__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
SCREAMING_SNAKE_CASE__ = loss_fct(__A , __A )
elif self.config.problem_type == "single_label_classification":
SCREAMING_SNAKE_CASE__ = CrossEntropyLoss()
SCREAMING_SNAKE_CASE__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
SCREAMING_SNAKE_CASE__ = BCEWithLogitsLoss()
SCREAMING_SNAKE_CASE__ = loss_fct(__A , __A )
if not return_dict:
SCREAMING_SNAKE_CASE__ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__A , logits=__A , hidden_states=outputs.hidden_states ) | 6 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_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_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :str , _snake_case :Any , _snake_case :int , _snake_case :List[Any] ) -> Optional[int]:
for attribute in key.split('''.''' ):
_A = getattr(_snake_case , _snake_case )
if weight_type is not None:
_A = getattr(_snake_case , _snake_case ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :Any , _snake_case :int ) -> Any:
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_snake_case )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "weight" in name:
_A = '''weight'''
elif "bias" in name:
_A = '''bias'''
else:
_A = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :List[str] , _snake_case :List[str] , _snake_case :Optional[int] , _snake_case :List[Any] ) -> Any:
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = 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.'''
)
_A = 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.'''
)
_A = 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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Tuple:
_A = SEWConfig()
if is_finetuned:
_A = model.wav_encoder.wav_model.cfg
else:
_A = model.cfg
_A = fs_config.conv_bias
_A = eval(fs_config.conv_feature_layers )
_A = [x[0] for x in conv_layers]
_A = [x[1] for x in conv_layers]
_A = [x[2] for x in conv_layers]
_A = '''gelu'''
_A = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_A = 0.0
_A = fs_config.activation_fn.name
_A = fs_config.encoder_embed_dim
_A = 0.02
_A = fs_config.encoder_ffn_embed_dim
_A = 1E-5
_A = fs_config.encoder_layerdrop
_A = fs_config.encoder_attention_heads
_A = fs_config.conv_pos_groups
_A = fs_config.conv_pos
_A = len(_snake_case )
_A = fs_config.encoder_layers
_A = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_A = model.cfg
_A = fs_config.final_dropout
_A = fs_config.layerdrop
_A = fs_config.activation_dropout
_A = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_A = fs_config.attention_dropout
_A = fs_config.dropout_input
_A = fs_config.dropout
_A = fs_config.mask_channel_length
_A = fs_config.mask_channel_prob
_A = fs_config.mask_length
_A = fs_config.mask_prob
_A = '''Wav2Vec2FeatureExtractor'''
_A = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] , _snake_case :Union[str, Any] , _snake_case :Optional[Any]=None , _snake_case :Optional[int]=None , _snake_case :Dict=True ) -> List[Any]:
if is_finetuned:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_A = SEWConfig.from_pretrained(_snake_case )
else:
_A = convert_config(model[0] , _snake_case )
_A = model[0].eval()
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
if is_finetuned:
if dict_path:
_A = Dictionary.load(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
_A = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_snake_case , )
_A = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
_A = SEWForCTC(_snake_case )
else:
_A = SEWModel(_snake_case )
feature_extractor.save_pretrained(_snake_case )
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_model.save_pretrained(_snake_case )
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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 2 | 0 |
"""simple docstring"""
# 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
a = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
a = (
subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('''utf-8''').split()
)
a = '''|'''.join(sys.argv[1:])
a = re.compile(rF'''^({joined_dirs}).*?\.py$''')
a = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 7 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Any ) -> Any:
pass
@is_pipeline_test
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@require_torch
def snake_case_ ( self : Tuple ) -> Tuple:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCAmelCase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : int ) -> Optional[int]:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> int:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def snake_case_ ( self : Optional[int] ) -> Dict:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 2 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : Dict ) -> Tuple:
__A : Any = [0] * len(__snake_case )
__A : Dict = []
__A : List[Any] = [1] * len(__snake_case )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__snake_case ) ):
if indegree[i] == 0:
queue.append(__snake_case )
while queue:
__A : Tuple = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A : Any = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__snake_case )
print(max(__snake_case ) )
# Adjacency list of Graph
lowercase__ : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 8 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : Tuple ) -> Optional[int]:
_A = tempfile.mkdtemp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_A = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Dict , **__lowerCAmelCase : int ) -> Optional[int]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : str , **__lowerCAmelCase : Optional[Any] ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Tuple , **__lowerCAmelCase : str ) -> Union[str, Any]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def snake_case_ ( self : int ) -> Optional[Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ ( self : Dict ) -> List[str]:
_A = self.get_tokenizer()
_A = self.get_rust_tokenizer()
_A = self.get_image_processor()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase )
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __lowerCAmelCase )
def snake_case_ ( self : List[Any] ) -> List[str]:
_A = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
_A = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(__lowerCAmelCase , return_tensors='''np''' )
_A = processor(images=__lowerCAmelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = processor(text=__lowerCAmelCase )
_A = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ ( self : List[str] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def snake_case_ ( self : Optional[Any] ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__lowerCAmelCase )
_A = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 2 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
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 (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self : List[str] ):
"""simple docstring"""
A__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_snake_case , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(_snake_case , 'num_attention_heads' ) )
self.parent.assertTrue(hasattr(_snake_case , 'num_encoder_blocks' ) )
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any , _snake_case : str , _snake_case : Union[str, Any]=13 , _snake_case : Any=64 , _snake_case : Optional[Any]=3 , _snake_case : Dict=4 , _snake_case : Tuple=[2, 2, 2, 2] , _snake_case : str=[8, 4, 2, 1] , _snake_case : Union[str, Any]=[16, 32, 64, 1_28] , _snake_case : int=[1, 4, 8, 16] , _snake_case : List[str]=[1, 2, 4, 8] , _snake_case : int=True , _snake_case : int=True , _snake_case : Union[str, Any]="gelu" , _snake_case : Optional[int]=0.1 , _snake_case : Tuple=0.1 , _snake_case : Dict=0.02 , _snake_case : Tuple=3 , _snake_case : int=None , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = num_encoder_blocks
A__ = sr_ratios
A__ = depths
A__ = hidden_sizes
A__ = downsampling_rates
A__ = num_attention_heads
A__ = is_training
A__ = use_labels
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = initializer_range
A__ = num_labels
A__ = scope
def _a ( self : 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.image_size, self.image_size] , self.num_labels )
A__ = self.get_config()
return config, pixel_values, labels
def _a ( self : int ):
"""simple docstring"""
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def _a ( self : int , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Any ):
"""simple docstring"""
A__ = SegformerModel(config=_snake_case )
model.to(_snake_case )
model.eval()
A__ = model(_snake_case )
A__ = A__ = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def _a ( self : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : Tuple , _snake_case : Dict ):
"""simple docstring"""
A__ = self.num_labels
A__ = SegformerForSemanticSegmentation(_snake_case )
model.to(_snake_case )
model.eval()
A__ = model(_snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
A__ = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def _a ( self : List[str] , _snake_case : Optional[Any] , _snake_case : Union[str, Any] , _snake_case : List[str] ):
"""simple docstring"""
A__ = 1
A__ = SegformerForSemanticSegmentation(config=_snake_case )
model.to(_snake_case )
model.eval()
A__ = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(_snake_case )
A__ = model(_snake_case , labels=_snake_case )
self.parent.assertGreater(result.loss , 0.0 )
def _a ( self : List[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 __lowerCAmelCase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
A__ : Optional[int] = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
A__ : Union[str, Any] = (
{
"feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
A__ : Optional[Any] = True
A__ : str = False
A__ : Tuple = False
A__ : Dict = False
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A__ = SegformerModelTester(self )
A__ = SegformerConfigTester(self , config_class=_snake_case )
def _a ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def _a ( self : List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*_snake_case )
def _a ( self : Tuple ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*_snake_case )
@unittest.skip('SegFormer does not use inputs_embeds' )
def _a ( self : List[Any] ):
"""simple docstring"""
pass
@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods' )
def _a ( self : Dict ):
"""simple docstring"""
pass
def _a ( 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(_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] , _snake_case )
def _a ( self : Dict ):
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = True
A__ = False
A__ = True
A__ = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(_snake_case , _snake_case ) )
A__ = outputs.attentions
A__ = sum(self.model_tester.depths )
self.assertEqual(len(_snake_case ) , _snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(_snake_case , _snake_case ) )
A__ = outputs.attentions
self.assertEqual(len(_snake_case ) , _snake_case )
# verify the first attentions (first block, first layer)
A__ = (self.model_tester.image_size // 4) ** 2
A__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
A__ = (self.model_tester.image_size // 32) ** 2
A__ = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
A__ = len(_snake_case )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(_snake_case , _snake_case ) )
self.assertEqual(out_len + 1 , len(_snake_case ) )
A__ = outputs.attentions
self.assertEqual(len(_snake_case ) , _snake_case )
# verify the first attentions (first block, first layer)
A__ = (self.model_tester.image_size // 4) ** 2
A__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
def check_hidden_states_output(_snake_case : Dict , _snake_case : int , _snake_case : List[Any] ):
A__ = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(_snake_case , _snake_case ) )
A__ = outputs.hidden_states
A__ = self.model_tester.num_encoder_blocks
self.assertEqual(len(_snake_case ) , _snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = 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"]
A__ = True
check_hidden_states_output(_snake_case , _snake_case , _snake_case )
def _a ( self : Tuple ):
"""simple docstring"""
if not self.model_tester.is_training:
return
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
if model_class in get_values(_snake_case ):
continue
A__ = model_class(_snake_case )
model.to(_snake_case )
model.train()
A__ = self._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
A__ = model(**_snake_case ).loss
loss.backward()
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _a ( self : Optional[Any] ):
"""simple docstring"""
pass
@slow
def _a ( self : Tuple ):
"""simple docstring"""
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = SegformerModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def A ( ) -> str:
A__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self : Dict ):
"""simple docstring"""
A__ = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=_snake_case , align=_snake_case , do_random_crop=_snake_case )
A__ = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
_snake_case )
A__ = prepare_img()
A__ = image_processor(images=_snake_case , return_tensors='pt' )
A__ = encoded_inputs.pixel_values.to(_snake_case )
with torch.no_grad():
A__ = model(_snake_case )
A__ = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape , _snake_case )
A__ = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _snake_case , atol=1E-4 ) )
@slow
def _a ( self : Optional[Any] ):
"""simple docstring"""
A__ = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=_snake_case , align=_snake_case , do_random_crop=_snake_case )
A__ = SegformerForSemanticSegmentation.from_pretrained(
'nvidia/segformer-b1-finetuned-cityscapes-1024-1024' ).to(_snake_case )
A__ = prepare_img()
A__ = image_processor(images=_snake_case , return_tensors='pt' )
A__ = encoded_inputs.pixel_values.to(_snake_case )
with torch.no_grad():
A__ = model(_snake_case )
A__ = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape , _snake_case )
A__ = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _snake_case , atol=1E-1 ) )
@slow
def _a ( self : Any ):
"""simple docstring"""
A__ = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=_snake_case , align=_snake_case , do_random_crop=_snake_case )
A__ = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
_snake_case )
A__ = prepare_img()
A__ = image_processor(images=_snake_case , return_tensors='pt' )
A__ = encoded_inputs.pixel_values.to(_snake_case )
with torch.no_grad():
A__ = model(_snake_case )
A__ = outputs.logits.detach().cpu()
A__ = image_processor.post_process_semantic_segmentation(outputs=_snake_case , target_sizes=[(5_00, 3_00)] )
A__ = torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape , _snake_case )
A__ = image_processor.post_process_semantic_segmentation(outputs=_snake_case )
A__ = torch.Size((1_28, 1_28) )
self.assertEqual(segmentation[0].shape , _snake_case )
| 9 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = "openai-gpt"
a__ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Union[str, Any] , __lowerCAmelCase : int=4_04_78 , __lowerCAmelCase : Tuple=5_12 , __lowerCAmelCase : str=7_68 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=1E-5 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Optional[Any]="cls_index" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=0.1 , **__lowerCAmelCase : Tuple , ) -> Optional[Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**__lowerCAmelCase )
| 2 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
_lowerCAmelCase = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
_lowerCAmelCase = (
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
)
_lowerCAmelCase = (
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
)
_lowerCAmelCase = [
"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 ( __snake_case , __snake_case ):
for tf_name, hf_name in patterns:
_UpperCamelCase = k.replace(__snake_case , __snake_case )
return k
def _snake_case ( __snake_case , __snake_case ):
_UpperCamelCase = BigBirdPegasusConfig(**__snake_case )
_UpperCamelCase = BigBirdPegasusForConditionalGeneration(__snake_case )
_UpperCamelCase = torch_model.state_dict()
_UpperCamelCase = {}
# separating decoder weights
_UpperCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
_UpperCamelCase = {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''' ):
_UpperCamelCase = [k.endswith(__snake_case ) for ending in KEYS_TO_IGNORE]
if any(__snake_case ):
continue
_UpperCamelCase = DECODER_PATTERNS
_UpperCamelCase = rename_state_dict_key(__snake_case , __snake_case )
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'''] ):
_UpperCamelCase = v.T
_UpperCamelCase = torch.from_numpy(__snake_case )
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''' ):
_UpperCamelCase = [k.endswith(__snake_case ) for ending in KEYS_TO_IGNORE]
if any(__snake_case ):
continue
_UpperCamelCase = REMAINING_PATTERNS
_UpperCamelCase = rename_state_dict_key(__snake_case , __snake_case )
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'''] ):
_UpperCamelCase = v.T
_UpperCamelCase = torch.from_numpy(__snake_case )
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}"""
_UpperCamelCase = mapping['''model.embed_positions.weight''']
_UpperCamelCase = mapping.pop('''model.embed_positions.weight''' )
_UpperCamelCase , _UpperCamelCase = torch_model.load_state_dict(__snake_case , strict=__snake_case )
_UpperCamelCase = [
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 ( __snake_case ):
_UpperCamelCase = tf.train.list_variables(__snake_case )
_UpperCamelCase = {}
_UpperCamelCase = ['''global_step''']
for name, shape in tqdm(__snake_case , desc='''converting tf checkpoint to dict''' ):
_UpperCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
_UpperCamelCase = tf.train.load_variable(__snake_case , __snake_case )
_UpperCamelCase = array
return tf_weights
def _snake_case ( __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = get_tf_weights_as_numpy(__snake_case )
_UpperCamelCase = convert_bigbird_pegasus(__snake_case , __snake_case )
torch_model.save_pretrained(__snake_case )
if __name__ == "__main__":
_lowerCAmelCase = 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.")
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 10 |
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 DeformableDetrImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : int=30 , __lowerCAmelCase : Dict=4_00 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=1 / 2_55 , __lowerCAmelCase : int=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
_A = parent
_A = batch_size
_A = num_channels
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
_A = do_rescale
_A = rescale_factor
_A = do_pad
def snake_case_ ( self : Optional[int] ) -> Any:
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 snake_case_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False ) -> Dict:
if not batched:
_A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
_A , _A = image.size
else:
_A , _A = image.shape[1], image.shape[2]
if w < h:
_A = int(self.size['''shortest_edge'''] * h / w )
_A = self.size['''shortest_edge''']
elif w > h:
_A = self.size['''shortest_edge''']
_A = int(self.size['''shortest_edge'''] * w / h )
else:
_A = self.size['''shortest_edge''']
_A = self.size['''shortest_edge''']
else:
_A = []
for image in image_inputs:
_A , _A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase__ ( _A , unittest.TestCase):
"""simple docstring"""
a__ : Any = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case_ ( self : Optional[int] ) -> Any:
_A = DeformableDetrImageProcessingTester(self )
@property
def snake_case_ ( self : Union[str, Any] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = 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 , '''do_rescale''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def snake_case_ ( self : List[str] ) -> int:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
_A = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def snake_case_ ( self : Any ) -> Union[str, Any]:
pass
def snake_case_ ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
_A = image_processing(__lowerCAmelCase , 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 snake_case_ ( self : Tuple ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
# prepare image and target
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
_A = DeformableDetrImageProcessor()
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def snake_case_ ( self : List[str] ) -> List[str]:
# prepare image, target and masks_path
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
_A = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_A = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
_A = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 2 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCAmelCase ():
"""simple docstring"""
assert xnor_gate(0 , 0) == 1
assert xnor_gate(0 , 1) == 0
assert xnor_gate(1 , 0) == 0
assert xnor_gate(1 , 1) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 11 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
lowerCamelCase__ : Optional[Any] = open # noqa: we just need to have a builtin inside this module to test it properly
| 12 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
_A = 2
while True:
if is_prime(_snake_case ):
yield num
num += 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 10_001 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , _snake_case ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 2 | 0 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
A__ : List[Any] = 6_3_7_8_1_3_7.0
A__ : Any = 6_3_5_6_7_5_2.3_1_4_2_4_5
A__ : Optional[Any] = 6378137
def UpperCAmelCase__ ( UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float ) -> float:
__lowerCamelCase : Tuple = (AXIS_A - AXIS_B) / AXIS_A
__lowerCamelCase : str = atan((1 - flattening) * tan(radians(UpperCAmelCase_ ) ) )
__lowerCamelCase : Union[str, Any] = atan((1 - flattening) * tan(radians(UpperCAmelCase_ ) ) )
__lowerCamelCase : Union[str, Any] = radians(UpperCAmelCase_ )
__lowerCamelCase : str = radians(UpperCAmelCase_ )
# Equation
__lowerCamelCase : Union[str, Any] = sin((phi_a - phi_a) / 2 )
__lowerCamelCase : int = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
__lowerCamelCase : Any = sqrt(sin_sq_phi + (cos(UpperCAmelCase_ ) * cos(UpperCAmelCase_ ) * sin_sq_lambda) )
return 2 * RADIUS * asin(UpperCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 13 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase_ = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase_ = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase_ = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase_ = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase_ = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase_ = re.compile(r"""^\s*else:""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] ) -> Any:
if _re_test_backend.search(_snake_case ) is None:
return None
_A = [b[0] for b in _re_backend.findall(_snake_case )]
backends.sort()
return "_and_".join(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any ) -> Any:
with open(_snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.readlines()
_A = 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
_A = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
_A = 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 ):
_A = _re_one_line_import_struct.search(_snake_case ).groups()[0]
_A = re.findall(r'''\[([^\]]+)\]''' , _snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
_A = _re_import_struct_key_value.search(_snake_case )
if single_line_import_search is not None:
_A = [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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
_A = 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:
_A = _re_import_struct_add_many.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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:
_A = _re_between_brackets.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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
_A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_A = []
while (
line_index < len(_snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
_A = lines[line_index]
_A = _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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
_A = lines[line_index]
_A = _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
_A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] , _snake_case :Dict ) -> Any:
def find_duplicates(_snake_case :Any ):
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!"]
_A = []
for key in import_dict_objects.keys():
_A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_A = 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] ) ):
_A = '''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 SCREAMING_SNAKE_CASE_ ( ) -> int:
_A = []
for root, _, files in os.walk(_snake_case ):
if "__init__.py" in files:
_A = os.path.join(_snake_case , '''__init__.py''' )
_A = parse_init(_snake_case )
if objects is not None:
_A = analyze_results(*_snake_case )
if len(_snake_case ) > 0:
_A = 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 SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_A = []
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
_A = str((Path(_snake_case ) / folder).relative_to(_snake_case ) )
_A = short_path.replace(os.path.sep , '''.''' )
submodules.append(_snake_case )
for fname in files:
if fname == "__init__.py":
continue
_A = str((Path(_snake_case ) / fname).relative_to(_snake_case ) )
_A = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_snake_case )
return submodules
UpperCAmelCase_ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_A = direct_transformers_import(_snake_case )
_A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_snake_case , '''__init__.py''' ) , '''r''' ) as f:
_A = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , _snake_case ) ) )
_A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_snake_case ) > 0:
_A = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 2 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
a__ = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ASTForAudioClassification''',
'''ASTModel''',
'''ASTPreTrainedModel''',
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ['''ASTFeatureExtractor''']
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 14 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(_A)
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Optional[int] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : List[str] ) -> List[str]:
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def snake_case_ ( self : Any , __lowerCAmelCase : Dict=None , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=None ) -> int:
_A = {}
_A = {}
if prompt is not None:
_A = prompt
if generate_kwargs is not None:
_A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
_A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[str] , __lowerCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
return super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any]=None ) -> int:
_A = load_image(__lowerCAmelCase )
if prompt is not None:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(__lowerCAmelCase )} - but expected a single string. '''
'''Note also that one single text can be provided for conditional image to text generation.''' )
_A = self.model.config.model_type
if model_type == "git":
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids
_A = [self.tokenizer.cls_token_id] + input_ids
_A = torch.tensor(__lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
_A = self.image_processor(images=__lowerCAmelCase , header_text=__lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(__lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(__lowerCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A = None
return model_inputs
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict=None ) -> str:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __lowerCAmelCase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
_A = None
if generate_kwargs is None:
_A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A = model_inputs.pop(self.model.main_input_name )
_A = self.model.generate(__lowerCAmelCase , **__lowerCAmelCase , **__lowerCAmelCase )
return model_outputs
def snake_case_ ( self : Dict , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = []
for output_ids in model_outputs:
_A = {
'''generated_text''': self.tokenizer.decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , )
}
records.append(__lowerCAmelCase )
return records
| 2 | 0 |
import qiskit
def UpperCamelCase ( __magic_name__ : int = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
lowercase__ = qubits
# Using Aer's simulator
lowercase__ = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
lowercase__ = qiskit.QuantumCircuit(__magic_name__ , __magic_name__ )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , __magic_name__ ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , __magic_name__ )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(__magic_name__ ) ) , list(range(__magic_name__ ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
lowercase__ = qiskit.execute(__magic_name__ , __magic_name__ , shots=1000 )
return job.result().get_counts(__magic_name__ )
if __name__ == "__main__":
print(F'Total count for various states are: {quantum_entanglement(3)}')
| 15 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_A = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 2 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MgpstrTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = {}
lowerCamelCase__ = False
def _snake_case ( self : Optional[Any] ):
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE = ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
# fmt: on
SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(__lowerCamelCase ) + "\n" )
def _snake_case ( self : Dict , **__lowerCamelCase : str ):
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def _snake_case ( self : Union[str, Any] , __lowerCamelCase : List[str] ):
SCREAMING_SNAKE_CASE = "tester"
SCREAMING_SNAKE_CASE = "tester"
return input_text, output_text
@unittest.skip("MGP-STR always lower cases letters." )
def _snake_case ( self : Union[str, Any] ):
pass
def _snake_case ( self : str ):
SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=__lowerCamelCase )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE = "[SPECIAL_TOKEN]"
tokenizer.add_special_tokens({"cls_token": special_token} )
SCREAMING_SNAKE_CASE = tokenizer.encode([special_token] , add_special_tokens=__lowerCamelCase )
self.assertEqual(len(__lowerCamelCase ) , 1 )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )
self.assertTrue(special_token not in decoded )
def _snake_case ( self : Tuple ):
SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.get_input_output_texts(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertNotEqual(len(__lowerCamelCase ) , 0 )
SCREAMING_SNAKE_CASE = tokenizer.decode(__lowerCamelCase )
self.assertIsInstance(__lowerCamelCase , __lowerCamelCase )
self.assertEqual(text_a.replace(" " , "" ) , __lowerCamelCase )
@unittest.skip("MGP-STR tokenizer only handles one sequence." )
def _snake_case ( self : Optional[int] ):
pass
@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer" )
def _snake_case ( self : Tuple ):
pass | 16 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_A = kruskal(_snake_case , _snake_case )
_A = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_snake_case ) == sorted(_snake_case )
| 2 | 0 |
import numpy as np
class lowerCamelCase_ :
def __init__( self : Union[str, Any] ):
__A : Union[str, Any] = (0, 0)
__A : Optional[Any] = None
__A : int = 0
__A : List[Any] = 0
__A : Any = 0
def __eq__( self : str , __A : Dict ):
return self.position == cell.position
def lowerCAmelCase_ ( self : int ):
print(self.position )
class lowerCamelCase_ :
def __init__( self : Dict , __A : List[str]=(5, 5) ):
__A : str = np.zeros(__A )
__A : str = world_size[0]
__A : Union[str, Any] = world_size[1]
def lowerCAmelCase_ ( self : Dict ):
print(self.w )
def lowerCAmelCase_ ( self : int , __A : str ):
__A : int = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
__A : Optional[int] = cell.position[0]
__A : List[str] = cell.position[1]
__A : Optional[int] = []
for n in neughbour_cord:
__A : Optional[int] = current_x + n[0]
__A : int = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
__A : int = Cell()
__A : Optional[Any] = (x, y)
__A : Optional[Any] = cell
neighbours.append(__A )
return neighbours
def __SCREAMING_SNAKE_CASE ( a__ : Tuple ,a__ : Optional[int] ,a__ : List[str] ) -> Any:
__A : Dict = []
__A : Dict = []
_open.append(a__ )
while _open:
__A : List[str] = np.argmin([n.f for n in _open] )
__A : Dict = _open[min_f]
_closed.append(_open.pop(a__ ) )
if current == goal:
break
for n in world.get_neigbours(a__ ):
for c in _closed:
if c == n:
continue
__A : Tuple = current.g + 1
__A , __A : int = n.position
__A , __A : str = goal.position
__A : Optional[int] = (ya - ya) ** 2 + (xa - xa) ** 2
__A : Any = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(a__ )
__A : Any = []
while current.parent is not None:
path.append(current.position )
__A : Tuple = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = Gridworld()
# Start position and goal
UpperCAmelCase_ : Any = Cell()
UpperCAmelCase_ : Tuple = (0, 0)
UpperCAmelCase_ : Tuple = Cell()
UpperCAmelCase_ : str = (4, 4)
print(f"""path from {start.position} to {goal.position}""")
UpperCAmelCase_ : Tuple = astar(world, start, goal)
# Just for visual reasons.
for i in s:
UpperCAmelCase_ : Optional[int] = 1
print(world.w)
| 17 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BioGptForCausalLM",
"BioGptForTokenClassification",
"BioGptForSequenceClassification",
"BioGptModel",
"BioGptPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 18 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return False
_A = 0
_A = 0
_A = 1
# Calculating the hash of pattern and substring of text
for i in range(_snake_case ):
_A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def SCREAMING_SNAKE_CASE_ ( ) -> None:
_A = '''abc1abc12'''
_A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_A = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(_snake_case , _snake_case ) and not rabin_karp(_snake_case , _snake_case )
# Test 2)
_A = '''ABABX'''
_A = '''ABABZABABYABABX'''
assert rabin_karp(_snake_case , _snake_case )
# Test 3)
_A = '''AAAB'''
_A = '''ABAAAAAB'''
assert rabin_karp(_snake_case , _snake_case )
# Test 4)
_A = '''abcdabcy'''
_A = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(_snake_case , _snake_case )
# Test 5)
_A = '''Lü'''
_A = '''Lüsai'''
assert rabin_karp(_snake_case , _snake_case )
_A = '''Lue'''
assert not rabin_karp(_snake_case , _snake_case )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 2 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , ) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = 13
_UpperCamelCase = 7
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = 99
_UpperCamelCase = 32
_UpperCamelCase = 2
_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
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_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 = RoFormerConfig(
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 , initializer_range=self.initializer_range , return_dict=__a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> List[str]:
'''simple docstring'''
_UpperCamelCase = TFRoFormerModel(config=__a)
_UpperCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
_UpperCamelCase = [input_ids, input_mask]
_UpperCamelCase = model(__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> List[str]:
'''simple docstring'''
_UpperCamelCase = True
_UpperCamelCase = TFRoFormerForCausalLM(config=__a)
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(__a)['''logits''']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape) , [self.batch_size, self.seq_length, self.vocab_size])
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> int:
'''simple docstring'''
_UpperCamelCase = TFRoFormerForMaskedLM(config=__a)
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFRoFormerForSequenceClassification(config=__a)
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.num_choices
_UpperCamelCase = TFRoFormerForMultipleChoice(config=__a)
_UpperCamelCase = tf.tile(tf.expand_dims(__a , 1) , (1, self.num_choices, 1))
_UpperCamelCase = tf.tile(tf.expand_dims(__a , 1) , (1, self.num_choices, 1))
_UpperCamelCase = tf.tile(tf.expand_dims(__a , 1) , (1, self.num_choices, 1))
_UpperCamelCase = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFRoFormerForTokenClassification(config=__a)
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> str:
'''simple docstring'''
_UpperCamelCase = TFRoFormerForQuestionAnswering(config=__a)
_UpperCamelCase = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase__ = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self , __a , __a , __a , __a , __a) -> List[str]:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFRoFormerModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
@slow
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''')
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
_UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]])
_UpperCamelCase = model(__a)[0]
# TODO Replace vocab size
_UpperCamelCase = 5_00_00
_UpperCamelCase = [1, 6, vocab_size]
self.assertEqual(output.shape , __a)
print(output[:, :3, :3])
# TODO Replace values below with what was printed above.
_UpperCamelCase = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
])
tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1e-4)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
lowercase__ = 1E-4
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = tf.constant([[4, 10]])
_UpperCamelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6)
_UpperCamelCase = emba(input_ids.shape)
_UpperCamelCase = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]])
tf.debugging.assert_near(__a , __a , atol=self.tolerance)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
])
_UpperCamelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12)
emba([2, 16, 5_12])
_UpperCamelCase = emba.weight[:3, :5]
tf.debugging.assert_near(__a , __a , atol=self.tolerance)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
lowercase__ = 1E-4
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
# 2,12,16,64
_UpperCamelCase = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa) , shape=(2, 12, 16, 64)) / 1_00
_UpperCamelCase = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa) , shape=(2, 12, 16, 64)) / 1_00
_UpperCamelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64)
_UpperCamelCase = embed_positions([2, 16, 7_68])[None, None, :, :]
_UpperCamelCase , _UpperCamelCase = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__a , __a , __a)
_UpperCamelCase = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
])
_UpperCamelCase = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
])
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __a , atol=self.tolerance)
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __a , atol=self.tolerance)
| 19 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
UpperCAmelCase_ = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
UpperCAmelCase_ = """</w>"""
UpperCAmelCase_ = """@@ """
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[Any] ) -> List[str]:
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
# Speech2Text2 has no max input length
UpperCAmelCase_ = {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Dict = VOCAB_FILES_NAMES
a__ : str = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : List[Any] = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str]="<s>" , __lowerCAmelCase : Tuple="<pad>" , __lowerCAmelCase : Optional[Any]="</s>" , __lowerCAmelCase : Dict="<unk>" , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=None , **__lowerCAmelCase : str , ) -> Dict:
super().__init__(
unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , do_lower_case=__lowerCAmelCase , **__lowerCAmelCase , )
_A = do_lower_case
with open(__lowerCAmelCase , encoding='''utf-8''' ) as vocab_handle:
_A = json.load(__lowerCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
_A = None
_A = None
else:
with open(__lowerCAmelCase , encoding='''utf-8''' ) as merges_handle:
_A = merges_handle.read().split('''\n''' )[:-1]
_A = [tuple(merge.split()[:2] ) for merge in merges]
_A = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_A = {}
@property
def snake_case_ ( self : List[str] ) -> int:
return len(self.decoder )
def snake_case_ ( self : Dict ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
_A = get_pairs(__lowerCAmelCase )
if not pairs:
return token
while True:
_A = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(__lowerCAmelCase ):
try:
_A = word.index(__lowerCAmelCase , __lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__lowerCAmelCase )
_A = new_word
if len(__lowerCAmelCase ) == 1:
break
else:
_A = get_pairs(__lowerCAmelCase )
_A = ''' '''.join(__lowerCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
_A = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(__lowerCAmelCase ):
_A = word.replace(__lowerCAmelCase , '''''' )
_A = word.replace(''' ''' , __lowerCAmelCase )
_A = word
return word
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Tuple ) -> Optional[int]:
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
_A = text.lower()
_A = text.split()
_A = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str ) -> int:
return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) )
def snake_case_ ( self : str , __lowerCAmelCase : int ) -> str:
_A = self.decoder.get(__lowerCAmelCase , self.unk_token )
return result
def snake_case_ ( self : List[str] , __lowerCAmelCase : List[str] ) -> str:
_A = ''' '''.join(__lowerCAmelCase )
# make sure @@ tokens are concatenated
_A = ''''''.join(string.split(__lowerCAmelCase ) )
return string
def snake_case_ ( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCAmelCase , ensure_ascii=__lowerCAmelCase ) + '''\n''' )
_A = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
_A = token_index
writer.write(''' '''.join(__lowerCAmelCase ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 2 | 0 |
import requests
_lowerCAmelCase: Union[str, Any] = '' # <-- Put your OpenWeatherMap appid here!
_lowerCAmelCase: Union[str, Any] = 'https://api.openweathermap.org/data/2.5/'
def _lowercase( __a : str = "Chicago" , __a : str = APPID ):
return requests.get(URL_BASE + 'weather' , params=locals() ).json()
def _lowercase( __a : str = "Kolkata, India" , __a : str = APPID ):
return requests.get(URL_BASE + 'forecast' , params=locals() ).json()
def _lowercase( __a : float = 55.68 , __a : float = 12.57 , __a : str = APPID ):
return requests.get(URL_BASE + 'onecall' , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
_lowerCAmelCase: Dict = input('Enter a location:').strip()
if location:
pprint(current_weather(location))
else:
break
| 20 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 2
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Optional[int] ) -> None:
_A = []
_A = {}
_A = 0
def __len__( self : str ) -> int:
return self.elements
def __repr__( self : Optional[int] ) -> str:
return str(self.heap )
def snake_case_ ( self : str ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
_A = self.elements
self.elements += 1
self._bubble_up(__lowerCAmelCase )
def snake_case_ ( self : Tuple ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
_A , _A = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
_A , _A = self.heap[0]
self._bubble_down(__lowerCAmelCase )
return elem
def snake_case_ ( self : int , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Update the weight of the given key
_A = self.position_map[elem]
_A = (elem, weight)
if position > 0:
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
_A = self.position_map[elem]
if curr_pos == 0:
return None
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[curr_pos]
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_up(__lowerCAmelCase )
return None
def snake_case_ ( self : Dict , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
_A = self.position_map[elem]
_A , _A = self.heap[curr_pos]
_A = get_child_left_position(__lowerCAmelCase )
_A = get_child_right_position(__lowerCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
_A , _A = self.heap[child_left_position]
_A , _A = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
if child_left_position < self.elements:
_A , _A = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
else:
return None
if child_right_position < self.elements:
_A , _A = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
return None
def snake_case_ ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
# Swap the nodes at the given positions
_A = self.heap[nodea_pos][0]
_A = self.heap[nodea_pos][0]
_A , _A = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
_A = nodea_pos
_A = nodea_pos
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : str ) -> None:
_A = {}
_A = 0
def __repr__( self : str ) -> str:
return str(self.connections )
def __len__( self : Dict ) -> int:
return self.nodes
def snake_case_ ( self : Any , __lowerCAmelCase : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
_A = {}
self.nodes += 1
def snake_case_ ( self : str , __lowerCAmelCase : T , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__lowerCAmelCase )
self.add_node(__lowerCAmelCase )
_A = weight
_A = weight
def SCREAMING_SNAKE_CASE_ ( _snake_case :GraphUndirectedWeighted[T] , ) -> tuple[dict[T, int], dict[T, T | None]]:
_A = {node: maxsize for node in graph.connections}
_A = {node: None for node in graph.connections}
_A = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_snake_case , _snake_case )
if priority_queue.is_empty():
return dist, parent
# initialization
_A = priority_queue.extract_min()
_A = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
# running prim's algorithm
while not priority_queue.is_empty():
_A = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
return dist, parent
| 2 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self :Optional[int] , __snake_case :int="</s>" , __snake_case :List[Any]="<unk>" , __snake_case :Optional[int]="<pad>" , __snake_case :Any=1_25 , __snake_case :Optional[Any]=None , **__snake_case :Optional[int] , ):
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
__magic_name__ : Tuple =[f"<extra_id_{i}>" for i in range(__snake_case )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__magic_name__ : List[Any] =len(set(filter(lambda __snake_case : bool("""extra_id""" in str(__snake_case ) ) , __snake_case ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
""" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
""" extra_ids tokens""" )
__magic_name__ : Tuple =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else pad_token
__magic_name__ : List[str] =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else eos_token
__magic_name__ : int =AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else unk_token
super().__init__(
eos_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , extra_ids=__snake_case , additional_special_tokens=__snake_case , **__snake_case , )
__magic_name__ : Union[str, Any] =extra_ids
__magic_name__ : Tuple =2**8 # utf is 8 bits
# define special tokens dict
__magic_name__ : Dict[int, str] ={
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__magic_name__ : Optional[int] =len(self.special_tokens_encoder )
__magic_name__ : Any =len(__snake_case )
for i, token in enumerate(__snake_case ):
__magic_name__ : Union[str, Any] =self.vocab_size + i - n
__magic_name__ : Dict[str, int] ={v: k for k, v in self.special_tokens_encoder.items()}
@property
def A__ ( self :Optional[Any] ):
'''simple docstring'''
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def A__ ( self :Tuple , __snake_case :List[int] , __snake_case :Optional[List[int]] = None , __snake_case :bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__snake_case )) + [1]
return ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case )) + [1]
def A__ ( self :Union[str, Any] , __snake_case :List[int] ):
'''simple docstring'''
if len(__snake_case ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
""" eos tokens being added.""" )
return token_ids
else:
return token_ids + [self.eos_token_id]
def A__ ( self :Any , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =[self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def A__ ( self :Union[str, Any] , __snake_case :List[int] , __snake_case :Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ : Optional[Any] =self._add_eos_if_not_present(__snake_case )
if token_ids_a is None:
return token_ids_a
else:
__magic_name__ : List[str] =self._add_eos_if_not_present(__snake_case )
return token_ids_a + token_ids_a
def A__ ( self :Tuple , __snake_case :str ):
'''simple docstring'''
__magic_name__ : Dict =[chr(__snake_case ) for i in text.encode("""utf-8""" )]
return tokens
def A__ ( self :int , __snake_case :List[str] ):
'''simple docstring'''
if token in self.special_tokens_encoder:
__magic_name__ : Dict =self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__magic_name__ : int =self.added_tokens_encoder[token]
elif len(__snake_case ) != 1:
__magic_name__ : Optional[Any] =self.unk_token_id
else:
__magic_name__ : Any =ord(__snake_case ) + self._num_special_tokens
return token_id
def A__ ( self :Optional[Any] , __snake_case :int ):
'''simple docstring'''
if index in self.special_tokens_decoder:
__magic_name__ : Any =self.special_tokens_decoder[index]
else:
__magic_name__ : int =chr(index - self._num_special_tokens )
return token
def A__ ( self :Union[str, Any] , __snake_case :int ):
'''simple docstring'''
__magic_name__ : Any =B""""""
for token in tokens:
if token in self.special_tokens_decoder:
__magic_name__ : int =self.special_tokens_decoder[token].encode("""utf-8""" )
elif token in self.added_tokens_decoder:
__magic_name__ : Union[str, Any] =self.special_tokens_decoder[token].encode("""utf-8""" )
elif token in self.special_tokens_encoder:
__magic_name__ : str =token.encode("""utf-8""" )
elif token in self.added_tokens_encoder:
__magic_name__ : Optional[int] =token.encode("""utf-8""" )
else:
__magic_name__ : Tuple =bytes([ord(__snake_case )] )
bstring += tok_string
__magic_name__ : str =bstring.decode("""utf-8""" , errors="""ignore""" )
return string
def A__ ( self :Tuple , __snake_case :str , __snake_case :Optional[str] = None ):
'''simple docstring'''
return ()
| 21 |
import os
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 logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [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]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 0 |
'''simple docstring'''
from __future__ import annotations
_snake_case : List[str] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case_ (UpperCamelCase : list[list[int]] , UpperCamelCase : list[int] , UpperCamelCase : list[int] , UpperCamelCase : int , UpperCamelCase : list[list[int]] , ):
'''simple docstring'''
_a = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase ) )
] # the reference grid
_a = 1
_a = [
[0 for col in range(len(grid[0] ) )] for row in range(len(UpperCamelCase ) )
] # the action grid
_a = init[0]
_a = init[1]
_a = 0
_a = g + heuristic[x][y] # cost from starting cell to destination cell
_a = [[f, g, x, y]]
_a = False # flag that is set when search is complete
_a = False # flag set if we can't find expand
while not found and not resign:
if len(UpperCamelCase ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
_a = cell.pop()
_a = next_cell[2]
_a = next_cell[3]
_a = next_cell[1]
if x == goal[0] and y == goal[1]:
_a = True
else:
for i in range(len(UpperCamelCase ) ): # to try out different valid actions
_a = x + DIRECTIONS[i][0]
_a = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(UpperCamelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
_a = g + cost
_a = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
_a = 1
_a = i
_a = []
_a = goal[0]
_a = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
_a = x - DIRECTIONS[action[x][y]][0]
_a = y - DIRECTIONS[action[x][y]][1]
_a = xa
_a = ya
invpath.append([x, y] )
_a = []
for i in range(len(UpperCamelCase ) ):
path.append(invpath[len(UpperCamelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
_snake_case : str = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
_snake_case : List[Any] = [0, 0]
# all coordinates are given in format [y,x]
_snake_case : Optional[int] = [len(grid) - 1, len(grid[0]) - 1]
_snake_case : Tuple = 1
# the cost map which pushes the path closer to the goal
_snake_case : str = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
_snake_case : Optional[int] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
_snake_case : str = 99
_snake_case , _snake_case : Any = search(grid, init, goal, cost, heuristic)
print('ACTION MAP')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 22 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_snake_case )
return explored
UpperCAmelCase_ = {
"""A""": ["""B""", """C""", """D"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F"""],
"""D""": ["""B""", """D"""],
"""E""": ["""B""", """F"""],
"""F""": ["""C""", """E""", """G"""],
"""G""": ["""F"""],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, """A"""))
| 2 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Tuple = """1"""
snake_case__ : Optional[int] = """0"""
snake_case__ : Any = """1"""
snake_case__ : List[str] = ort.SessionOptions()
snake_case__ : List[str] = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
snake_case__ : List[Any] = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
snake_case__ : str = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
snake_case__ : List[str] = ort.RunOptions()
snake_case__ : Optional[int] = 1_2_8
snake_case__ : Optional[int] = 1
snake_case__ : Union[str, Any] = np.ones((batch, sequence), dtype=np.intaa)
snake_case__ : int = np.ones((batch, sequence), dtype=np.intaa)
snake_case__ : Tuple = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
snake_case__ : str = time.time()
snake_case__ : Tuple = 2_0_0_0
snake_case__ : Dict = {}
for iter in range(max_iters):
snake_case__ : Tuple = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_0_0_0 / max_iters))
| 23 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _UpperCamelCase (_lowerCamelCase : Union[dict, list, tuple, torch.Tensor] )-> List[Tuple[int, ...]]:
'''simple docstring'''
__snake_case = []
if isinstance(_lowerCamelCase , _lowerCamelCase ):
for v in tree.values():
shapes.extend(_fetch_dims(_lowerCamelCase ) )
elif isinstance(_lowerCamelCase , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(_lowerCamelCase ) )
elif isinstance(_lowerCamelCase , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : Tuple[int, ...] )-> Tuple[int, ...]:
'''simple docstring'''
__snake_case = []
for d in reversed(_lowerCamelCase ):
idx.append(flat_idx % d )
__snake_case = flat_idx // d
return tuple(reversed(_lowerCamelCase ) )
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : Sequence[int] , _lowerCamelCase : Sequence[int] , _lowerCamelCase : Sequence[int] , _lowerCamelCase : Optional[Sequence[bool]] = None , _lowerCamelCase : Optional[Sequence[bool]] = None , )-> List[Tuple[slice, ...]]:
'''simple docstring'''
def reduce_edge_list(_lowerCamelCase : List[bool] ) -> None:
__snake_case = True
for i in range(len(_lowerCamelCase ) ):
__snake_case = -1 * (i + 1)
l[reversed_idx] &= tally
__snake_case = l[reversed_idx]
if start_edges is None:
__snake_case = [s == 0 for s in start]
reduce_edge_list(_lowerCamelCase )
if end_edges is None:
__snake_case = [e == (d - 1) for e, d in zip(_lowerCamelCase , _lowerCamelCase )]
reduce_edge_list(_lowerCamelCase )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(_lowerCamelCase ) == 0:
return [()]
elif len(_lowerCamelCase ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__snake_case = []
__snake_case = []
# Dimensions common to start and end can be selected directly
for s, e in zip(_lowerCamelCase , _lowerCamelCase ):
if s == e:
path_list.append(slice(_lowerCamelCase , s + 1 ) )
else:
break
__snake_case = tuple(_lowerCamelCase )
__snake_case = len(_lowerCamelCase )
# start == end, and we're done
if divergence_idx == len(_lowerCamelCase ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__snake_case = start[divergence_idx]
return tuple(
path + (slice(_lowerCamelCase , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__snake_case = end[divergence_idx]
return tuple(
path + (slice(_lowerCamelCase , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__snake_case = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _UpperCamelCase (_lowerCamelCase : torch.Tensor , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int )-> torch.Tensor:
'''simple docstring'''
__snake_case = t.shape[:no_batch_dims]
__snake_case = list(_flat_idx_to_idx(_lowerCamelCase , _lowerCamelCase ) )
# _get_minimal_slice_set is inclusive
__snake_case = list(_flat_idx_to_idx(flat_end - 1 , _lowerCamelCase ) )
# Get an ordered list of slices to perform
__snake_case = _get_minimal_slice_set(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
__snake_case = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _UpperCamelCase (_lowerCamelCase : Callable , _lowerCamelCase : Dict[str, Any] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool = False , _lowerCamelCase : Any = None , _lowerCamelCase : bool = False , )-> Any:
'''simple docstring'''
if not (len(_lowerCamelCase ) > 0):
raise ValueError('''Must provide at least one input''' )
__snake_case = [shape[:no_batch_dims] for shape in _fetch_dims(_lowerCamelCase )]
__snake_case = tuple([max(_lowerCamelCase ) for s in zip(*_lowerCamelCase )] )
def _prep_inputs(_lowerCamelCase : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__snake_case = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__snake_case = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__snake_case = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__snake_case = tensor_tree_map(_prep_inputs , _lowerCamelCase )
__snake_case = None
if _out is not None:
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__snake_case = 1
for d in orig_batch_dims:
flat_batch_dim *= d
__snake_case = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(_lowerCamelCase : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__snake_case = 0
__snake_case = prepped_outputs
for _ in range(_lowerCamelCase ):
# Chunk the input
if not low_mem:
__snake_case = _select_chunk
else:
__snake_case = partial(
_chunk_slice , flat_start=_lowerCamelCase , flat_end=min(_lowerCamelCase , i + chunk_size ) , no_batch_dims=len(_lowerCamelCase ) , )
__snake_case = tensor_tree_map(_lowerCamelCase , _lowerCamelCase )
# Run the layer on the chunk
__snake_case = layer(**_lowerCamelCase )
# Allocate space for the output
if out is None:
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , _lowerCamelCase )
# Put the chunk in its pre-allocated space
if isinstance(_lowerCamelCase , _lowerCamelCase ):
def assign(_lowerCamelCase : dict , _lowerCamelCase : dict ) -> None:
for k, v in da.items():
if isinstance(_lowerCamelCase , _lowerCamelCase ):
assign(_lowerCamelCase , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__snake_case = da[k]
assign(_lowerCamelCase , _lowerCamelCase )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
for xa, xa in zip(_lowerCamelCase , _lowerCamelCase ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__snake_case = xa
elif isinstance(_lowerCamelCase , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__snake_case = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__snake_case = tensor_tree_map(lambda _lowerCamelCase : t.view(orig_batch_dims + t.shape[1:] ) , _lowerCamelCase )
return out
class lowerCAmelCase :
def __init__( self , __SCREAMING_SNAKE_CASE = 512 , ) -> List[Any]:
'''simple docstring'''
__snake_case = max_chunk_size
__snake_case = None
__snake_case = None
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__snake_case = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
__snake_case = [c for c in candidates if c > min_chunk_size]
__snake_case = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(__SCREAMING_SNAKE_CASE ) -> bool:
try:
with torch.no_grad():
fn(*__SCREAMING_SNAKE_CASE , chunk_size=__SCREAMING_SNAKE_CASE )
return True
except RuntimeError:
return False
__snake_case = 0
__snake_case = len(__SCREAMING_SNAKE_CASE ) - 1
while i > min_viable_chunk_size_index:
__snake_case = test_chunk_size(candidates[i] )
if not viable:
__snake_case = (min_viable_chunk_size_index + i) // 2
else:
__snake_case = i
__snake_case = (i + len(__SCREAMING_SNAKE_CASE ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
__snake_case = True
for aa, aa in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
assert type(__SCREAMING_SNAKE_CASE ) == type(__SCREAMING_SNAKE_CASE )
if isinstance(__SCREAMING_SNAKE_CASE , (list, tuple) ):
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE : x[0] )]
__snake_case = [v for _, v in sorted(aa.items() , key=lambda __SCREAMING_SNAKE_CASE : x[0] )]
consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
else:
consistent &= aa == aa
return consistent
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ) -> int:
'''simple docstring'''
__snake_case = True
__snake_case = tree_map(lambda __SCREAMING_SNAKE_CASE : a.shape if isinstance(__SCREAMING_SNAKE_CASE , torch.Tensor ) else a , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(__SCREAMING_SNAKE_CASE )
__snake_case = self._compare_arg_caches(self.cached_arg_data , __SCREAMING_SNAKE_CASE )
else:
# Otherwise, we can reuse the precomputed value
__snake_case = False
if not consistent:
__snake_case = self._determine_favorable_chunk_size(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )
__snake_case = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 24 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Any = "xlnet"
a__ : Dict = ["mems"]
a__ : List[str] = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , __lowerCAmelCase : Dict=3_20_00 , __lowerCAmelCase : List[str]=10_24 , __lowerCAmelCase : Dict=24 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=40_96 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]="bi" , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Optional[Any]=5_12 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Any="last" , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Tuple="tanh" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : str=5 , __lowerCAmelCase : str=5 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=2 , **__lowerCAmelCase : List[str] , ) -> Tuple:
_A = vocab_size
_A = d_model
_A = n_layer
_A = n_head
if d_model % n_head != 0:
raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __lowerCAmelCase , )
_A = kwargs['''use_cache''']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def snake_case_ ( self : Optional[Any] ) -> Union[str, Any]:
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 snake_case_ ( self : Tuple , __lowerCAmelCase : Optional[Any] ) -> Dict:
# 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.''' )
| 2 | 0 |
def lowerCamelCase__ ( _a):
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 lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_a) # No of vertices in graph
SCREAMING_SNAKE_CASE : str = [0] * n
SCREAMING_SNAKE_CASE : str = [False] * n
def dfs(_a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : str = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(_a , _a , _a , id_)
SCREAMING_SNAKE_CASE : List[Any] = 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
SCREAMING_SNAKE_CASE : Tuple = min(low[at] , low[to])
SCREAMING_SNAKE_CASE : list[tuple[int, int]] = []
for i in range(_a):
if not visited[i]:
dfs(_a , -1 , _a , id_)
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(_snake_case ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_snake_case ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_snake_case ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase ) -> float:
"""simple docstring"""
__snake_case : Any = u
for i in range(1 , _lowerCamelCase ):
__snake_case : Optional[Any] = temp * (u - i)
return temp
def _a ( ) -> None:
"""simple docstring"""
__snake_case : str = int(input("""enter the numbers of values: """ ) )
__snake_case : list[list[float]] = []
for _ in range(_lowerCamelCase ):
y.append([] )
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
y[i].append(_lowerCamelCase )
__snake_case : Any = 0
print("""enter the values of parameters in a list: """ )
__snake_case : Dict = list(map(_lowerCamelCase , input().split() ) )
print("""enter the values of corresponding parameters: """ )
for i in range(_lowerCamelCase ):
__snake_case : Optional[int] = float(input() )
__snake_case : List[str] = int(input("""enter the value to interpolate: """ ) )
__snake_case : int = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , _lowerCamelCase ):
for j in range(n - i ):
__snake_case : List[Any] = y[j + 1][i - 1] - y[j][i - 1]
__snake_case : Union[str, Any] = y[0][0]
for i in range(1 , _lowerCamelCase ):
summ += (ucal(_lowerCamelCase , _lowerCamelCase ) * y[0][i]) / math.factorial(_lowerCamelCase )
print(F'''the value at {value} is {summ}''' )
if __name__ == "__main__":
main()
| 26 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_snake_case ) == 1:
return True
_A = series[1] - series[0]
for index in range(len(_snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> float:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
_A = 0
for val in series:
answer += val
return answer / len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
_A = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_A = ''
_A = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(_SCREAMING_SNAKE_CASE ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_A, _A = 0, 0
# length[i] shows the length of palindromic substring with center i
_A = [1 for i in range(len(_SCREAMING_SNAKE_CASE ) )]
# for each character in new_string find corresponding palindromic string
_A = 0
for j in range(len(_SCREAMING_SNAKE_CASE ) ):
_A = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(_SCREAMING_SNAKE_CASE )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_A = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_A = j - k + 1 # noqa: E741
_A = j + k - 1
# update max_length and start position
if max_length < length[j]:
_A = length[j]
_A = j
# create that string
_A = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_snake_case ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
_A = QuantumRegister(_snake_case , '''qr''' )
_A = ClassicalRegister(_snake_case , '''cr''' )
_A = QuantumCircuit(_snake_case , _snake_case )
_A = number_of_qubits
for i in range(_snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _snake_case , _snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_snake_case , _snake_case )
# simulate with 10000 shots
_A = Aer.get_backend('''qasm_simulator''' )
_A = execute(_snake_case , _snake_case , shots=10_000 )
return job.result().get_counts(_snake_case )
if __name__ == "__main__":
print(
f'Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'
)
| 2 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
"junnyu/roformer_chinese_char_small": (
"https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
),
"junnyu/roformer_chinese_char_base": (
"https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
),
"junnyu/roformer_small_discriminator": (
"https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
),
"junnyu/roformer_small_generator": (
"https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : List[str] = '''roformer'''
def __init__( self, A=50_000, A=None, A=768, A=12, A=12, A=3_072, A="gelu", A=0.1, A=0.1, A=1_536, A=2, A=0.02, A=1E-12, A=0, A=False, A=True, **A, ):
'''simple docstring'''
super().__init__(pad_token_id=A, **A )
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE : Any = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : Tuple = rotary_value
SCREAMING_SNAKE_CASE : str = use_cache
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[int] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE : Optional[int] = {0: 'batch', 1: 'sequence'}
SCREAMING_SNAKE_CASE : int = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 28 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_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_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :str , _snake_case :Any , _snake_case :int , _snake_case :List[Any] ) -> Optional[int]:
for attribute in key.split('''.''' ):
_A = getattr(_snake_case , _snake_case )
if weight_type is not None:
_A = getattr(_snake_case , _snake_case ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :Any , _snake_case :int ) -> Any:
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_snake_case )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "weight" in name:
_A = '''weight'''
elif "bias" in name:
_A = '''bias'''
else:
_A = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :List[str] , _snake_case :List[str] , _snake_case :Optional[int] , _snake_case :List[Any] ) -> Any:
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = 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.'''
)
_A = 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.'''
)
_A = 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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Tuple:
_A = SEWConfig()
if is_finetuned:
_A = model.wav_encoder.wav_model.cfg
else:
_A = model.cfg
_A = fs_config.conv_bias
_A = eval(fs_config.conv_feature_layers )
_A = [x[0] for x in conv_layers]
_A = [x[1] for x in conv_layers]
_A = [x[2] for x in conv_layers]
_A = '''gelu'''
_A = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_A = 0.0
_A = fs_config.activation_fn.name
_A = fs_config.encoder_embed_dim
_A = 0.02
_A = fs_config.encoder_ffn_embed_dim
_A = 1E-5
_A = fs_config.encoder_layerdrop
_A = fs_config.encoder_attention_heads
_A = fs_config.conv_pos_groups
_A = fs_config.conv_pos
_A = len(_snake_case )
_A = fs_config.encoder_layers
_A = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_A = model.cfg
_A = fs_config.final_dropout
_A = fs_config.layerdrop
_A = fs_config.activation_dropout
_A = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_A = fs_config.attention_dropout
_A = fs_config.dropout_input
_A = fs_config.dropout
_A = fs_config.mask_channel_length
_A = fs_config.mask_channel_prob
_A = fs_config.mask_length
_A = fs_config.mask_prob
_A = '''Wav2Vec2FeatureExtractor'''
_A = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] , _snake_case :Union[str, Any] , _snake_case :Optional[Any]=None , _snake_case :Optional[int]=None , _snake_case :Dict=True ) -> List[Any]:
if is_finetuned:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_A = SEWConfig.from_pretrained(_snake_case )
else:
_A = convert_config(model[0] , _snake_case )
_A = model[0].eval()
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
if is_finetuned:
if dict_path:
_A = Dictionary.load(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
_A = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_snake_case , )
_A = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
_A = SEWForCTC(_snake_case )
else:
_A = SEWModel(_snake_case )
feature_extractor.save_pretrained(_snake_case )
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_model.save_pretrained(_snake_case )
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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 2 | 0 |
"""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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A_ = logging.get_logger(__name__)
A_ = torch.device("""cpu""")
def lowercase ( ):
lowerCamelCase_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase_ = Image.open(requests.get(lowerCAmelCase__ ,stream=lowerCAmelCase__ ).raw )
return im
def lowercase ( lowerCAmelCase__ ):
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02] )
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ = dct.pop(lowerCAmelCase__ )
lowerCamelCase_ = val
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = []
for k in state_dict.keys():
lowerCamelCase_ = k
if ".pwconv" in k:
lowerCamelCase_ = k_new.replace('''.pwconv''' ,'''.point_wise_conv''' )
if ".dwconv" in k:
lowerCamelCase_ = k_new.replace('''.dwconv''' ,'''.depth_wise_conv''' )
if ".Proj." in k:
lowerCamelCase_ = k_new.replace('''.Proj.''' ,'''.proj.''' )
if "patch_embed" in k_new:
lowerCamelCase_ = k_new.replace('''patch_embed''' ,'''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
lowerCamelCase_ = k_new.split('''.''' )
if ls[2].isdigit():
lowerCamelCase_ = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
lowerCamelCase_ = k_new.replace('''network''' ,'''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowerCamelCase_ = 1_000
lowerCamelCase_ = '''huggingface/label-files'''
lowerCamelCase_ = '''imagenet-1k-id2label.json'''
lowerCamelCase_ = json.load(open(hf_hub_download(lowerCAmelCase__ ,lowerCAmelCase__ ,repo_type='''dataset''' ) ,'''r''' ) )
lowerCamelCase_ = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
lowerCamelCase_ = idalabel
lowerCamelCase_ = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowerCamelCase_ = [3, 3, 6, 4]
lowerCamelCase_ = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
lowerCamelCase_ = [3, 3, 9, 6]
lowerCamelCase_ = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
lowerCamelCase_ = [4, 3, 10, 5]
lowerCamelCase_ = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
lowerCamelCase_ = [4, 4, 12, 6]
lowerCamelCase_ = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
lowerCamelCase_ = torch.hub.load_state_dict_from_url(lowerCAmelCase__ ,map_location='''cpu''' ,check_hash=lowerCAmelCase__ )
else:
lowerCamelCase_ = torch.load(lowerCAmelCase__ ,map_location='''cpu''' )
lowerCamelCase_ = checkpoint
lowerCamelCase_ = create_rename_keys(lowerCAmelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
# load HuggingFace model
lowerCamelCase_ = SwiftFormerForImageClassification(lowerCAmelCase__ ).eval()
hf_model.load_state_dict(lowerCAmelCase__ )
# prepare test inputs
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
lowerCamelCase_ = processor(images=lowerCAmelCase__ ,return_tensors='''pt''' )
# compare outputs from both models
lowerCamelCase_ = get_expected_output(lowerCAmelCase__ )
lowerCamelCase_ = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 1_000] )
assert torch.allclose(hf_logits[0, 0:5] ,lowerCAmelCase__ ,atol=1E-3 )
Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
print(f"Saving model {swiftformer_name} to {pytorch_dump_folder_path}" )
hf_model.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swiftformer_name""",
default="""swiftformer_xs""",
choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
type=str,
help="""Name of the SwiftFormer model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""./converted_outputs/""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")
A_ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 29 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Any ) -> Any:
pass
@is_pipeline_test
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@require_torch
def snake_case_ ( self : Tuple ) -> Tuple:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCAmelCase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : int ) -> Optional[int]:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> int:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def snake_case_ ( self : Optional[int] ) -> Dict:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 2 | 0 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __a( nn.Module ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 0.0
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = jnp.floataa
def a__ ( self ) -> int:
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : Union[str, Any] = []
for i in range(self.num_layers ):
UpperCAmelCase_ : List[Any] = self.in_channels if i == 0 else self.out_channels
UpperCAmelCase_ : Tuple = FlaxResnetBlockaD(
in_channels=_SCREAMING_SNAKE_CASE ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = FlaxTransformeraDModel(
in_channels=self.out_channels ,n_heads=self.num_attention_heads ,d_head=self.out_channels // self.num_attention_heads ,depth=1 ,use_linear_projection=self.use_linear_projection ,only_cross_attention=self.only_cross_attention ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
attentions.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[Any] = resnets
UpperCAmelCase_ : List[str] = attentions
if self.add_downsample:
UpperCAmelCase_ : Tuple = FlaxDownsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True ) -> List[Any]:
UpperCAmelCase_ : List[Any] = ()
for resnet, attn in zip(self.resnets ,self.attentions ):
UpperCAmelCase_ : str = resnet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = attn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
UpperCAmelCase_ : Any = self.downsamplers_a(_SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class __a( nn.Module ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 0.0
lowerCAmelCase = 1
lowerCAmelCase = True
lowerCAmelCase = jnp.floataa
def a__ ( self ) -> Union[str, Any]:
UpperCAmelCase_ : Union[str, Any] = []
for i in range(self.num_layers ):
UpperCAmelCase_ : Dict = self.in_channels if i == 0 else self.out_channels
UpperCAmelCase_ : List[Any] = FlaxResnetBlockaD(
in_channels=_SCREAMING_SNAKE_CASE ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = resnets
if self.add_downsample:
UpperCAmelCase_ : List[str] = FlaxDownsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True ) -> Any:
UpperCAmelCase_ : str = ()
for resnet in self.resnets:
UpperCAmelCase_ : Tuple = resnet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
if self.add_downsample:
UpperCAmelCase_ : Optional[Any] = self.downsamplers_a(_SCREAMING_SNAKE_CASE )
output_states += (hidden_states,)
return hidden_states, output_states
class __a( nn.Module ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 0.0
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = jnp.floataa
def a__ ( self ) -> Optional[Any]:
UpperCAmelCase_ : Any = []
UpperCAmelCase_ : Tuple = []
for i in range(self.num_layers ):
UpperCAmelCase_ : List[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCAmelCase_ : Optional[int] = self.prev_output_channel if i == 0 else self.out_channels
UpperCAmelCase_ : Any = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = FlaxTransformeraDModel(
in_channels=self.out_channels ,n_heads=self.num_attention_heads ,d_head=self.out_channels // self.num_attention_heads ,depth=1 ,use_linear_projection=self.use_linear_projection ,only_cross_attention=self.only_cross_attention ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
attentions.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = resnets
UpperCAmelCase_ : Dict = attentions
if self.add_upsample:
UpperCAmelCase_ : Tuple = FlaxUpsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True ) -> Dict:
for resnet, attn in zip(self.resnets ,self.attentions ):
# pop res hidden states
UpperCAmelCase_ : int = res_hidden_states_tuple[-1]
UpperCAmelCase_ : int = res_hidden_states_tuple[:-1]
UpperCAmelCase_ : List[str] = jnp.concatenate((hidden_states, res_hidden_states) ,axis=-1 )
UpperCAmelCase_ : Tuple = resnet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[Any] = attn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
if self.add_upsample:
UpperCAmelCase_ : Dict = self.upsamplers_a(_SCREAMING_SNAKE_CASE )
return hidden_states
class __a( nn.Module ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = 0.0
lowerCAmelCase = 1
lowerCAmelCase = True
lowerCAmelCase = jnp.floataa
def a__ ( self ) -> Any:
UpperCAmelCase_ : List[Any] = []
for i in range(self.num_layers ):
UpperCAmelCase_ : Optional[Any] = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCAmelCase_ : Any = self.prev_output_channel if i == 0 else self.out_channels
UpperCAmelCase_ : int = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = resnets
if self.add_upsample:
UpperCAmelCase_ : List[str] = FlaxUpsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True ) -> str:
for resnet in self.resnets:
# pop res hidden states
UpperCAmelCase_ : Optional[Any] = res_hidden_states_tuple[-1]
UpperCAmelCase_ : List[str] = res_hidden_states_tuple[:-1]
UpperCAmelCase_ : List[str] = jnp.concatenate((hidden_states, res_hidden_states) ,axis=-1 )
UpperCAmelCase_ : Optional[Any] = resnet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
if self.add_upsample:
UpperCAmelCase_ : Tuple = self.upsamplers_a(_SCREAMING_SNAKE_CASE )
return hidden_states
class __a( nn.Module ):
"""simple docstring"""
lowerCAmelCase = 42
lowerCAmelCase = 0.0
lowerCAmelCase = 1
lowerCAmelCase = 1
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = jnp.floataa
def a__ ( self ) -> Tuple:
# there is always at least one resnet
UpperCAmelCase_ : Any = [
FlaxResnetBlockaD(
in_channels=self.in_channels ,out_channels=self.in_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
]
UpperCAmelCase_ : int = []
for _ in range(self.num_layers ):
UpperCAmelCase_ : Tuple = FlaxTransformeraDModel(
in_channels=self.in_channels ,n_heads=self.num_attention_heads ,d_head=self.in_channels // self.num_attention_heads ,depth=1 ,use_linear_projection=self.use_linear_projection ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
attentions.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = FlaxResnetBlockaD(
in_channels=self.in_channels ,out_channels=self.in_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = resnets
UpperCAmelCase_ : Optional[Any] = attentions
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=True ) -> Any:
UpperCAmelCase_ : int = self.resnets[0](_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for attn, resnet in zip(self.attentions ,self.resnets[1:] ):
UpperCAmelCase_ : Optional[int] = attn(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = resnet(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,deterministic=_SCREAMING_SNAKE_CASE )
return hidden_states | 30 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : Tuple ) -> Optional[int]:
_A = tempfile.mkdtemp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_A = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Dict , **__lowerCAmelCase : int ) -> Optional[int]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : str , **__lowerCAmelCase : Optional[Any] ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Tuple , **__lowerCAmelCase : str ) -> Union[str, Any]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def snake_case_ ( self : int ) -> Optional[Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ ( self : Dict ) -> List[str]:
_A = self.get_tokenizer()
_A = self.get_rust_tokenizer()
_A = self.get_image_processor()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase )
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __lowerCAmelCase )
def snake_case_ ( self : List[Any] ) -> List[str]:
_A = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
_A = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(__lowerCAmelCase , return_tensors='''np''' )
_A = processor(images=__lowerCAmelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = processor(text=__lowerCAmelCase )
_A = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ ( self : List[str] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def snake_case_ ( self : Optional[Any] ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__lowerCAmelCase )
_A = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 2 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : int = 10 , __UpperCAmelCase : int = 10_00 , __UpperCAmelCase : bool = True ) -> int:
assert (
isinstance(__UpperCAmelCase , __UpperCAmelCase )
and isinstance(__UpperCAmelCase , __UpperCAmelCase )
and isinstance(__UpperCAmelCase , __UpperCAmelCase )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
return int((number_a + number_a) / 2 )
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) -> None:
assert (
isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(__UpperCAmelCase : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
SCREAMING_SNAKE_CASE_ = lower
SCREAMING_SNAKE_CASE_ = higher
SCREAMING_SNAKE_CASE_ = []
while True:
SCREAMING_SNAKE_CASE_ = get_avg(__UpperCAmelCase , __UpperCAmelCase )
last_numbers.append(__UpperCAmelCase )
if answer(__UpperCAmelCase ) == "low":
SCREAMING_SNAKE_CASE_ = number
elif answer(__UpperCAmelCase ) == "high":
SCREAMING_SNAKE_CASE_ = number
else:
break
print(f"guess the number : {last_numbers[-1]}" )
print(f"details : {last_numbers!s}" )
def UpperCAmelCase_ ( ) -> None:
SCREAMING_SNAKE_CASE_ = int(input('Enter lower value : ' ).strip() )
SCREAMING_SNAKE_CASE_ = int(input('Enter high value : ' ).strip() )
SCREAMING_SNAKE_CASE_ = int(input('Enter value to guess : ' ).strip() )
guess_the_number(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
main() | 31 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = "openai-gpt"
a__ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Union[str, Any] , __lowerCAmelCase : int=4_04_78 , __lowerCAmelCase : Tuple=5_12 , __lowerCAmelCase : str=7_68 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=1E-5 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Optional[Any]="cls_index" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=0.1 , **__lowerCAmelCase : Tuple , ) -> Optional[Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**__lowerCAmelCase )
| 2 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = "T5Config"
class __UpperCamelCase ( A__ ):
__A : str = """mt5"""
__A : Optional[Any] = MTaConfig
class __UpperCamelCase ( A__ ):
__A : Tuple = """mt5"""
__A : List[str] = MTaConfig
class __UpperCamelCase ( A__ ):
__A : Optional[Any] = """mt5"""
__A : int = MTaConfig | 32 |
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 DeformableDetrImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : int=30 , __lowerCAmelCase : Dict=4_00 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=1 / 2_55 , __lowerCAmelCase : int=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
_A = parent
_A = batch_size
_A = num_channels
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
_A = do_rescale
_A = rescale_factor
_A = do_pad
def snake_case_ ( self : Optional[int] ) -> Any:
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 snake_case_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False ) -> Dict:
if not batched:
_A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
_A , _A = image.size
else:
_A , _A = image.shape[1], image.shape[2]
if w < h:
_A = int(self.size['''shortest_edge'''] * h / w )
_A = self.size['''shortest_edge''']
elif w > h:
_A = self.size['''shortest_edge''']
_A = int(self.size['''shortest_edge'''] * w / h )
else:
_A = self.size['''shortest_edge''']
_A = self.size['''shortest_edge''']
else:
_A = []
for image in image_inputs:
_A , _A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase__ ( _A , unittest.TestCase):
"""simple docstring"""
a__ : Any = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case_ ( self : Optional[int] ) -> Any:
_A = DeformableDetrImageProcessingTester(self )
@property
def snake_case_ ( self : Union[str, Any] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = 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 , '''do_rescale''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def snake_case_ ( self : List[str] ) -> int:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
_A = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def snake_case_ ( self : Any ) -> Union[str, Any]:
pass
def snake_case_ ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
_A = image_processing(__lowerCAmelCase , 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 snake_case_ ( self : Tuple ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
# prepare image and target
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
_A = DeformableDetrImageProcessor()
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def snake_case_ ( self : List[str] ) -> List[str]:
# prepare image, target and masks_path
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
_A = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_A = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
_A = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 2 | 0 |
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 GLPNImageProcessor
class __magic_name__ (unittest.TestCase ):
'''simple docstring'''
def __init__( self:Union[str, Any] , _a:Optional[int] , _a:Tuple=7 , _a:Dict=3 , _a:Optional[Any]=18 , _a:Optional[Any]=30 , _a:Union[str, Any]=4_00 , _a:str=True , _a:Optional[Any]=32 , _a:Tuple=True , ):
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = num_channels
snake_case__ = image_size
snake_case__ = min_resolution
snake_case__ = max_resolution
snake_case__ = do_resize
snake_case__ = size_divisor
snake_case__ = do_rescale
def SCREAMING_SNAKE_CASE__ ( self:List[str] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class __magic_name__ (snake_case_ ,unittest.TestCase ):
'''simple docstring'''
__lowercase : Optional[Any] = GLPNImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self:Any ):
snake_case__ = GLPNImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self:List[str] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
snake_case__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , '''do_resize''' ) )
self.assertTrue(hasattr(_a , '''size_divisor''' ) )
self.assertTrue(hasattr(_a , '''resample''' ) )
self.assertTrue(hasattr(_a , '''do_rescale''' ) )
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
# Initialize image_processing
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
# Initialize image_processing
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
# Initialize image_processing
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 33 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case_ :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( *lowerCamelCase_ , **lowerCamelCase_) -> Optional[int]:
pass
@is_pipeline_test
@require_torch
@require_vision
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
A_ = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> Dict:
UpperCamelCase = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''')
UpperCamelCase = [
{
'''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
'''question''': '''How many cats are there?''',
},
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''question''': '''How many cats are there?''',
},
]
return vqa_pipeline, examples
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_) -> Dict:
UpperCamelCase = vqa_pipeline(lowerCamelCase_ , top_k=1)
self.assertEqual(
lowerCamelCase_ , [
[{'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}],
[{'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}],
] , )
@require_torch
def UpperCAmelCase__ ( self) -> Union[str, Any]:
UpperCamelCase = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''')
UpperCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
UpperCamelCase = '''How many cats are there?'''
UpperCamelCase = vqa_pipeline(image=lowerCamelCase_ , question='''How many cats are there?''' , top_k=2)
self.assertEqual(
lowerCamelCase_ , [{'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}, {'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}])
UpperCamelCase = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2)
self.assertEqual(
lowerCamelCase_ , [{'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}, {'''score''': ANY(lowerCamelCase_), '''answer''': ANY(lowerCamelCase_)}])
@slow
@require_torch
def UpperCAmelCase__ ( self) -> Optional[int]:
UpperCamelCase = pipeline('''visual-question-answering''' , model='''dandelin/vilt-b32-finetuned-vqa''')
UpperCamelCase = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
UpperCamelCase = '''How many cats are there?'''
UpperCamelCase = vqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2)
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4) , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}])
UpperCamelCase = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2)
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4) , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}])
UpperCamelCase = vqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2)
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4) , [[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]] * 2 , )
@require_tf
@unittest.skip('''Visual question answering not implemented in TF''')
def UpperCAmelCase__ ( self) -> Optional[int]:
pass | 34 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
_A = 2
while True:
if is_prime(_snake_case ):
yield num
num += 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 10_001 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , _snake_case ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 2 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ :List[str] = logging.get_logger(__name__)
a_ :List[str] = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : Optional[Any] = '''owlvit_text_model'''
def __init__( self : List[str] , _lowercase : Optional[Any]=4_94_08 , _lowercase : Tuple=5_12 , _lowercase : List[str]=20_48 , _lowercase : List[Any]=12 , _lowercase : Any=8 , _lowercase : int=16 , _lowercase : int="quick_gelu" , _lowercase : Any=1E-5 , _lowercase : Union[str, Any]=0.0 , _lowercase : Union[str, Any]=0.02 , _lowercase : List[Any]=1.0 , _lowercase : List[str]=0 , _lowercase : List[Any]=4_94_06 , _lowercase : Dict=4_94_07 , **_lowercase : Tuple , ):
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
SCREAMING_SNAKE_CASE__ : Any = vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE__ : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE__ : int = initializer_factor
@classmethod
def lowercase__ ( cls : Any , _lowercase : Union[str, os.PathLike] , **_lowercase : Union[str, Any] ):
cls._set_token_in_kwargs(_lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = cls.get_config_dict(_lowercase , **_lowercase )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
SCREAMING_SNAKE_CASE__ : Dict = config_dict['''text_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(_lowercase , **_lowercase )
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : str = '''owlvit_vision_model'''
def __init__( self : Any , _lowercase : Dict=7_68 , _lowercase : List[str]=30_72 , _lowercase : Tuple=12 , _lowercase : Any=12 , _lowercase : Any=3 , _lowercase : Any=7_68 , _lowercase : Optional[int]=32 , _lowercase : Dict="quick_gelu" , _lowercase : Union[str, Any]=1E-5 , _lowercase : Optional[int]=0.0 , _lowercase : Dict=0.02 , _lowercase : Optional[Any]=1.0 , **_lowercase : List[Any] , ):
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Any = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = attention_dropout
SCREAMING_SNAKE_CASE__ : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
@classmethod
def lowercase__ ( cls : Any , _lowercase : Union[str, os.PathLike] , **_lowercase : Union[str, Any] ):
cls._set_token_in_kwargs(_lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
SCREAMING_SNAKE_CASE__ : str = 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(_lowercase , **_lowercase )
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : str = '''owlvit'''
lowerCamelCase : int = True
def __init__( self : Union[str, Any] , _lowercase : Dict=None , _lowercase : List[str]=None , _lowercase : Union[str, Any]=5_12 , _lowercase : Dict=2.6592 , _lowercase : Union[str, Any]=True , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
if text_config is None:
SCREAMING_SNAKE_CASE__ : List[Any] = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
SCREAMING_SNAKE_CASE__ : Any = OwlViTTextConfig(**_lowercase )
SCREAMING_SNAKE_CASE__ : int = OwlViTVisionConfig(**_lowercase )
SCREAMING_SNAKE_CASE__ : Any = projection_dim
SCREAMING_SNAKE_CASE__ : Dict = logit_scale_init_value
SCREAMING_SNAKE_CASE__ : Union[str, Any] = return_dict
SCREAMING_SNAKE_CASE__ : Any = 1.0
@classmethod
def lowercase__ ( cls : List[Any] , _lowercase : Union[str, os.PathLike] , **_lowercase : Dict ):
cls._set_token_in_kwargs(_lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = cls.get_config_dict(_lowercase , **_lowercase )
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(_lowercase , **_lowercase )
@classmethod
def lowercase__ ( cls : int , _lowercase : Dict , _lowercase : Dict , **_lowercase : List[str] ):
SCREAMING_SNAKE_CASE__ : str = {}
SCREAMING_SNAKE_CASE__ : List[str] = text_config
SCREAMING_SNAKE_CASE__ : Tuple = vision_config
return cls.from_dict(_lowercase , **_lowercase )
def lowercase__ ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE__ : str = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.text_config.to_dict()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE__ : int = self.__class__.model_type
return output
class lowercase ( _UpperCAmelCase ):
@property
def lowercase__ ( self : int ):
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowercase__ ( self : Dict ):
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowercase__ ( self : Any ):
return 1E-4
def lowercase__ ( self : Tuple , _lowercase : "ProcessorMixin" , _lowercase : int = -1 , _lowercase : int = -1 , _lowercase : Optional["TensorType"] = None , ):
SCREAMING_SNAKE_CASE__ : Optional[int] = super().generate_dummy_inputs(
processor.tokenizer , batch_size=_lowercase , seq_length=_lowercase , framework=_lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] = super().generate_dummy_inputs(
processor.image_processor , batch_size=_lowercase , framework=_lowercase )
return {**text_input_dict, **image_input_dict}
@property
def lowercase__ ( self : int ):
return 14
| 35 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase_ = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase_ = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase_ = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase_ = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase_ = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase_ = re.compile(r"""^\s*else:""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] ) -> Any:
if _re_test_backend.search(_snake_case ) is None:
return None
_A = [b[0] for b in _re_backend.findall(_snake_case )]
backends.sort()
return "_and_".join(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any ) -> Any:
with open(_snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.readlines()
_A = 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
_A = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
_A = 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 ):
_A = _re_one_line_import_struct.search(_snake_case ).groups()[0]
_A = re.findall(r'''\[([^\]]+)\]''' , _snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
_A = _re_import_struct_key_value.search(_snake_case )
if single_line_import_search is not None:
_A = [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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
_A = 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:
_A = _re_import_struct_add_many.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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:
_A = _re_between_brackets.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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
_A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_A = []
while (
line_index < len(_snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
_A = lines[line_index]
_A = _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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
_A = lines[line_index]
_A = _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
_A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] , _snake_case :Dict ) -> Any:
def find_duplicates(_snake_case :Any ):
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!"]
_A = []
for key in import_dict_objects.keys():
_A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_A = 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] ) ):
_A = '''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 SCREAMING_SNAKE_CASE_ ( ) -> int:
_A = []
for root, _, files in os.walk(_snake_case ):
if "__init__.py" in files:
_A = os.path.join(_snake_case , '''__init__.py''' )
_A = parse_init(_snake_case )
if objects is not None:
_A = analyze_results(*_snake_case )
if len(_snake_case ) > 0:
_A = 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 SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_A = []
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
_A = str((Path(_snake_case ) / folder).relative_to(_snake_case ) )
_A = short_path.replace(os.path.sep , '''.''' )
submodules.append(_snake_case )
for fname in files:
if fname == "__init__.py":
continue
_A = str((Path(_snake_case ) / fname).relative_to(_snake_case ) )
_A = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_snake_case )
return submodules
UpperCAmelCase_ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_A = direct_transformers_import(_snake_case )
_A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_snake_case , '''__init__.py''' ) , '''r''' ) as f:
_A = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , _snake_case ) ) )
_A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_snake_case ) > 0:
_A = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 2 | 0 |
def lowercase ( __A : list ) -> list:
'''simple docstring'''
if len(__A ) <= 1:
return lst
snake_case : List[Any] = 1
while i < len(__A ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case , snake_case : Tuple = lst[i], lst[i - 1]
i -= 1
if i == 0:
snake_case : int = 1
return lst
if __name__ == "__main__":
__lowercase : Dict = input('''Enter numbers separated by a comma:\n''').strip()
__lowercase : Dict = [int(item) for item in user_input.split(''',''')]
print(gnome_sort(unsorted))
| 36 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(_A)
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Optional[int] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : List[str] ) -> List[str]:
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def snake_case_ ( self : Any , __lowerCAmelCase : Dict=None , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=None ) -> int:
_A = {}
_A = {}
if prompt is not None:
_A = prompt
if generate_kwargs is not None:
_A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
_A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[str] , __lowerCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
return super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any]=None ) -> int:
_A = load_image(__lowerCAmelCase )
if prompt is not None:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(__lowerCAmelCase )} - but expected a single string. '''
'''Note also that one single text can be provided for conditional image to text generation.''' )
_A = self.model.config.model_type
if model_type == "git":
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids
_A = [self.tokenizer.cls_token_id] + input_ids
_A = torch.tensor(__lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
_A = self.image_processor(images=__lowerCAmelCase , header_text=__lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(__lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(__lowerCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A = None
return model_inputs
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict=None ) -> str:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __lowerCAmelCase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
_A = None
if generate_kwargs is None:
_A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A = model_inputs.pop(self.model.main_input_name )
_A = self.model.generate(__lowerCAmelCase , **__lowerCAmelCase , **__lowerCAmelCase )
return model_outputs
def snake_case_ ( self : Dict , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = []
for output_ids in model_outputs:
_A = {
'''generated_text''': self.tokenizer.decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , )
}
records.append(__lowerCAmelCase )
return records
| 2 | 0 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : List[str] = logging.get_logger(__name__)
def UpperCamelCase_ ( __a , __a=False ) -> str:
a__ : Optional[int] = []
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"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
a__ : Any = [(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 UpperCamelCase_ ( __a , __a , __a=False ) -> List[str]:
for i in range(config.num_hidden_layers ):
if base_model:
a__ : Union[str, Any] = ""
else:
a__ : str = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
a__ : Tuple = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
a__ : Any = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
a__ : List[str] = in_proj_weight[
: config.hidden_size, :
]
a__ : List[Any] = in_proj_bias[: config.hidden_size]
a__ : Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
a__ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
a__ : str = in_proj_weight[
-config.hidden_size :, :
]
a__ : Union[str, Any] = in_proj_bias[-config.hidden_size :]
def UpperCamelCase_ ( __a ) -> Union[str, Any]:
a__ : Union[str, Any] = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(__a , __a )
def UpperCamelCase_ ( __a , __a , __a ) -> Union[str, Any]:
a__ : Optional[int] = dct.pop(__a )
a__ : str = val
def UpperCamelCase_ ( ) -> Dict:
a__ : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
a__ : Dict = Image.open(requests.get(__a , stream=__a ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( __a , __a , __a=True ) -> str:
a__ : Tuple = ViTConfig()
# patch_size
if model_name[-1] == "8":
a__ : Any = 8
# set labels if required
if not base_model:
a__ : str = 1_000
a__ : Tuple = "huggingface/label-files"
a__ : int = "imagenet-1k-id2label.json"
a__ : Dict = json.load(open(hf_hub_download(__a , __a , repo_type="dataset" ) , "r" ) )
a__ : List[Any] = {int(__a ): v for k, v in idalabel.items()}
a__ : Tuple = idalabel
a__ : Any = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
a__ : Dict = 384
a__ : Tuple = 1_536
a__ : str = 12
a__ : Union[str, Any] = 6
# load original model from torch hub
a__ : List[Any] = torch.hub.load("facebookresearch/dino:main" , __a )
original_model.eval()
# load state_dict of original model, remove and rename some keys
a__ : Dict = original_model.state_dict()
if base_model:
remove_classification_head_(__a )
a__ : Any = create_rename_keys(__a , base_model=__a )
for src, dest in rename_keys:
rename_key(__a , __a , __a )
read_in_q_k_v(__a , __a , __a )
# load HuggingFace model
if base_model:
a__ : List[str] = ViTModel(__a , add_pooling_layer=__a ).eval()
else:
a__ : List[str] = ViTForImageClassification(__a ).eval()
model.load_state_dict(__a )
# Check outputs on an image, prepared by ViTImageProcessor
a__ : Optional[int] = ViTImageProcessor()
a__ : List[Any] = image_processor(images=prepare_img() , return_tensors="pt" )
a__ : List[str] = encoding["pixel_values"]
a__ : List[Any] = model(__a )
if base_model:
a__ : List[Any] = original_model(__a )
assert torch.allclose(__a , outputs.last_hidden_state[:, 0, :] , atol=1e-1 )
else:
a__ : List[str] = original_model(__a )
assert logits.shape == outputs.logits.shape
assert torch.allclose(__a , outputs.logits , atol=1e-3 )
Path(__a ).mkdir(exist_ok=__a )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__a )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__a )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""dino_vitb16""",
type=str,
help="""Name of the model trained with DINO you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--base_model""",
action="""store_true""",
help="""Whether to only convert the base model (no projection head weights).""",
)
parser.set_defaults(base_model=True)
UpperCamelCase : int = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 37 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_A = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 2 | 0 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
A_ : Tuple = logging.getLogger(__name__)
@dataclass
class __snake_case :
'''simple docstring'''
lowerCamelCase__ = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
lowerCamelCase__ = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
lowerCamelCase__ = field(
default=1_024 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
lowerCamelCase__ = field(default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def __UpperCamelCase ( self ):
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
snake_case__ : str = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
snake_case__ : List[str] = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __snake_case :
'''simple docstring'''
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
lowerCamelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
lowerCamelCase__ = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def UpperCamelCase__ ( ) -> int:
'''simple docstring'''
snake_case__ : 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.
snake_case__ , snake_case__ , snake_case__ : Any = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
snake_case__ , snake_case__ , snake_case__ : Optional[Any] = parser.parse_args_into_dataclasses()
# 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 )] , )
snake_case__ : str = training_args.get_process_log_level()
logger.setLevel(__magic_name__ )
datasets.utils.logging.set_verbosity(__magic_name__ )
transformers.utils.logging.set_verbosity(__magic_name__ )
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.
snake_case__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
snake_case__ : Dict = 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 training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
snake_case__ : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
snake_case__ : List[str] = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
snake_case__ : int = data_args.train_file.split(""".""" )[-1]
snake_case__ : int = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
snake_case__ : List[str] = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
snake_case__ : int = load_dataset("""csv""" , data_files=__magic_name__ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
snake_case__ : Tuple = load_dataset("""json""" , data_files=__magic_name__ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
snake_case__ : Dict = raw_datasets["""train"""].features["""label"""].names
snake_case__ : Optional[Any] = len(__magic_name__ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
snake_case__ : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
snake_case__ : Dict = TapexTokenizer.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 , add_prefix_space=__magic_name__ , )
snake_case__ : Tuple = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
snake_case__ : Dict = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
snake_case__ : Dict = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
snake_case__ : Union[str, Any] = {"""Refused""": 0, """Entailed""": 1}
snake_case__ : List[str] = {0: """Refused""", 1: """Entailed"""}
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}." )
snake_case__ : int = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(__magic_name__ : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(__magic_name__ : int ):
snake_case__ : List[Any] = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
snake_case__ : Optional[Any] = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
snake_case__ : Optional[int] = examples["""statement"""]
snake_case__ : Dict = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
snake_case__ : Optional[Any] = tokenizer(__magic_name__ , __magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ )
snake_case__ : Tuple = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
snake_case__ : List[str] = raw_datasets.map(
__magic_name__ , batched=__magic_name__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
snake_case__ : Optional[int] = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
snake_case__ : List[Any] = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
snake_case__ : int = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
snake_case__ : Any = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
snake_case__ : List[Any] = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
snake_case__ : Dict = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__magic_name__ ) ) , 3 ):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__magic_name__ : EvalPrediction ):
snake_case__ : Union[str, Any] = p.predictions[0] if isinstance(p.predictions , __magic_name__ ) else p.predictions
snake_case__ : Union[str, Any] = np.argmax(__magic_name__ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
snake_case__ : Union[str, Any] = default_data_collator
elif training_args.fpaa:
snake_case__ : Optional[int] = DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 )
else:
snake_case__ : str = None
# Initialize our Trainer
snake_case__ : str = Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__magic_name__ , tokenizer=__magic_name__ , data_collator=__magic_name__ , )
# Training
if training_args.do_train:
snake_case__ : Optional[Any] = None
if training_args.resume_from_checkpoint is not None:
snake_case__ : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
snake_case__ : Optional[int] = last_checkpoint
snake_case__ : Any = trainer.train(resume_from_checkpoint=__magic_name__ )
snake_case__ : str = train_result.metrics
snake_case__ : Dict = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__magic_name__ )
)
snake_case__ : Any = min(__magic_name__ , len(__magic_name__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __magic_name__ )
trainer.save_metrics("""train""" , __magic_name__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
snake_case__ : Optional[Any] = trainer.evaluate(eval_dataset=__magic_name__ )
snake_case__ : Union[str, Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__magic_name__ )
snake_case__ : List[str] = min(__magic_name__ , len(__magic_name__ ) )
trainer.log_metrics("""eval""" , __magic_name__ )
trainer.save_metrics("""eval""" , __magic_name__ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
snake_case__ : Dict = predict_dataset.remove_columns("""label""" )
snake_case__ : str = trainer.predict(__magic_name__ , metric_key_prefix="""predict""" ).predictions
snake_case__ : str = np.argmax(__magic_name__ , axis=1 )
snake_case__ : Tuple = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(__magic_name__ , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__magic_name__ ):
snake_case__ : List[str] = label_list[item]
writer.write(f"{index}\t{item}\n" )
snake_case__ : Union[str, Any] = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**__magic_name__ )
else:
trainer.create_model_card(**__magic_name__ )
def UpperCamelCase__ ( __magic_name__ : str ) -> List[str]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 38 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_A = kruskal(_snake_case , _snake_case )
_A = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_snake_case ) == sorted(_snake_case )
| 2 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 39 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 40 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return False
_A = 0
_A = 0
_A = 1
# Calculating the hash of pattern and substring of text
for i in range(_snake_case ):
_A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def SCREAMING_SNAKE_CASE_ ( ) -> None:
_A = '''abc1abc12'''
_A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_A = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(_snake_case , _snake_case ) and not rabin_karp(_snake_case , _snake_case )
# Test 2)
_A = '''ABABX'''
_A = '''ABABZABABYABABX'''
assert rabin_karp(_snake_case , _snake_case )
# Test 3)
_A = '''AAAB'''
_A = '''ABAAAAAB'''
assert rabin_karp(_snake_case , _snake_case )
# Test 4)
_A = '''abcdabcy'''
_A = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(_snake_case , _snake_case )
# Test 5)
_A = '''Lü'''
_A = '''Lüsai'''
assert rabin_karp(_snake_case , _snake_case )
_A = '''Lue'''
assert not rabin_karp(_snake_case , _snake_case )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 2 | 0 |
'''simple docstring'''
from typing import Any
import numpy as np
def _A ( A__ ):
"""simple docstring"""
return np.array_equal(A__ , matrix.conjugate().T )
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = v.conjugate().T
__lowercase = v_star.dot(A__ )
assert isinstance(A__ , np.ndarray )
return (v_star_dot.dot(A__ )) / (v_star.dot(A__ ))
def _A ( ):
"""simple docstring"""
__lowercase = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]] )
__lowercase = np.array([[1], [2], [3]] )
assert is_hermitian(A__ ), F"{a} is not hermitian."
print(rayleigh_quotient(A__ , A__ ) )
__lowercase = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(A__ ), F"{a} is not hermitian."
assert rayleigh_quotient(A__ , A__ ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 41 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
UpperCAmelCase_ = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
UpperCAmelCase_ = """</w>"""
UpperCAmelCase_ = """@@ """
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[Any] ) -> List[str]:
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
# Speech2Text2 has no max input length
UpperCAmelCase_ = {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Dict = VOCAB_FILES_NAMES
a__ : str = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : List[Any] = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str]="<s>" , __lowerCAmelCase : Tuple="<pad>" , __lowerCAmelCase : Optional[Any]="</s>" , __lowerCAmelCase : Dict="<unk>" , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=None , **__lowerCAmelCase : str , ) -> Dict:
super().__init__(
unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , do_lower_case=__lowerCAmelCase , **__lowerCAmelCase , )
_A = do_lower_case
with open(__lowerCAmelCase , encoding='''utf-8''' ) as vocab_handle:
_A = json.load(__lowerCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
_A = None
_A = None
else:
with open(__lowerCAmelCase , encoding='''utf-8''' ) as merges_handle:
_A = merges_handle.read().split('''\n''' )[:-1]
_A = [tuple(merge.split()[:2] ) for merge in merges]
_A = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_A = {}
@property
def snake_case_ ( self : List[str] ) -> int:
return len(self.decoder )
def snake_case_ ( self : Dict ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
_A = get_pairs(__lowerCAmelCase )
if not pairs:
return token
while True:
_A = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(__lowerCAmelCase ):
try:
_A = word.index(__lowerCAmelCase , __lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__lowerCAmelCase )
_A = new_word
if len(__lowerCAmelCase ) == 1:
break
else:
_A = get_pairs(__lowerCAmelCase )
_A = ''' '''.join(__lowerCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
_A = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(__lowerCAmelCase ):
_A = word.replace(__lowerCAmelCase , '''''' )
_A = word.replace(''' ''' , __lowerCAmelCase )
_A = word
return word
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Tuple ) -> Optional[int]:
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
_A = text.lower()
_A = text.split()
_A = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str ) -> int:
return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) )
def snake_case_ ( self : str , __lowerCAmelCase : int ) -> str:
_A = self.decoder.get(__lowerCAmelCase , self.unk_token )
return result
def snake_case_ ( self : List[str] , __lowerCAmelCase : List[str] ) -> str:
_A = ''' '''.join(__lowerCAmelCase )
# make sure @@ tokens are concatenated
_A = ''''''.join(string.split(__lowerCAmelCase ) )
return string
def snake_case_ ( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCAmelCase , ensure_ascii=__lowerCAmelCase ) + '''\n''' )
_A = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
_A = token_index
writer.write(''' '''.join(__lowerCAmelCase ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 2 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ = logging.get_logger(__name__)
A_ = {
"shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'nat'
SCREAMING_SNAKE_CASE_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=[3, 4, 6, 5] , SCREAMING_SNAKE_CASE_=[2, 4, 8, 16] , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3.0 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1E-5 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embed_dim
lowerCamelCase_ = depths
lowerCamelCase_ = len(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = num_heads
lowerCamelCase_ = kernel_size
lowerCamelCase_ = mlp_ratio
lowerCamelCase_ = qkv_bias
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = hidden_act
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase_ = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_ ) - 1) )
lowerCamelCase_ = layer_scale_init_value
lowerCamelCase_ = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(SCREAMING_SNAKE_CASE_ ) + 1 )]
lowerCamelCase_ ,lowerCamelCase_ = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE_ , out_indices=SCREAMING_SNAKE_CASE_ , stage_names=self.stage_names )
| 42 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 2
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Optional[int] ) -> None:
_A = []
_A = {}
_A = 0
def __len__( self : str ) -> int:
return self.elements
def __repr__( self : Optional[int] ) -> str:
return str(self.heap )
def snake_case_ ( self : str ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
_A = self.elements
self.elements += 1
self._bubble_up(__lowerCAmelCase )
def snake_case_ ( self : Tuple ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
_A , _A = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
_A , _A = self.heap[0]
self._bubble_down(__lowerCAmelCase )
return elem
def snake_case_ ( self : int , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Update the weight of the given key
_A = self.position_map[elem]
_A = (elem, weight)
if position > 0:
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
_A = self.position_map[elem]
if curr_pos == 0:
return None
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[curr_pos]
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_up(__lowerCAmelCase )
return None
def snake_case_ ( self : Dict , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
_A = self.position_map[elem]
_A , _A = self.heap[curr_pos]
_A = get_child_left_position(__lowerCAmelCase )
_A = get_child_right_position(__lowerCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
_A , _A = self.heap[child_left_position]
_A , _A = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
if child_left_position < self.elements:
_A , _A = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
else:
return None
if child_right_position < self.elements:
_A , _A = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
return None
def snake_case_ ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
# Swap the nodes at the given positions
_A = self.heap[nodea_pos][0]
_A = self.heap[nodea_pos][0]
_A , _A = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
_A = nodea_pos
_A = nodea_pos
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : str ) -> None:
_A = {}
_A = 0
def __repr__( self : str ) -> str:
return str(self.connections )
def __len__( self : Dict ) -> int:
return self.nodes
def snake_case_ ( self : Any , __lowerCAmelCase : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
_A = {}
self.nodes += 1
def snake_case_ ( self : str , __lowerCAmelCase : T , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__lowerCAmelCase )
self.add_node(__lowerCAmelCase )
_A = weight
_A = weight
def SCREAMING_SNAKE_CASE_ ( _snake_case :GraphUndirectedWeighted[T] , ) -> tuple[dict[T, int], dict[T, T | None]]:
_A = {node: maxsize for node in graph.connections}
_A = {node: None for node in graph.connections}
_A = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_snake_case , _snake_case )
if priority_queue.is_empty():
return dist, parent
# initialization
_A = priority_queue.extract_min()
_A = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
# running prim's algorithm
while not priority_queue.is_empty():
_A = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
return dist, parent
| 2 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class _a ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self: Any ) -> str:
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModel.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModel.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: Tuple ) -> Optional[Any]:
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForPreTraining.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: List[Any] ) -> Tuple:
"""simple docstring"""
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForCausalLM.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: Dict ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForMaskedLM.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: Union[str, Any] ) -> List[str]:
"""simple docstring"""
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
UpperCamelCase_ , output_loading_info=UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: int ) -> int:
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: str ) -> Dict:
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase_ ) , 14_410 )
lowercase__ = AutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase_ ) , 14_410 )
def lowerCamelCase_ ( self: Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_pt=UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase_ ) , 14_410 )
lowercase__ = AutoModelWithLMHead.from_pretrained(UpperCamelCase_ , from_tf=UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(model.num_parameters() , 14_410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase_ ) , 14_410 )
| 43 |
import os
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 logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [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]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
_lowerCamelCase : str = len(bin(_lowerCAmelCase )[3:] )
_lowerCamelCase : List[str] = bin(abs(_lowerCAmelCase ) - (1 << binary_number_length) )[3:]
_lowerCamelCase : List[str] = (
(
"1"
+ "0" * (binary_number_length - len(_lowerCAmelCase ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod() | 44 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_snake_case )
return explored
UpperCAmelCase_ = {
"""A""": ["""B""", """C""", """D"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F"""],
"""D""": ["""B""", """D"""],
"""E""": ["""B""", """F"""],
"""F""": ["""C""", """E""", """G"""],
"""G""": ["""F"""],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, """A"""))
| 2 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
_snake_case : str = """data2vec-audio"""
def __init__( self :List[Any] , lowerCamelCase__ :List[str]=32 , lowerCamelCase__ :Optional[Any]=7_68 , lowerCamelCase__ :int=12 , lowerCamelCase__ :List[Any]=12 , lowerCamelCase__ :List[str]=30_72 , lowerCamelCase__ :int="gelu" , lowerCamelCase__ :Tuple=0.1 , lowerCamelCase__ :List[str]=0.1 , lowerCamelCase__ :Dict=0.1 , lowerCamelCase__ :Optional[Any]=0.0 , lowerCamelCase__ :Any=0.1 , lowerCamelCase__ :Optional[int]=0.1 , lowerCamelCase__ :Dict=0.02 , lowerCamelCase__ :List[str]=1e-5 , lowerCamelCase__ :Union[str, Any]="gelu" , lowerCamelCase__ :Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , lowerCamelCase__ :int=(5, 2, 2, 2, 2, 2, 2) , lowerCamelCase__ :Dict=(10, 3, 3, 3, 3, 2, 2) , lowerCamelCase__ :List[Any]=False , lowerCamelCase__ :List[Any]=16 , lowerCamelCase__ :Union[str, Any]=19 , lowerCamelCase__ :int=5 , lowerCamelCase__ :Dict=0.05 , lowerCamelCase__ :Union[str, Any]=10 , lowerCamelCase__ :Optional[int]=2 , lowerCamelCase__ :int=0.0 , lowerCamelCase__ :Dict=10 , lowerCamelCase__ :List[str]=0 , lowerCamelCase__ :Dict="sum" , lowerCamelCase__ :Optional[int]=False , lowerCamelCase__ :int=False , lowerCamelCase__ :Dict=2_56 , lowerCamelCase__ :Any=(5_12, 5_12, 5_12, 5_12, 15_00) , lowerCamelCase__ :int=(5, 3, 3, 1, 1) , lowerCamelCase__ :Union[str, Any]=(1, 2, 3, 1, 1) , lowerCamelCase__ :List[Any]=5_12 , lowerCamelCase__ :str=0 , lowerCamelCase__ :str=1 , lowerCamelCase__ :Optional[int]=2 , lowerCamelCase__ :Optional[Any]=False , lowerCamelCase__ :Optional[Any]=3 , lowerCamelCase__ :List[str]=2 , lowerCamelCase__ :List[str]=3 , lowerCamelCase__ :List[str]=None , **lowerCamelCase__ :Any , ):
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
UpperCamelCase__ :int = hidden_size
UpperCamelCase__ :int = feat_extract_activation
UpperCamelCase__ :str = list(lowerCamelCase__ )
UpperCamelCase__ :Tuple = list(lowerCamelCase__ )
UpperCamelCase__ :List[Any] = list(lowerCamelCase__ )
UpperCamelCase__ :Union[str, Any] = conv_bias
UpperCamelCase__ :Any = num_conv_pos_embeddings
UpperCamelCase__ :Optional[Any] = num_conv_pos_embedding_groups
UpperCamelCase__ :Optional[Any] = conv_pos_kernel_size
UpperCamelCase__ :int = len(self.conv_dim )
UpperCamelCase__ :List[str] = num_hidden_layers
UpperCamelCase__ :List[Any] = intermediate_size
UpperCamelCase__ :Any = hidden_act
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :int = hidden_dropout
UpperCamelCase__ :Dict = attention_dropout
UpperCamelCase__ :Any = activation_dropout
UpperCamelCase__ :int = feat_proj_dropout
UpperCamelCase__ :List[Any] = final_dropout
UpperCamelCase__ :List[Any] = layerdrop
UpperCamelCase__ :List[Any] = layer_norm_eps
UpperCamelCase__ :Any = initializer_range
UpperCamelCase__ :Any = vocab_size
UpperCamelCase__ :str = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCamelCase__ :Tuple = mask_time_prob
UpperCamelCase__ :Union[str, Any] = mask_time_length
UpperCamelCase__ :Any = mask_time_min_masks
UpperCamelCase__ :Tuple = mask_feature_prob
UpperCamelCase__ :Optional[Any] = mask_feature_length
UpperCamelCase__ :Tuple = mask_feature_min_masks
# ctc loss
UpperCamelCase__ :List[str] = ctc_loss_reduction
UpperCamelCase__ :Optional[int] = ctc_zero_infinity
# adapter
UpperCamelCase__ :Optional[int] = add_adapter
UpperCamelCase__ :List[str] = adapter_kernel_size
UpperCamelCase__ :Dict = adapter_stride
UpperCamelCase__ :Optional[Any] = num_adapter_layers
UpperCamelCase__ :Union[str, Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCamelCase__ :Any = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCamelCase__ :List[str] = list(lowerCamelCase__ )
UpperCamelCase__ :int = list(lowerCamelCase__ )
UpperCamelCase__ :List[str] = list(lowerCamelCase__ )
UpperCamelCase__ :Union[str, Any] = xvector_output_dim
@property
def __a ( self :Optional[Any] ):
return math.prod(self.conv_stride ) | 45 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : int = []
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
F"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
F"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
F"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
F"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase : Optional[Any] = []
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def lowerCamelCase_( _lowerCamelCase ) -> List[Any]:
'''simple docstring'''
_lowerCamelCase : Tuple = []
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token") )
return token
def lowerCamelCase_( ) -> int:
'''simple docstring'''
_lowerCamelCase : List[str] = []
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> List[Any]:
'''simple docstring'''
_lowerCamelCase : Optional[int] = "imagenet-1k-id2label.json"
_lowerCamelCase : List[Any] = 1000
_lowerCamelCase : str = "huggingface/label-files"
_lowerCamelCase : List[Any] = num_labels
_lowerCamelCase : List[Any] = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) ) , "r" ) )
_lowerCamelCase : Optional[int] = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCamelCase : Any = idalabel
_lowerCamelCase : List[Any] = {v: k for k, v in idalabel.items()}
_lowerCamelCase : List[str] = CvtConfig(num_labels=_lowerCamelCase , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
_lowerCamelCase : List[str] = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
_lowerCamelCase : Dict = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
_lowerCamelCase : Tuple = [2, 2, 20]
_lowerCamelCase : List[str] = [3, 12, 16]
_lowerCamelCase : Optional[Any] = [192, 768, 1024]
_lowerCamelCase : Union[str, Any] = CvtForImageClassification(_lowerCamelCase )
_lowerCamelCase : Any = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
_lowerCamelCase : Union[str, Any] = image_size
_lowerCamelCase : List[Any] = torch.load(_lowerCamelCase , map_location=torch.device("cpu" ) )
_lowerCamelCase : Any = OrderedDict()
_lowerCamelCase : Dict = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
_lowerCamelCase : Tuple = list_of_state_dict + cls_token(_lowerCamelCase )
_lowerCamelCase : Optional[int] = list_of_state_dict + embeddings(_lowerCamelCase )
for cnt in range(config.depth[idx] ):
_lowerCamelCase : Dict = list_of_state_dict + attention(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_lowerCamelCase )
for i in range(len(_lowerCamelCase ) ):
_lowerCamelCase : Optional[Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_lowerCamelCase )
model.save_pretrained(_lowerCamelCase )
image_processor.save_pretrained(_lowerCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_lowerCAmelCase : int = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
_lowerCAmelCase : Tuple = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path) | 46 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Any = "xlnet"
a__ : Dict = ["mems"]
a__ : List[str] = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , __lowerCAmelCase : Dict=3_20_00 , __lowerCAmelCase : List[str]=10_24 , __lowerCAmelCase : Dict=24 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=40_96 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]="bi" , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Optional[Any]=5_12 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Any="last" , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Tuple="tanh" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : str=5 , __lowerCAmelCase : str=5 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=2 , **__lowerCAmelCase : List[str] , ) -> Tuple:
_A = vocab_size
_A = d_model
_A = n_layer
_A = n_head
if d_model % n_head != 0:
raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __lowerCAmelCase , )
_A = kwargs['''use_cache''']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def snake_case_ ( self : Optional[Any] ) -> Union[str, Any]:
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 snake_case_ ( self : Tuple , __lowerCAmelCase : Optional[Any] ) -> Dict:
# 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.''' )
| 2 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE__ = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
SCREAMING_SNAKE_CASE__ = {
'''allenai/led-base-16384''': 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCAmelCase__ ( ):
__a : List[str] = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
__a : str = bs[:]
__a : List[Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCamelCase_ )
cs.append(2**8 + n )
n += 1
__a : Dict = [chr(lowerCamelCase_ ) for n in cs]
return dict(zip(lowerCamelCase_ , lowerCamelCase_ ) )
def UpperCAmelCase__ ( lowerCamelCase_ : Optional[Any] ):
__a : Optional[Any] = set()
__a : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__a : Dict = char
return pairs
class _UpperCamelCase( __lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Tuple = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : int = ['''input_ids''', '''attention_mask''']
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple="replace" , SCREAMING_SNAKE_CASE__ : int="<s>" , SCREAMING_SNAKE_CASE__ : List[str]="</s>" , SCREAMING_SNAKE_CASE__ : int="</s>" , SCREAMING_SNAKE_CASE__ : List[Any]="<s>" , SCREAMING_SNAKE_CASE__ : List[Any]="<unk>" , SCREAMING_SNAKE_CASE__ : Union[str, Any]="<pad>" , SCREAMING_SNAKE_CASE__ : List[Any]="<mask>" , SCREAMING_SNAKE_CASE__ : Optional[int]=False , **SCREAMING_SNAKE_CASE__ : Optional[Any] , ):
'''simple docstring'''
__a : Union[str, Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else bos_token
__a : List[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else eos_token
__a : int = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else sep_token
__a : Optional[int] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else cls_token
__a : Optional[int] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else unk_token
__a : Dict = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__a : Tuple = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
super().__init__(
errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
with open(SCREAMING_SNAKE_CASE__ , encoding='utf-8' ) as vocab_handle:
__a : List[Any] = json.load(SCREAMING_SNAKE_CASE__ )
__a : List[str] = {v: k for k, v in self.encoder.items()}
__a : Dict = errors # how to handle errors in decoding
__a : List[Any] = bytes_to_unicode()
__a : List[Any] = {v: k for k, v in self.byte_encoder.items()}
with open(SCREAMING_SNAKE_CASE__ , encoding='utf-8' ) as merges_handle:
__a : str = merges_handle.read().split('\n' )[1:-1]
__a : List[str] = [tuple(merge.split() ) for merge in bpe_merges]
__a : str = dict(zip(SCREAMING_SNAKE_CASE__ , range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
__a : Tuple = {}
__a : Union[str, Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__a : Optional[Any] = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
return len(self.encoder )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : Dict ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__a : List[Any] = tuple(SCREAMING_SNAKE_CASE__ )
__a : Tuple = get_pairs(SCREAMING_SNAKE_CASE__ )
if not pairs:
return token
while True:
__a : Tuple = min(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__a , __a : str = bigram
__a : Optional[Any] = []
__a : List[str] = 0
while i < len(SCREAMING_SNAKE_CASE__ ):
try:
__a : str = word.index(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__a : List[Any] = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__a : Optional[int] = tuple(SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = new_word
if len(SCREAMING_SNAKE_CASE__ ) == 1:
break
else:
__a : Dict = get_pairs(SCREAMING_SNAKE_CASE__ )
__a : Tuple = ' '.join(SCREAMING_SNAKE_CASE__ )
__a : List[Any] = word
return word
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : Any ):
'''simple docstring'''
__a : int = []
for token in re.findall(self.pat , SCREAMING_SNAKE_CASE__ ):
__a : Optional[Any] = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__ ).split(' ' ) )
return bpe_tokens
def __lowerCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
return self.encoder.get(SCREAMING_SNAKE_CASE__ , self.encoder.get(self.unk_token ) )
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
return self.decoder.get(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
__a : Optional[Any] = ''.join(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__a : int = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__a : Optional[int] = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(SCREAMING_SNAKE_CASE__ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ ) + '\n' )
__a : List[Any] = 0
with open(SCREAMING_SNAKE_CASE__ , '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 SCREAMING_SNAKE_CASE__ : 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!' )
__a : Optional[int] = token_index
writer.write(' '.join(SCREAMING_SNAKE_CASE__ ) + '\n' )
index += 1
return vocab_file, merge_file
def __lowerCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__a : Union[str, Any] = [self.cls_token_id]
__a : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __lowerCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE__ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ):
'''simple docstring'''
__a : Any = [self.sep_token_id]
__a : Union[str, 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]
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str]=False , **SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
__a : Dict = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__ ) > 0 and not text[0].isspace()):
__a : Tuple = ' ' + text
return (text, kwargs)
def __lowerCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Union[Dict[str, EncodedInput], BatchEncoding] , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , ):
'''simple docstring'''
__a : Union[str, Any] = super()._pad(
encoded_inputs=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , padding_strategy=SCREAMING_SNAKE_CASE__ , pad_to_multiple_of=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , )
# Load from model defaults
if return_attention_mask is None:
__a : Tuple = 'attention_mask' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__a : Optional[Any] = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__a : int = len(encoded_inputs['global_attention_mask'] ) != len(SCREAMING_SNAKE_CASE__ )
if needs_to_be_padded:
__a : Any = len(SCREAMING_SNAKE_CASE__ ) - len(encoded_inputs['global_attention_mask'] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__a : Optional[Any] = (
encoded_inputs['global_attention_mask'] + [-1] * difference
)
elif self.padding_side == "left":
__a : List[str] = [-1] * difference + encoded_inputs[
'global_attention_mask'
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return encoded_inputs
| 47 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(_snake_case ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_snake_case ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_snake_case ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class A ( SCREAMING_SNAKE_CASE__ ):
def __get__( self : List[Any] , __magic_name__ : Dict , __magic_name__ : Union[str, Any]=None ):
"""simple docstring"""
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute" )
lowerCAmelCase__ = "__cached_" + self.fget.__name__
lowerCAmelCase__ = getattr(__magic_name__ , __magic_name__ , __magic_name__ )
if cached is None:
lowerCAmelCase__ = self.fget(__magic_name__ )
setattr(__magic_name__ , __magic_name__ , __magic_name__ )
return cached
def A ( UpperCamelCase_ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase__ = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"""invalid truth value {val!r}""" )
def A ( UpperCamelCase_ : List[Any] ) -> Any:
'''simple docstring'''
if is_torch_fx_proxy(UpperCamelCase_ ):
return True
if is_torch_available():
import torch
if isinstance(UpperCamelCase_ , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(UpperCamelCase_ , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(UpperCamelCase_ , (jnp.ndarray, Tracer) ):
return True
return isinstance(UpperCamelCase_ , np.ndarray )
def A ( UpperCamelCase_ : Any ) -> Any:
'''simple docstring'''
return isinstance(UpperCamelCase_ , np.ndarray )
def A ( UpperCamelCase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return _is_numpy(UpperCamelCase_ )
def A ( UpperCamelCase_ : Union[str, Any] ) -> Dict:
'''simple docstring'''
import torch
return isinstance(UpperCamelCase_ , torch.Tensor )
def A ( UpperCamelCase_ : str ) -> Optional[Any]:
'''simple docstring'''
return False if not is_torch_available() else _is_torch(UpperCamelCase_ )
def A ( UpperCamelCase_ : Union[str, Any] ) -> str:
'''simple docstring'''
import torch
return isinstance(UpperCamelCase_ , torch.device )
def A ( UpperCamelCase_ : Any ) -> str:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(UpperCamelCase_ )
def A ( UpperCamelCase_ : Optional[Any] ) -> List[str]:
'''simple docstring'''
import torch
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
if hasattr(UpperCamelCase_ , UpperCamelCase_ ):
lowerCAmelCase__ = getattr(UpperCamelCase_ , UpperCamelCase_ )
else:
return False
return isinstance(UpperCamelCase_ , torch.dtype )
def A ( UpperCamelCase_ : int ) -> Any:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(UpperCamelCase_ )
def A ( UpperCamelCase_ : str ) -> Tuple:
'''simple docstring'''
import tensorflow as tf
return isinstance(UpperCamelCase_ , tf.Tensor )
def A ( UpperCamelCase_ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(UpperCamelCase_ )
def A ( UpperCamelCase_ : Optional[int] ) -> str:
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(UpperCamelCase_ , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(UpperCamelCase_ )
return type(UpperCamelCase_ ) == tf.Tensor
def A ( UpperCamelCase_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCamelCase_ )
def A ( UpperCamelCase_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(UpperCamelCase_ , jnp.ndarray )
def A ( UpperCamelCase_ : Union[str, Any] ) -> str:
'''simple docstring'''
return False if not is_flax_available() else _is_jax(UpperCamelCase_ )
def A ( UpperCamelCase_ : List[str] ) -> Tuple:
'''simple docstring'''
if isinstance(UpperCamelCase_ , (dict, UserDict) ):
return {k: to_py_obj(UpperCamelCase_ ) for k, v in obj.items()}
elif isinstance(UpperCamelCase_ , (list, tuple) ):
return [to_py_obj(UpperCamelCase_ ) for o in obj]
elif is_tf_tensor(UpperCamelCase_ ):
return obj.numpy().tolist()
elif is_torch_tensor(UpperCamelCase_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(UpperCamelCase_ ):
return np.asarray(UpperCamelCase_ ).tolist()
elif isinstance(UpperCamelCase_ , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( UpperCamelCase_ : Tuple ) -> Optional[Any]:
'''simple docstring'''
if isinstance(UpperCamelCase_ , (dict, UserDict) ):
return {k: to_numpy(UpperCamelCase_ ) for k, v in obj.items()}
elif isinstance(UpperCamelCase_ , (list, tuple) ):
return np.array(UpperCamelCase_ )
elif is_tf_tensor(UpperCamelCase_ ):
return obj.numpy()
elif is_torch_tensor(UpperCamelCase_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(UpperCamelCase_ ):
return np.asarray(UpperCamelCase_ )
else:
return obj
class A ( SCREAMING_SNAKE_CASE__ ):
def __SCREAMING_SNAKE_CASE ( self : List[Any] ):
"""simple docstring"""
lowerCAmelCase__ = fields(self )
# Safety and consistency checks
if not len(__magic_name__ ):
raise ValueError(f"""{self.__class__.__name__} has no fields.""" )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f"""{self.__class__.__name__} should not have more than one required field.""" )
lowerCAmelCase__ = getattr(self , class_fields[0].name )
lowerCAmelCase__ = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__magic_name__ ):
if isinstance(__magic_name__ , __magic_name__ ):
lowerCAmelCase__ = first_field.items()
lowerCAmelCase__ = True
else:
try:
lowerCAmelCase__ = iter(__magic_name__ )
lowerCAmelCase__ = True
except TypeError:
lowerCAmelCase__ = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__magic_name__ ):
if (
not isinstance(__magic_name__ , (list, tuple) )
or not len(__magic_name__ ) == 2
or not isinstance(element[0] , __magic_name__ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
lowerCAmelCase__ = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"""Cannot set key/value for {element}. It needs to be a tuple (key, value).""" )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
lowerCAmelCase__ = element[1]
elif first_field is not None:
lowerCAmelCase__ = first_field
else:
for field in class_fields:
lowerCAmelCase__ = getattr(self , field.name )
if v is not None:
lowerCAmelCase__ = v
def __delitem__( self : Union[str, Any] , *__magic_name__ : Tuple , **__magic_name__ : Dict ):
"""simple docstring"""
raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""" )
def __SCREAMING_SNAKE_CASE ( self : Tuple , *__magic_name__ : Tuple , **__magic_name__ : Dict ):
"""simple docstring"""
raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""" )
def __SCREAMING_SNAKE_CASE ( self : int , *__magic_name__ : Union[str, Any] , **__magic_name__ : Union[str, Any] ):
"""simple docstring"""
raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""" )
def __SCREAMING_SNAKE_CASE ( self : List[Any] , *__magic_name__ : List[Any] , **__magic_name__ : List[Any] ):
"""simple docstring"""
raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance.""" )
def __getitem__( self : Union[str, Any] , __magic_name__ : Optional[int] ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
lowerCAmelCase__ = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any] ):
"""simple docstring"""
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__magic_name__ , __magic_name__ )
super().__setattr__(__magic_name__ , __magic_name__ )
def __setitem__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : List[Any] ):
"""simple docstring"""
super().__setitem__(__magic_name__ , __magic_name__ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__magic_name__ , __magic_name__ )
def __SCREAMING_SNAKE_CASE ( self : Tuple ):
"""simple docstring"""
return tuple(self[k] for k in self.keys() )
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
@classmethod
def __SCREAMING_SNAKE_CASE ( cls : int , __magic_name__ : Optional[int] ):
"""simple docstring"""
raise ValueError(
f"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}""" )
class A ( SCREAMING_SNAKE_CASE__ ):
snake_case__ :int = 'longest'
snake_case__ :Optional[Any] = 'max_length'
snake_case__ :str = 'do_not_pad'
class A ( SCREAMING_SNAKE_CASE__ ):
snake_case__ :Any = 'pt'
snake_case__ :Union[str, Any] = 'tf'
snake_case__ :Any = 'np'
snake_case__ :List[str] = 'jax'
class A :
def __init__( self : Dict , __magic_name__ : List[ContextManager] ):
"""simple docstring"""
lowerCAmelCase__ = context_managers
lowerCAmelCase__ = ExitStack()
def __enter__( self : str ):
"""simple docstring"""
for context_manager in self.context_managers:
self.stack.enter_context(__magic_name__ )
def __exit__( self : Dict , *__magic_name__ : Dict , **__magic_name__ : int ):
"""simple docstring"""
self.stack.__exit__(*__magic_name__ , **__magic_name__ )
def A ( UpperCamelCase_ : Dict ) -> List[str]:
'''simple docstring'''
lowerCAmelCase__ = infer_framework(UpperCamelCase_ )
if framework == "tf":
lowerCAmelCase__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase__ = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase__ = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( UpperCamelCase_ : int ) -> int:
'''simple docstring'''
lowerCAmelCase__ = model_class.__name__
lowerCAmelCase__ = infer_framework(UpperCamelCase_ )
if framework == "tf":
lowerCAmelCase__ = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase__ = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase__ = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( UpperCamelCase_ : MutableMapping , UpperCamelCase_ : str = "" , UpperCamelCase_ : str = "." ) -> List[Any]:
'''simple docstring'''
def _flatten_dict(UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int]="" , UpperCamelCase_ : Any="." ):
for k, v in d.items():
lowerCAmelCase__ = str(UpperCamelCase_ ) + delimiter + str(UpperCamelCase_ ) if parent_key else k
if v and isinstance(UpperCamelCase_ , UpperCamelCase_ ):
yield from flatten_dict(UpperCamelCase_ , UpperCamelCase_ , delimiter=UpperCamelCase_ ).items()
else:
yield key, v
return dict(_flatten_dict(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) )
@contextmanager
def A ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : bool = False ) -> Any:
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Union[str, Any]=None ) -> Tuple:
'''simple docstring'''
if is_numpy_array(UpperCamelCase_ ):
return np.transpose(UpperCamelCase_ , axes=UpperCamelCase_ )
elif is_torch_tensor(UpperCamelCase_ ):
return array.T if axes is None else array.permute(*UpperCamelCase_ )
elif is_tf_tensor(UpperCamelCase_ ):
import tensorflow as tf
return tf.transpose(UpperCamelCase_ , perm=UpperCamelCase_ )
elif is_jax_tensor(UpperCamelCase_ ):
return jnp.transpose(UpperCamelCase_ , axes=UpperCamelCase_ )
else:
raise ValueError(F"""Type not supported for transpose: {type(UpperCamelCase_ )}.""" )
def A ( UpperCamelCase_ : str , UpperCamelCase_ : int ) -> Dict:
'''simple docstring'''
if is_numpy_array(UpperCamelCase_ ):
return np.reshape(UpperCamelCase_ , UpperCamelCase_ )
elif is_torch_tensor(UpperCamelCase_ ):
return array.reshape(*UpperCamelCase_ )
elif is_tf_tensor(UpperCamelCase_ ):
import tensorflow as tf
return tf.reshape(UpperCamelCase_ , UpperCamelCase_ )
elif is_jax_tensor(UpperCamelCase_ ):
return jnp.reshape(UpperCamelCase_ , UpperCamelCase_ )
else:
raise ValueError(F"""Type not supported for reshape: {type(UpperCamelCase_ )}.""" )
def A ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[str]=None ) -> Optional[int]:
'''simple docstring'''
if is_numpy_array(UpperCamelCase_ ):
return np.squeeze(UpperCamelCase_ , axis=UpperCamelCase_ )
elif is_torch_tensor(UpperCamelCase_ ):
return array.squeeze() if axis is None else array.squeeze(dim=UpperCamelCase_ )
elif is_tf_tensor(UpperCamelCase_ ):
import tensorflow as tf
return tf.squeeze(UpperCamelCase_ , axis=UpperCamelCase_ )
elif is_jax_tensor(UpperCamelCase_ ):
return jnp.squeeze(UpperCamelCase_ , axis=UpperCamelCase_ )
else:
raise ValueError(F"""Type not supported for squeeze: {type(UpperCamelCase_ )}.""" )
def A ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if is_numpy_array(UpperCamelCase_ ):
return np.expand_dims(UpperCamelCase_ , UpperCamelCase_ )
elif is_torch_tensor(UpperCamelCase_ ):
return array.unsqueeze(dim=UpperCamelCase_ )
elif is_tf_tensor(UpperCamelCase_ ):
import tensorflow as tf
return tf.expand_dims(UpperCamelCase_ , axis=UpperCamelCase_ )
elif is_jax_tensor(UpperCamelCase_ ):
return jnp.expand_dims(UpperCamelCase_ , axis=UpperCamelCase_ )
else:
raise ValueError(F"""Type not supported for expand_dims: {type(UpperCamelCase_ )}.""" )
def A ( UpperCamelCase_ : List[str] ) -> Dict:
'''simple docstring'''
if is_numpy_array(UpperCamelCase_ ):
return np.size(UpperCamelCase_ )
elif is_torch_tensor(UpperCamelCase_ ):
return array.numel()
elif is_tf_tensor(UpperCamelCase_ ):
import tensorflow as tf
return tf.size(UpperCamelCase_ )
elif is_jax_tensor(UpperCamelCase_ ):
return array.size
else:
raise ValueError(F"""Type not supported for expand_dims: {type(UpperCamelCase_ )}.""" )
def A ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[str] ) -> Dict:
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(UpperCamelCase_ , (tuple, list) ):
lowerCAmelCase__ = [F"""{repo_id}--{v}""" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
lowerCAmelCase__ = F"""{repo_id}--{value}"""
return auto_map
def A ( UpperCamelCase_ : str ) -> Any:
'''simple docstring'''
for base_class in inspect.getmro(UpperCamelCase_ ):
lowerCAmelCase__ = base_class.__module__
lowerCAmelCase__ = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"""Could not infer framework from class {model_class}.""" )
| 48 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_snake_case ) == 1:
return True
_A = series[1] - series[0]
for index in range(len(_snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> float:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
_A = 0
for val in series:
answer += val
return answer / len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int = 100 ):
__UpperCAmelCase = n * (n + 1) * (2 * n + 1) / 6
__UpperCAmelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 49 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_snake_case ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
_A = QuantumRegister(_snake_case , '''qr''' )
_A = ClassicalRegister(_snake_case , '''cr''' )
_A = QuantumCircuit(_snake_case , _snake_case )
_A = number_of_qubits
for i in range(_snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _snake_case , _snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_snake_case , _snake_case )
# simulate with 10000 shots
_A = Aer.get_backend('''qasm_simulator''' )
_A = execute(_snake_case , _snake_case , shots=10_000 )
return job.result().get_counts(_snake_case )
if __name__ == "__main__":
print(
f'Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'
)
| 2 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : List[str] = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
'processing_x_clip': ['XCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'XCLIPModel',
'XCLIPPreTrainedModel',
'XCLIPTextModel',
'XCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
UpperCamelCase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_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_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :str , _snake_case :Any , _snake_case :int , _snake_case :List[Any] ) -> Optional[int]:
for attribute in key.split('''.''' ):
_A = getattr(_snake_case , _snake_case )
if weight_type is not None:
_A = getattr(_snake_case , _snake_case ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :Any , _snake_case :int ) -> Any:
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_snake_case )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "weight" in name:
_A = '''weight'''
elif "bias" in name:
_A = '''bias'''
else:
_A = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :List[str] , _snake_case :List[str] , _snake_case :Optional[int] , _snake_case :List[Any] ) -> Any:
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = 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.'''
)
_A = 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.'''
)
_A = 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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Tuple:
_A = SEWConfig()
if is_finetuned:
_A = model.wav_encoder.wav_model.cfg
else:
_A = model.cfg
_A = fs_config.conv_bias
_A = eval(fs_config.conv_feature_layers )
_A = [x[0] for x in conv_layers]
_A = [x[1] for x in conv_layers]
_A = [x[2] for x in conv_layers]
_A = '''gelu'''
_A = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_A = 0.0
_A = fs_config.activation_fn.name
_A = fs_config.encoder_embed_dim
_A = 0.02
_A = fs_config.encoder_ffn_embed_dim
_A = 1E-5
_A = fs_config.encoder_layerdrop
_A = fs_config.encoder_attention_heads
_A = fs_config.conv_pos_groups
_A = fs_config.conv_pos
_A = len(_snake_case )
_A = fs_config.encoder_layers
_A = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_A = model.cfg
_A = fs_config.final_dropout
_A = fs_config.layerdrop
_A = fs_config.activation_dropout
_A = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_A = fs_config.attention_dropout
_A = fs_config.dropout_input
_A = fs_config.dropout
_A = fs_config.mask_channel_length
_A = fs_config.mask_channel_prob
_A = fs_config.mask_length
_A = fs_config.mask_prob
_A = '''Wav2Vec2FeatureExtractor'''
_A = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] , _snake_case :Union[str, Any] , _snake_case :Optional[Any]=None , _snake_case :Optional[int]=None , _snake_case :Dict=True ) -> List[Any]:
if is_finetuned:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_A = SEWConfig.from_pretrained(_snake_case )
else:
_A = convert_config(model[0] , _snake_case )
_A = model[0].eval()
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
if is_finetuned:
if dict_path:
_A = Dictionary.load(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
_A = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_snake_case , )
_A = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
_A = SEWForCTC(_snake_case )
else:
_A = SEWModel(_snake_case )
feature_extractor.save_pretrained(_snake_case )
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_model.save_pretrained(_snake_case )
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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 2 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = (boundary[1] - boundary[0]) / steps
UpperCAmelCase = boundary[0]
UpperCAmelCase = boundary[1]
UpperCAmelCase = make_points(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCAmelCase = 0.0
y += (h / 2.0) * f(SCREAMING_SNAKE_CASE_ )
for i in x_i:
# print(i)
y += h * f(SCREAMING_SNAKE_CASE_ )
y += (h / 2.0) * f(SCREAMING_SNAKE_CASE_ )
return y
def __snake_case ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase = a + h
while x < (b - h):
yield x
UpperCAmelCase = x + h
def __snake_case ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Tuple: # enter your function here
"""simple docstring"""
UpperCAmelCase = (x - 0) * (x - 0)
return y
def __snake_case ( ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = 0.0 # Lower bound of integration
UpperCAmelCase = 1.0 # Upper bound of integration
UpperCAmelCase = 10.0 # define number of steps or resolution
UpperCAmelCase = [a, b] # define boundary of integration
UpperCAmelCase = method_a(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print(f"y = {y}" )
if __name__ == "__main__":
main()
| 51 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Any ) -> Any:
pass
@is_pipeline_test
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@require_torch
def snake_case_ ( self : Tuple ) -> Tuple:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCAmelCase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : int ) -> Optional[int]:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> int:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def snake_case_ ( self : Optional[int] ) -> Dict:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 2 | 0 |
"""simple docstring"""
from collections import defaultdict
def __A ( a_ :int) -> int:
__a : Dict = 1
__a : Any = True
for v in tree[start]:
if v not in visited:
ret += dfs(a_)
if ret % 2 == 0:
cuts.append(a_)
return ret
def __A ( ) -> List[Any]:
dfs(1)
if __name__ == "__main__":
A , A = 10, 9
A = defaultdict(list)
A = {}
A = []
A = 0
A = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1) | 52 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : Tuple ) -> Optional[int]:
_A = tempfile.mkdtemp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_A = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Dict , **__lowerCAmelCase : int ) -> Optional[int]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : str , **__lowerCAmelCase : Optional[Any] ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Tuple , **__lowerCAmelCase : str ) -> Union[str, Any]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def snake_case_ ( self : int ) -> Optional[Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ ( self : Dict ) -> List[str]:
_A = self.get_tokenizer()
_A = self.get_rust_tokenizer()
_A = self.get_image_processor()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase )
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __lowerCAmelCase )
def snake_case_ ( self : List[Any] ) -> List[str]:
_A = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
_A = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(__lowerCAmelCase , return_tensors='''np''' )
_A = processor(images=__lowerCAmelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = processor(text=__lowerCAmelCase )
_A = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ ( self : List[str] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def snake_case_ ( self : Optional[Any] ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__lowerCAmelCase )
_A = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 2 | 0 |
def a_ ( lowerCAmelCase_ : str ):
__lowerCAmelCase = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def a_ ( lowerCAmelCase_ : str ):
__lowerCAmelCase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowerCAmelCase = remove_duplicates(key.upper() )
__lowerCAmelCase = len(lowerCAmelCase_ )
# First fill cipher with key characters
__lowerCAmelCase = {alphabet[i]: char for i, char in enumerate(lowerCAmelCase_ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(lowerCAmelCase_ ), 26 ):
__lowerCAmelCase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowerCAmelCase = alphabet[i - offset]
__lowerCAmelCase = char
return cipher_alphabet
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : dict[str, str] ):
return "".join(cipher_map.get(lowerCAmelCase_, lowerCAmelCase_ ) for ch in message.upper() )
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : dict[str, str] ):
__lowerCAmelCase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(lowerCAmelCase_, lowerCAmelCase_ ) for ch in message.upper() )
def a_ ( ):
__lowerCAmelCase = input('Enter message to encode or decode: ' ).strip()
__lowerCAmelCase = input('Enter keyword: ' ).strip()
__lowerCAmelCase = input('Encipher or decipher? E/D:' ).strip()[0].lower()
try:
__lowerCAmelCase = {'e': encipher, 'd': decipher}[option]
except KeyError:
raise KeyError('invalid input option' )
__lowerCAmelCase = create_cipher_map(lowerCAmelCase_ )
print(func(lowerCAmelCase_, lowerCAmelCase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 53 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = "openai-gpt"
a__ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Union[str, Any] , __lowerCAmelCase : int=4_04_78 , __lowerCAmelCase : Tuple=5_12 , __lowerCAmelCase : str=7_68 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=1E-5 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Optional[Any]="cls_index" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=0.1 , **__lowerCAmelCase : Tuple , ) -> Optional[Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**__lowerCAmelCase )
| 2 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
UpperCAmelCase_ =precision
UpperCAmelCase_ =ceil(precision / 1_4 )
UpperCAmelCase_ =4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
UpperCAmelCase_ =1
UpperCAmelCase_ =1_3_5_9_1_4_0_9
UpperCAmelCase_ =Decimal(lowercase__ )
for k in range(1 , lowercase__ ):
UpperCAmelCase_ =factorial(6 * k ) // (factorial(3 * k ) * factorial(lowercase__ ) ** 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__":
__lowercase : List[str] =50
print(f"""The first {n} digits of pi is: {pi(n)}""")
| 54 |
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 DeformableDetrImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : int=30 , __lowerCAmelCase : Dict=4_00 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=1 / 2_55 , __lowerCAmelCase : int=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
_A = parent
_A = batch_size
_A = num_channels
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
_A = do_rescale
_A = rescale_factor
_A = do_pad
def snake_case_ ( self : Optional[int] ) -> Any:
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 snake_case_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False ) -> Dict:
if not batched:
_A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
_A , _A = image.size
else:
_A , _A = image.shape[1], image.shape[2]
if w < h:
_A = int(self.size['''shortest_edge'''] * h / w )
_A = self.size['''shortest_edge''']
elif w > h:
_A = self.size['''shortest_edge''']
_A = int(self.size['''shortest_edge'''] * w / h )
else:
_A = self.size['''shortest_edge''']
_A = self.size['''shortest_edge''']
else:
_A = []
for image in image_inputs:
_A , _A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase__ ( _A , unittest.TestCase):
"""simple docstring"""
a__ : Any = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case_ ( self : Optional[int] ) -> Any:
_A = DeformableDetrImageProcessingTester(self )
@property
def snake_case_ ( self : Union[str, Any] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = 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 , '''do_rescale''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def snake_case_ ( self : List[str] ) -> int:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
_A = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def snake_case_ ( self : Any ) -> Union[str, Any]:
pass
def snake_case_ ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
_A = image_processing(__lowerCAmelCase , 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 snake_case_ ( self : Tuple ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
# prepare image and target
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
_A = DeformableDetrImageProcessor()
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def snake_case_ ( self : List[str] ) -> List[str]:
# prepare image, target and masks_path
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
_A = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_A = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
_A = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 2 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = (CMStochasticIterativeScheduler,)
snake_case_ = 10
def UpperCamelCase_ ( self : str ,**A : List[str] ):
__A = {
"num_train_timesteps": 2_01,
"sigma_min": 0.0_02,
"sigma_max": 80.0,
}
config.update(**A )
return config
def UpperCamelCase_ ( self : int ):
__A = 10
__A = self.get_scheduler_config()
__A = self.scheduler_classes[0](**A )
scheduler.set_timesteps(A )
__A = scheduler.timesteps[0]
__A = scheduler.timesteps[1]
__A = self.dummy_sample
__A = 0.1 * sample
__A = scheduler.step(A ,A ,A ).prev_sample
__A = scheduler.step(A ,A ,A ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def UpperCamelCase_ ( self : Dict ):
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=A )
def UpperCamelCase_ ( self : Dict ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=A )
def UpperCamelCase_ ( self : str ):
__A = self.scheduler_classes[0]
__A = self.get_scheduler_config()
__A = scheduler_class(**A )
__A = 1
scheduler.set_timesteps(A )
__A = scheduler.timesteps
__A = torch.manual_seed(0 )
__A = self.dummy_model()
__A = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(A ):
# 1. scale model input
__A = scheduler.scale_model_input(A ,A )
# 2. predict noise residual
__A = model(A ,A )
# 3. predict previous sample x_t-1
__A = scheduler.step(A ,A ,A ,generator=A ).prev_sample
__A = pred_prev_sample
__A = torch.sum(torch.abs(A ) )
__A = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 1_92.76_14 ) < 1E-2
assert abs(result_mean.item() - 0.25_10 ) < 1E-3
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = self.scheduler_classes[0]
__A = self.get_scheduler_config()
__A = scheduler_class(**A )
__A = [1_06, 0]
scheduler.set_timesteps(timesteps=A )
__A = scheduler.timesteps
__A = torch.manual_seed(0 )
__A = self.dummy_model()
__A = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
__A = scheduler.scale_model_input(A ,A )
# 2. predict noise residual
__A = model(A ,A )
# 3. predict previous sample x_t-1
__A = scheduler.step(A ,A ,A ,generator=A ).prev_sample
__A = pred_prev_sample
__A = torch.sum(torch.abs(A ) )
__A = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 3_47.63_57 ) < 1E-2
assert abs(result_mean.item() - 0.45_27 ) < 1E-3
def UpperCamelCase_ ( self : Dict ):
__A = self.scheduler_classes[0]
__A = self.get_scheduler_config()
__A = scheduler_class(**A )
__A = [39, 30, 12, 15, 0]
with self.assertRaises(A ,msg="`timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=A )
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.scheduler_classes[0]
__A = self.get_scheduler_config()
__A = scheduler_class(**A )
__A = [39, 30, 12, 1, 0]
__A = len(A )
with self.assertRaises(A ,msg="Can only pass one of `num_inference_steps` or `timesteps`." ):
scheduler.set_timesteps(num_inference_steps=A ,timesteps=A )
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.scheduler_classes[0]
__A = self.get_scheduler_config()
__A = scheduler_class(**A )
__A = [scheduler.config.num_train_timesteps]
with self.assertRaises(
A ,msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,):
scheduler.set_timesteps(timesteps=A )
| 55 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 0 |
'''simple docstring'''
import math
import unittest
def _a (lowercase__ : int ) -> bool:
"""simple docstring"""
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowercase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class _lowercase ( unittest.TestCase ):
def a ( self : Optional[Any] ) -> Any:
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def a ( self : Tuple ) -> Dict:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1 ) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 56 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
_A = 2
while True:
if is_prime(_snake_case ):
yield num
num += 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 10_001 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , _snake_case ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 2 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def snake_case (UpperCAmelCase__ ) -> int:
if isinstance(UpperCAmelCase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class _lowerCAmelCase:
"""simple docstring"""
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
pass
def _a ( self ):
pass
def _a ( self ):
pass
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: str = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCamelCase , _lowerCamelCase , f'''Difference between torch and flax is {diff} (>= {tol}).''' )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
UpperCamelCase_: str = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: Any = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
UpperCamelCase_: Optional[int] = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], config.projection_dim) )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: List[str] = {'vision_model': vision_model, 'text_model': text_model}
UpperCamelCase_: List[Any] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: Optional[int] = {'vision_model': vision_model, 'text_model': text_model}
UpperCamelCase_: Any = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
UpperCamelCase_: Optional[Any] = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
UpperCamelCase_: Any = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )
UpperCamelCase_: Any = model(input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase )
UpperCamelCase_: int = after_output[0]
UpperCamelCase_: Dict = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1e-3 )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , **_lowerCamelCase ):
UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = self.get_vision_text_model(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: List[Any] = {'vision_model': vision_model, 'text_model': text_model}
UpperCamelCase_: Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )
UpperCamelCase_: List[str] = model(
input_ids=_lowerCamelCase , pixel_values=_lowerCamelCase , attention_mask=_lowerCamelCase , output_attentions=_lowerCamelCase )
UpperCamelCase_: List[str] = output.vision_model_output.attentions
self.assertEqual(len(_lowerCamelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCamelCase_: Any = to_atuple(vision_model.config.image_size )
UpperCamelCase_: Union[str, Any] = to_atuple(vision_model.config.patch_size )
UpperCamelCase_: int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
UpperCamelCase_: Optional[int] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
UpperCamelCase_: str = output.text_model_output.attentions
self.assertEqual(len(_lowerCamelCase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
pt_model.to(_lowerCamelCase )
pt_model.eval()
# prepare inputs
UpperCamelCase_: Optional[Any] = inputs_dict
UpperCamelCase_: Optional[int] = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
UpperCamelCase_: Union[str, Any] = pt_model(**_lowerCamelCase ).to_tuple()
UpperCamelCase_: List[Any] = fx_model(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output.numpy() , 4e-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowerCamelCase )
UpperCamelCase_: int = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
UpperCamelCase_: List[Any] = fx_model_loaded(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output.numpy() , 4e-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowerCamelCase )
UpperCamelCase_: Any = VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase , from_flax=_lowerCamelCase )
pt_model_loaded.to(_lowerCamelCase )
pt_model_loaded.eval()
with torch.no_grad():
UpperCamelCase_: int = pt_model_loaded(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(_lowerCamelCase , pt_output_loaded.numpy() , 4e-2 )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: int = VisionTextDualEncoderModel(_lowerCamelCase )
UpperCamelCase_: str = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
UpperCamelCase_: List[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _lowerCamelCase )
UpperCamelCase_: List[str] = fx_state
self.check_pt_flax_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: str = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase_: Tuple = VisionTextDualEncoderModel(_lowerCamelCase )
UpperCamelCase_: str = FlaxVisionTextDualEncoderModel(_lowerCamelCase )
UpperCamelCase_: List[Any] = load_flax_weights_in_pytorch_model(_lowerCamelCase , fx_model.params )
self.check_pt_flax_equivalence(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def _a ( self ):
UpperCamelCase_: str = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: int = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: Optional[Any] = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: int = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCamelCase )
@is_pt_flax_cross_test
def _a ( self ):
UpperCamelCase_: Dict = self.prepare_config_and_inputs()
UpperCamelCase_: str = config_inputs_dict.pop('vision_config' )
UpperCamelCase_: Dict = config_inputs_dict.pop('text_config' )
UpperCamelCase_: int = config_inputs_dict
self.check_equivalence_pt_to_flax(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.check_equivalence_flax_to_pt(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@slow
def _a ( self ):
UpperCamelCase_ ,UpperCamelCase_: int = self.get_pretrained_model_and_inputs()
UpperCamelCase_: Optional[Any] = model_a(**_lowerCamelCase )
UpperCamelCase_: Any = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCamelCase )
UpperCamelCase_: str = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )
UpperCamelCase_: str = model_a(**_lowerCamelCase )
UpperCamelCase_: str = after_outputs[0]
UpperCamelCase_: Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCamelCase , 1e-5 )
@require_flax
class _lowerCAmelCase( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: Optional[int] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-vit' , 'hf-internal-testing/tiny-bert' , vision_from_pt=_lowerCamelCase , text_from_pt=_lowerCamelCase , )
UpperCamelCase_: List[Any] = 1_3
UpperCamelCase_: Tuple = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
UpperCamelCase_: Union[str, Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
UpperCamelCase_: str = random_attention_mask([batch_size, 4] )
UpperCamelCase_: Optional[int] = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Any = FlaxViTModel(_lowerCamelCase )
UpperCamelCase_: Dict = FlaxBertModel(_lowerCamelCase )
return vision_model, text_model
def _a ( self ):
UpperCamelCase_: Union[str, Any] = FlaxViTModelTester(self )
UpperCamelCase_: Dict = FlaxBertModelTester(self )
UpperCamelCase_: Tuple = vit_model_tester.prepare_config_and_inputs()
UpperCamelCase_: Optional[int] = bert_model_tester.prepare_config_and_inputs()
UpperCamelCase_ ,UpperCamelCase_: Tuple = vision_config_and_inputs
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_: Optional[int] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class _lowerCAmelCase( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-clip' , 'hf-internal-testing/tiny-bert' , vision_from_pt=_lowerCamelCase , text_from_pt=_lowerCamelCase , )
UpperCamelCase_: Union[str, Any] = 1_3
UpperCamelCase_: Optional[int] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
UpperCamelCase_: Optional[Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
UpperCamelCase_: Any = random_attention_mask([batch_size, 4] )
UpperCamelCase_: List[str] = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: str = FlaxCLIPVisionModel(_lowerCamelCase )
UpperCamelCase_: int = FlaxBertModel(_lowerCamelCase )
return vision_model, text_model
def _a ( self ):
UpperCamelCase_: int = FlaxCLIPVisionModelTester(self )
UpperCamelCase_: Tuple = FlaxBertModelTester(self )
UpperCamelCase_: List[str] = clip_model_tester.prepare_config_and_inputs()
UpperCamelCase_: int = bert_model_tester.prepare_config_and_inputs()
UpperCamelCase_ ,UpperCamelCase_: Optional[int] = vision_config_and_inputs
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_: Union[str, Any] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
@slow
def _a ( self ):
UpperCamelCase_: Optional[int] = FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian' , logit_scale_init_value=1.0 )
UpperCamelCase_: Optional[Any] = VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )
UpperCamelCase_: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
UpperCamelCase_: Any = processor(
text=['una foto di un gatto', 'una foto di un cane'] , images=_lowerCamelCase , padding=_lowerCamelCase , return_tensors='np' )
UpperCamelCase_: Union[str, Any] = model(**_lowerCamelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
UpperCamelCase_: str = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , _lowerCamelCase , atol=1e-3 ) ) | 57 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase_ = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase_ = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase_ = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase_ = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase_ = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase_ = re.compile(r"""^\s*else:""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] ) -> Any:
if _re_test_backend.search(_snake_case ) is None:
return None
_A = [b[0] for b in _re_backend.findall(_snake_case )]
backends.sort()
return "_and_".join(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any ) -> Any:
with open(_snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.readlines()
_A = 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
_A = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
_A = 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 ):
_A = _re_one_line_import_struct.search(_snake_case ).groups()[0]
_A = re.findall(r'''\[([^\]]+)\]''' , _snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
_A = _re_import_struct_key_value.search(_snake_case )
if single_line_import_search is not None:
_A = [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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
_A = 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:
_A = _re_import_struct_add_many.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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:
_A = _re_between_brackets.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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
_A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_A = []
while (
line_index < len(_snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
_A = lines[line_index]
_A = _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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
_A = lines[line_index]
_A = _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
_A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] , _snake_case :Dict ) -> Any:
def find_duplicates(_snake_case :Any ):
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!"]
_A = []
for key in import_dict_objects.keys():
_A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_A = 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] ) ):
_A = '''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 SCREAMING_SNAKE_CASE_ ( ) -> int:
_A = []
for root, _, files in os.walk(_snake_case ):
if "__init__.py" in files:
_A = os.path.join(_snake_case , '''__init__.py''' )
_A = parse_init(_snake_case )
if objects is not None:
_A = analyze_results(*_snake_case )
if len(_snake_case ) > 0:
_A = 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 SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_A = []
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
_A = str((Path(_snake_case ) / folder).relative_to(_snake_case ) )
_A = short_path.replace(os.path.sep , '''.''' )
submodules.append(_snake_case )
for fname in files:
if fname == "__init__.py":
continue
_A = str((Path(_snake_case ) / fname).relative_to(_snake_case ) )
_A = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_snake_case )
return submodules
UpperCAmelCase_ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_A = direct_transformers_import(_snake_case )
_A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_snake_case , '''__init__.py''' ) , '''r''' ) as f:
_A = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , _snake_case ) ) )
_A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_snake_case ) > 0:
_A = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 2 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : List[str] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : str = ['''MobileViTFeatureExtractor''']
__lowerCAmelCase : Optional[int] = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[int] = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Union[str, Any] = [
'''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
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(_A)
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Optional[int] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : List[str] ) -> List[str]:
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def snake_case_ ( self : Any , __lowerCAmelCase : Dict=None , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=None ) -> int:
_A = {}
_A = {}
if prompt is not None:
_A = prompt
if generate_kwargs is not None:
_A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
_A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[str] , __lowerCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
return super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any]=None ) -> int:
_A = load_image(__lowerCAmelCase )
if prompt is not None:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(__lowerCAmelCase )} - but expected a single string. '''
'''Note also that one single text can be provided for conditional image to text generation.''' )
_A = self.model.config.model_type
if model_type == "git":
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids
_A = [self.tokenizer.cls_token_id] + input_ids
_A = torch.tensor(__lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
_A = self.image_processor(images=__lowerCAmelCase , header_text=__lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(__lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(__lowerCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A = None
return model_inputs
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict=None ) -> str:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __lowerCAmelCase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
_A = None
if generate_kwargs is None:
_A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A = model_inputs.pop(self.model.main_input_name )
_A = self.model.generate(__lowerCAmelCase , **__lowerCAmelCase , **__lowerCAmelCase )
return model_outputs
def snake_case_ ( self : Dict , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = []
for output_ids in model_outputs:
_A = {
'''generated_text''': self.tokenizer.decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , )
}
records.append(__lowerCAmelCase )
return records
| 2 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
__A = logging.get_logger(__name__) # pylint: disable=invalid-name
__A = 256
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["melgan"]
def __init__(self : Tuple , UpperCAmelCase_ : SpectrogramNotesEncoder , UpperCAmelCase_ : SpectrogramContEncoder , UpperCAmelCase_ : TaFilmDecoder , UpperCAmelCase_ : DDPMScheduler , UpperCAmelCase_ : OnnxRuntimeModel if is_onnx_available() else Any , ) ->None:
'''simple docstring'''
super().__init__()
# From MELGAN
lowerCamelCase__: List[Any] =math.log(1E-5) # Matches MelGAN training.
lowerCamelCase__: str =4.0 # Largest value for most examples
lowerCamelCase__: Dict =128
self.register_modules(
notes_encoder=UpperCAmelCase_ , continuous_encoder=UpperCAmelCase_ , decoder=UpperCAmelCase_ , scheduler=UpperCAmelCase_ , melgan=UpperCAmelCase_ , )
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=(-1.0, 1.0) , UpperCAmelCase_ : Dict=False) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: str =output_range
if clip:
lowerCamelCase__: Any =torch.clip(UpperCAmelCase_ , self.min_value , self.max_value)
# Scale to [0, 1].
lowerCamelCase__: Tuple =(features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any]=(-1.0, 1.0) , UpperCAmelCase_ : List[str]=False) ->Dict:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: List[Any] =input_range
lowerCamelCase__: List[str] =torch.clip(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_) if clip else outputs
# Scale to [0, 1].
lowerCamelCase__: List[str] =(outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[Any]) ->int:
'''simple docstring'''
lowerCamelCase__: Dict =input_tokens > 0
lowerCamelCase__ , lowerCamelCase__: int =self.notes_encoder(
encoder_input_tokens=UpperCAmelCase_ , encoder_inputs_mask=UpperCAmelCase_)
lowerCamelCase__ , lowerCamelCase__: Dict =self.continuous_encoder(
encoder_inputs=UpperCAmelCase_ , encoder_inputs_mask=UpperCAmelCase_)
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : Tuple) ->int:
'''simple docstring'''
lowerCamelCase__: Tuple =noise_time
if not torch.is_tensor(UpperCAmelCase_):
lowerCamelCase__: Tuple =torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
elif torch.is_tensor(UpperCAmelCase_) and len(timesteps.shape) == 0:
lowerCamelCase__: Union[str, Any] =timesteps[None].to(input_tokens.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowerCamelCase__: List[Any] =timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)
lowerCamelCase__: Tuple =self.decoder(
encodings_and_masks=UpperCAmelCase_ , decoder_input_tokens=UpperCAmelCase_ , decoder_noise_time=UpperCAmelCase_)
return logits
@torch.no_grad()
def __call__(self : Union[str, Any] , UpperCAmelCase_ : List[List[int]] , UpperCAmelCase_ : Optional[torch.Generator] = None , UpperCAmelCase_ : int = 100 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : str = "numpy" , UpperCAmelCase_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase_ : int = 1 , ) ->Union[AudioPipelineOutput, Tuple]:
'''simple docstring'''
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCAmelCase_ , UpperCAmelCase_) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(UpperCAmelCase_)}.""")
lowerCamelCase__: List[str] =np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
lowerCamelCase__: Union[str, Any] =np.zeros([1, 0, self.n_dims] , np.floataa)
lowerCamelCase__: Optional[int] =torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=UpperCAmelCase_ , device=self.device)
for i, encoder_input_tokens in enumerate(UpperCAmelCase_):
if i == 0:
lowerCamelCase__: Optional[Any] =torch.from_numpy(pred_mel[:1].copy()).to(
device=self.device , dtype=self.decoder.dtype)
# The first chunk has no previous context.
lowerCamelCase__: int =torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=UpperCAmelCase_ , device=self.device)
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
lowerCamelCase__: Tuple =ones
lowerCamelCase__: Optional[Any] =self.scale_features(
UpperCAmelCase_ , output_range=[-1.0, 1.0] , clip=UpperCAmelCase_)
lowerCamelCase__: Tuple =self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=UpperCAmelCase_ , continuous_mask=UpperCAmelCase_ , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
lowerCamelCase__: List[Any] =randn_tensor(
shape=encoder_continuous_inputs.shape , generator=UpperCAmelCase_ , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(UpperCAmelCase_)
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
lowerCamelCase__: Optional[int] =self.decode(
encodings_and_masks=UpperCAmelCase_ , input_tokens=UpperCAmelCase_ , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
lowerCamelCase__: str =self.scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample
lowerCamelCase__: Optional[Any] =self.scale_to_features(UpperCAmelCase_ , input_range=[-1.0, 1.0])
lowerCamelCase__: List[Any] =mel[:1]
lowerCamelCase__: Optional[int] =mel.cpu().float().numpy()
lowerCamelCase__: int =np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1)
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCAmelCase_ , UpperCAmelCase_)
logger.info("Generated segment" , UpperCAmelCase_)
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.")
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.")
if output_type == "numpy":
lowerCamelCase__: Any =self.melgan(input_features=full_pred_mel.astype(np.floataa))
else:
lowerCamelCase__: Union[str, Any] =full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=UpperCAmelCase_)
| 59 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_A = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 2 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def lowerCamelCase_ ( _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : List[str] = torch.exp(_UpperCamelCase )
snake_case_ : List[str] = torch.sum(_UpperCamelCase , dim=1 ) # sum of exp(x_i)
snake_case_ : Union[str, Any] = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(_UpperCamelCase ) - B / A
class __lowerCAmelCase ( nn.Module ):
def __init__(self , __magic_name__ ) -> Tuple:
'''simple docstring'''
super().__init__()
snake_case_ : Any = config.output_attentions
snake_case_ : Any = config.output_hidden_states
snake_case_ : Union[str, Any] = nn.ModuleList([BertLayer(__magic_name__ ) for _ in range(config.num_hidden_layers )] )
snake_case_ : Tuple = nn.ModuleList([BertHighway(__magic_name__ ) for _ in range(config.num_hidden_layers )] )
snake_case_ : Any = [-1 for _ in range(config.num_hidden_layers )]
def lowerCamelCase (self , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
if (type(__magic_name__ ) is float) or (type(__magic_name__ ) is int):
for i in range(len(self.early_exit_entropy ) ):
snake_case_ : Optional[Any] = x
else:
snake_case_ : str = x
def lowerCamelCase (self , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def lowerCamelCase (self , __magic_name__ , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = ()
snake_case_ : str = ()
snake_case_ : Any = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
snake_case_ : Optional[Any] = all_hidden_states + (hidden_states,)
snake_case_ : Union[str, Any] = layer_module(
__magic_name__ , __magic_name__ , head_mask[i] , __magic_name__ , __magic_name__ )
snake_case_ : List[str] = layer_outputs[0]
if self.output_attentions:
snake_case_ : List[str] = all_attentions + (layer_outputs[1],)
snake_case_ : int = (hidden_states,)
if self.output_hidden_states:
snake_case_ : str = current_outputs + (all_hidden_states,)
if self.output_attentions:
snake_case_ : Optional[int] = current_outputs + (all_attentions,)
snake_case_ : Dict = self.highway[i](__magic_name__ )
# logits, pooled_output
if not self.training:
snake_case_ : List[str] = highway_exit[0]
snake_case_ : str = entropy(__magic_name__ )
snake_case_ : Any = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
snake_case_ : Optional[Any] = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
snake_case_ : Optional[Any] = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(__magic_name__ , i + 1 )
else:
snake_case_ : List[str] = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
snake_case_ : Dict = all_hidden_states + (hidden_states,)
snake_case_ : Union[str, Any] = (hidden_states,)
if self.output_hidden_states:
snake_case_ : int = outputs + (all_hidden_states,)
if self.output_attentions:
snake_case_ : Tuple = outputs + (all_attentions,)
snake_case_ : Dict = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'''The Bert Model transformer with early exiting (DeeBERT). ''', _a, )
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ ) -> int:
'''simple docstring'''
super().__init__(__magic_name__ )
snake_case_ : Any = config
snake_case_ : int = BertEmbeddings(__magic_name__ )
snake_case_ : str = DeeBertEncoder(__magic_name__ )
snake_case_ : Any = BertPooler(__magic_name__ )
self.init_weights()
def lowerCamelCase (self ) -> str:
'''simple docstring'''
self.encoder.init_highway_pooler(self.pooler )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return self.embeddings.word_embeddings
def lowerCamelCase (self , __magic_name__ ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = value
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(__magic_name__ )
@add_start_docstrings_to_model_forward(__magic_name__ )
def lowerCamelCase (self , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
snake_case_ : Union[str, Any] = input_ids.size()
elif inputs_embeds is not None:
snake_case_ : int = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
snake_case_ : Union[str, Any] = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
snake_case_ : Optional[Any] = torch.ones(__magic_name__ , device=__magic_name__ )
if encoder_attention_mask is None:
snake_case_ : Optional[int] = torch.ones(__magic_name__ , device=__magic_name__ )
if token_type_ids is None:
snake_case_ : int = torch.zeros(__magic_name__ , dtype=torch.long , device=__magic_name__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
snake_case_ : torch.Tensor = self.get_extended_attention_mask(__magic_name__ , __magic_name__ , __magic_name__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
snake_case_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
snake_case_ : List[str] = encoder_attention_mask[:, None, None, :]
snake_case_ : Optional[Any] = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
snake_case_ : Any = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
snake_case_ : Any = self.get_head_mask(__magic_name__ , self.config.num_hidden_layers )
snake_case_ : Tuple = self.embeddings(
input_ids=__magic_name__ , position_ids=__magic_name__ , token_type_ids=__magic_name__ , inputs_embeds=__magic_name__ )
snake_case_ : List[Any] = self.encoder(
__magic_name__ , attention_mask=__magic_name__ , head_mask=__magic_name__ , encoder_hidden_states=__magic_name__ , encoder_attention_mask=__magic_name__ , )
snake_case_ : List[str] = encoder_outputs[0]
snake_case_ : List[Any] = self.pooler(__magic_name__ )
snake_case_ : Any = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ , __magic_name__ ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = message
snake_case_ : Dict = exit_layer # start from 1!
class __lowerCAmelCase ( nn.Module ):
def __init__(self , __magic_name__ ) -> List[str]:
'''simple docstring'''
super().__init__()
snake_case_ : Optional[int] = BertPooler(__magic_name__ )
snake_case_ : Optional[int] = nn.Dropout(config.hidden_dropout_prob )
snake_case_ : Union[str, Any] = nn.Linear(config.hidden_size , config.num_labels )
def lowerCamelCase (self , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = encoder_outputs[0]
snake_case_ : Dict = self.pooler(__magic_name__ )
# "return" pooler_output
# BertModel
snake_case_ : Any = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
snake_case_ : Dict = bmodel_output[1]
snake_case_ : List[Any] = self.dropout(__magic_name__ )
snake_case_ : Any = self.classifier(__magic_name__ )
return logits, pooled_output
@add_start_docstrings(
'''Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. ''', _a, )
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ ) -> int:
'''simple docstring'''
super().__init__(__magic_name__ )
snake_case_ : Dict = config.num_labels
snake_case_ : Union[str, Any] = config.num_hidden_layers
snake_case_ : str = DeeBertModel(__magic_name__ )
snake_case_ : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob )
snake_case_ : Dict = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(__magic_name__ )
def lowerCamelCase (self , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=None , __magic_name__=-1 , __magic_name__=False , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.num_layers
try:
snake_case_ : int = self.bert(
__magic_name__ , attention_mask=__magic_name__ , token_type_ids=__magic_name__ , position_ids=__magic_name__ , head_mask=__magic_name__ , inputs_embeds=__magic_name__ , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
snake_case_ : List[str] = outputs[1]
snake_case_ : List[str] = self.dropout(__magic_name__ )
snake_case_ : Tuple = self.classifier(__magic_name__ )
snake_case_ : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
snake_case_ : Optional[int] = e.message
snake_case_ : Dict = e.exit_layer
snake_case_ : str = outputs[0]
if not self.training:
snake_case_ : Optional[int] = entropy(__magic_name__ )
snake_case_ : Optional[int] = []
snake_case_ : List[str] = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
snake_case_ : str = MSELoss()
snake_case_ : Optional[int] = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case_ : List[Any] = CrossEntropyLoss()
snake_case_ : str = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
snake_case_ : str = []
for highway_exit in outputs[-1]:
snake_case_ : List[Any] = highway_exit[0]
if not self.training:
highway_logits_all.append(__magic_name__ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
snake_case_ : Optional[Any] = MSELoss()
snake_case_ : Union[str, Any] = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case_ : Tuple = CrossEntropyLoss()
snake_case_ : Optional[int] = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__magic_name__ )
if train_highway:
snake_case_ : Union[str, Any] = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
snake_case_ : int = (loss,) + outputs
if not self.training:
snake_case_ : Dict = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
snake_case_ : List[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 60 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_A = kruskal(_snake_case , _snake_case )
_A = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_snake_case ) == sorted(_snake_case )
| 2 | 0 |
def _A ( lowerCAmelCase_ : Optional[Any] ):
"""simple docstring"""
lowerCAmelCase__ = [0] * len(lowerCAmelCase_ )
lowerCAmelCase__ = []
lowerCAmelCase__ = [1] * len(lowerCAmelCase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCAmelCase_ ) ):
if indegree[i] == 0:
queue.append(lowerCAmelCase_ )
while queue:
lowerCAmelCase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowerCAmelCase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(lowerCAmelCase_ )
print(max(lowerCAmelCase_ ) )
# Adjacency list of Graph
UpperCamelCase = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 61 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = len(lowercase )
print("The following activities are selected:" )
# The first activity is always selected
SCREAMING_SNAKE_CASE : Optional[int] = 0
print(lowercase , end="," )
# Consider rest of the activities
for j in range(lowercase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(lowercase , end="," )
SCREAMING_SNAKE_CASE : List[str] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
snake_case = [1, 3, 0, 5, 8, 5]
snake_case = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 62 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return False
_A = 0
_A = 0
_A = 1
# Calculating the hash of pattern and substring of text
for i in range(_snake_case ):
_A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def SCREAMING_SNAKE_CASE_ ( ) -> None:
_A = '''abc1abc12'''
_A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_A = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(_snake_case , _snake_case ) and not rabin_karp(_snake_case , _snake_case )
# Test 2)
_A = '''ABABX'''
_A = '''ABABZABABYABABX'''
assert rabin_karp(_snake_case , _snake_case )
# Test 3)
_A = '''AAAB'''
_A = '''ABAAAAAB'''
assert rabin_karp(_snake_case , _snake_case )
# Test 4)
_A = '''abcdabcy'''
_A = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(_snake_case , _snake_case )
# Test 5)
_A = '''Lü'''
_A = '''Lüsai'''
assert rabin_karp(_snake_case , _snake_case )
_A = '''Lue'''
assert not rabin_karp(_snake_case , _snake_case )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 2 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) <= 1:
return lst
__UpperCAmelCase : Optional[Any] = 1
while i < len(__lowerCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = lst[i], lst[i - 1]
i -= 1
if i == 0:
__UpperCAmelCase : Optional[int] = 1
return lst
if __name__ == "__main__":
a : Tuple = input("Enter numbers separated by a comma:\n").strip()
a : Union[str, Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 63 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
UpperCAmelCase_ = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
UpperCAmelCase_ = """</w>"""
UpperCAmelCase_ = """@@ """
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[Any] ) -> List[str]:
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
# Speech2Text2 has no max input length
UpperCAmelCase_ = {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Dict = VOCAB_FILES_NAMES
a__ : str = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : List[Any] = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str]="<s>" , __lowerCAmelCase : Tuple="<pad>" , __lowerCAmelCase : Optional[Any]="</s>" , __lowerCAmelCase : Dict="<unk>" , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=None , **__lowerCAmelCase : str , ) -> Dict:
super().__init__(
unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , do_lower_case=__lowerCAmelCase , **__lowerCAmelCase , )
_A = do_lower_case
with open(__lowerCAmelCase , encoding='''utf-8''' ) as vocab_handle:
_A = json.load(__lowerCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
_A = None
_A = None
else:
with open(__lowerCAmelCase , encoding='''utf-8''' ) as merges_handle:
_A = merges_handle.read().split('''\n''' )[:-1]
_A = [tuple(merge.split()[:2] ) for merge in merges]
_A = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_A = {}
@property
def snake_case_ ( self : List[str] ) -> int:
return len(self.decoder )
def snake_case_ ( self : Dict ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
_A = get_pairs(__lowerCAmelCase )
if not pairs:
return token
while True:
_A = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(__lowerCAmelCase ):
try:
_A = word.index(__lowerCAmelCase , __lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__lowerCAmelCase )
_A = new_word
if len(__lowerCAmelCase ) == 1:
break
else:
_A = get_pairs(__lowerCAmelCase )
_A = ''' '''.join(__lowerCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
_A = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(__lowerCAmelCase ):
_A = word.replace(__lowerCAmelCase , '''''' )
_A = word.replace(''' ''' , __lowerCAmelCase )
_A = word
return word
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Tuple ) -> Optional[int]:
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
_A = text.lower()
_A = text.split()
_A = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str ) -> int:
return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) )
def snake_case_ ( self : str , __lowerCAmelCase : int ) -> str:
_A = self.decoder.get(__lowerCAmelCase , self.unk_token )
return result
def snake_case_ ( self : List[str] , __lowerCAmelCase : List[str] ) -> str:
_A = ''' '''.join(__lowerCAmelCase )
# make sure @@ tokens are concatenated
_A = ''''''.join(string.split(__lowerCAmelCase ) )
return string
def snake_case_ ( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCAmelCase , ensure_ascii=__lowerCAmelCase ) + '''\n''' )
_A = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
_A = token_index
writer.write(''' '''.join(__lowerCAmelCase ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 2 | 0 |
from __future__ import annotations
class _lowerCamelCase :
def __init__( self , lowerCAmelCase = 0 ) -> List[str]:
SCREAMING_SNAKE_CASE__: Tuple= key
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> list[str]:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(lowerCAmelCase ) ^ key ) for ch in content]
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> list[str]:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Dict= key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(lowerCAmelCase ) ^ key ) for ch in content]
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase = 0 ) -> str:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Any= key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE__: Union[str, Any]= ''''''
for ch in content:
ans += chr(ord(lowerCAmelCase ) ^ key )
return ans
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase = 0 ) -> str:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
SCREAMING_SNAKE_CASE__: Union[str, Any]= key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE__: Optional[Any]= ''''''
for ch in content:
ans += chr(ord(lowerCAmelCase ) ^ key )
return ans
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase = 0 ) -> bool:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
try:
with open(lowerCAmelCase ) as fin, open('''encrypt.out''' , '''w+''' ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(lowerCAmelCase , lowerCAmelCase ) )
except OSError:
return False
return True
def UpperCamelCase_ ( self , lowerCAmelCase , lowerCAmelCase ) -> bool:
assert isinstance(lowerCAmelCase , lowerCAmelCase ) and isinstance(lowerCAmelCase , lowerCAmelCase )
try:
with open(lowerCAmelCase ) as fin, open('''decrypt.out''' , '''w+''' ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(lowerCAmelCase , lowerCAmelCase ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 64 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 2
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Optional[int] ) -> None:
_A = []
_A = {}
_A = 0
def __len__( self : str ) -> int:
return self.elements
def __repr__( self : Optional[int] ) -> str:
return str(self.heap )
def snake_case_ ( self : str ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
_A = self.elements
self.elements += 1
self._bubble_up(__lowerCAmelCase )
def snake_case_ ( self : Tuple ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
_A , _A = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
_A , _A = self.heap[0]
self._bubble_down(__lowerCAmelCase )
return elem
def snake_case_ ( self : int , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Update the weight of the given key
_A = self.position_map[elem]
_A = (elem, weight)
if position > 0:
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
_A = self.position_map[elem]
if curr_pos == 0:
return None
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[curr_pos]
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_up(__lowerCAmelCase )
return None
def snake_case_ ( self : Dict , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
_A = self.position_map[elem]
_A , _A = self.heap[curr_pos]
_A = get_child_left_position(__lowerCAmelCase )
_A = get_child_right_position(__lowerCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
_A , _A = self.heap[child_left_position]
_A , _A = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
if child_left_position < self.elements:
_A , _A = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
else:
return None
if child_right_position < self.elements:
_A , _A = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
return None
def snake_case_ ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
# Swap the nodes at the given positions
_A = self.heap[nodea_pos][0]
_A = self.heap[nodea_pos][0]
_A , _A = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
_A = nodea_pos
_A = nodea_pos
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : str ) -> None:
_A = {}
_A = 0
def __repr__( self : str ) -> str:
return str(self.connections )
def __len__( self : Dict ) -> int:
return self.nodes
def snake_case_ ( self : Any , __lowerCAmelCase : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
_A = {}
self.nodes += 1
def snake_case_ ( self : str , __lowerCAmelCase : T , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__lowerCAmelCase )
self.add_node(__lowerCAmelCase )
_A = weight
_A = weight
def SCREAMING_SNAKE_CASE_ ( _snake_case :GraphUndirectedWeighted[T] , ) -> tuple[dict[T, int], dict[T, T | None]]:
_A = {node: maxsize for node in graph.connections}
_A = {node: None for node in graph.connections}
_A = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_snake_case , _snake_case )
if priority_queue.is_empty():
return dist, parent
# initialization
_A = priority_queue.extract_min()
_A = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
# running prim's algorithm
while not priority_queue.is_empty():
_A = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
return dist, parent
| 2 | 0 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 65 |
import os
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 logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [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]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCamelCase = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n"
UpperCamelCase = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"
UpperCamelCase = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"pearson\": Pearson Correlation\n \"spearmanr\": Spearman Correlation\n \"matthews_correlation\": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'stsb')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n {'pearson': 1.0, 'spearmanr': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'cola')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
return float((preds == labels).mean() )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
_lowercase : Optional[int] = simple_accuracy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_lowercase : int = float(fa_score(y_true=SCREAMING_SNAKE_CASE , y_pred=SCREAMING_SNAKE_CASE ) )
return {
"accuracy": acc,
"f1": fa,
}
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
_lowercase : Optional[Any] = float(pearsonr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )[0] )
_lowercase : int = float(spearmanr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
def __a ( self ):
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32' ),
} ) , codebase_urls=[] , reference_urls=[] , format='numpy' , )
def __a ( self , _lowerCAmelCase , _lowerCAmelCase ):
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase )}
elif self.config_name == "stsb":
return pearson_and_spearman(_lowerCAmelCase , _lowerCAmelCase )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(_lowerCAmelCase , _lowerCAmelCase )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(_lowerCAmelCase , _lowerCAmelCase )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' )
| 66 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_snake_case )
return explored
UpperCAmelCase_ = {
"""A""": ["""B""", """C""", """D"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F"""],
"""D""": ["""B""", """D"""],
"""E""": ["""B""", """F"""],
"""F""": ["""C""", """E""", """G"""],
"""G""": ["""F"""],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, """A"""))
| 2 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
snake_case = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Union[str, Any] , snake_case__ :Optional[int] , snake_case__ :Any , snake_case__ :Union[str, Any]=None , snake_case__ :str=None ) -> Optional[Any]:
# Recurse if needed
if "." in tensor_name:
_lowercase = tensor_name.split('.' )
for split in splits[:-1]:
_lowercase = getattr(snake_case__ , snake_case__ )
if new_module is None:
raise ValueError(F"""{module} has no attribute {split}.""" )
_lowercase = new_module
_lowercase = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F"""{module} does not have a parameter or a buffer named {tensor_name}.""" )
_lowercase = tensor_name in module._buffers
_lowercase = getattr(snake_case__ , snake_case__ )
if old_value.device == torch.device('meta' ) and device not in ["meta", torch.device('meta' )] and value is None:
raise ValueError(F"""{tensor_name} is on the meta device, we need a `value` to put in on {device}.""" )
_lowercase = False
_lowercase = False
if is_buffer or not is_bitsandbytes_available():
_lowercase = False
_lowercase = False
else:
_lowercase = hasattr(bnb.nn , 'Params4bit' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
_lowercase = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
_lowercase = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
_lowercase = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
_lowercase = value.to('cpu' )
if value.dtype == torch.inta:
_lowercase = version.parse(importlib.metadata.version('bitsandbytes' ) ) > version.parse(
'0.37.2' )
if not is_abit_serializable:
raise ValueError(
'Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '
'Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.' )
else:
_lowercase = torch.tensor(snake_case__ , device='cpu' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , snake_case__ ) and fpaa_statistics is None:
_lowercase = new_value.T
_lowercase = old_value.__dict__
if is_abit:
_lowercase = bnb.nn.IntaParams(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
elif is_abit:
_lowercase = bnb.nn.Paramsabit(snake_case__ , requires_grad=snake_case__ , **snake_case__ ).to(snake_case__ )
_lowercase = new_value
if fpaa_statistics is not None:
setattr(module.weight , 'SCB' , fpaa_statistics.to(snake_case__ ) )
else:
if value is None:
_lowercase = old_value.to(snake_case__ )
elif isinstance(snake_case__ , torch.Tensor ):
_lowercase = value.to(snake_case__ )
else:
_lowercase = torch.tensor(snake_case__ , device=snake_case__ )
if is_buffer:
_lowercase = new_value
else:
_lowercase = nn.Parameter(snake_case__ , requires_grad=old_value.requires_grad )
_lowercase = new_value
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Union[str, Any] , snake_case__ :Any=None , snake_case__ :Union[str, Any]=None , snake_case__ :Optional[Any]=None , snake_case__ :str=False ) -> Optional[int]:
for name, module in model.named_children():
if current_key_name is None:
_lowercase = []
current_key_name.append(snake_case__ )
if (isinstance(snake_case__ , nn.Linear ) or isinstance(snake_case__ , snake_case__ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '.'.join(snake_case__ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(snake_case__ , snake_case__ ):
_lowercase , _lowercase = module.weight.shape
else:
_lowercase = module.in_features
_lowercase = module.out_features
if quantization_config.quantization_method() == "llm_int8":
_lowercase = bnb.nn.LinearabitLt(
snake_case__ , snake_case__ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
_lowercase = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
_lowercase = bnb.nn.Linearabit(
snake_case__ , snake_case__ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
_lowercase = True
# Store the module class in case we need to transpose the weight later
_lowercase = type(snake_case__ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(snake_case__ )
if len(list(module.children() ) ) > 0:
_lowercase , _lowercase = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , has_been_replaced=snake_case__ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int , snake_case__ :Union[str, Any]=None , snake_case__ :Union[str, Any]=None , snake_case__ :Union[str, Any]=None ) -> Optional[Any]:
_lowercase = ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
_lowercase , _lowercase = _replace_with_bnb_linear(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if not has_been_replaced:
logger.warning(
'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
' Please double check your model architecture, or submit an issue on github if you think this is'
' a bug.' )
return model
def SCREAMING_SNAKE_CASE__ ( *snake_case__ :Optional[Any] , **snake_case__ :Optional[int] ) -> str:
warnings.warn(
'`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead' , snake_case__ , )
return replace_with_bnb_linear(*snake_case__ , **snake_case__ )
def SCREAMING_SNAKE_CASE__ ( *snake_case__ :Dict , **snake_case__ :List[str] ) -> Tuple:
warnings.warn(
'`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead' , snake_case__ , )
return set_module_quantized_tensor_to_device(*snake_case__ , **snake_case__ )
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int ) -> Any:
_lowercase = deepcopy(snake_case__ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
_lowercase = find_tied_parameters(snake_case__ )
# For compatibility with Accelerate < 0.18
if isinstance(snake_case__ , snake_case__ ):
_lowercase = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
_lowercase = sum(snake_case__ , [] )
_lowercase = len(snake_case__ ) > 0
# Check if it is a base model
_lowercase = not hasattr(snake_case__ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
_lowercase = list(model.named_children() )
_lowercase = [list_modules[-1][0]]
# add last module together with tied weights
_lowercase = set(snake_case__ ) - set(snake_case__ )
_lowercase = list(set(snake_case__ ) ) + list(snake_case__ )
# remove ".weight" from the keys
_lowercase = ['.weight', '.bias']
_lowercase = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
_lowercase = name.replace(snake_case__ , '' )
filtered_module_names.append(snake_case__ )
return filtered_module_names | 67 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 0 |
from collections.abc import Callable
import numpy as np
def lowercase__ ( A_: Callable , A_: float , A_: float , A_: float , A_: float ) -> np.array:
"""simple docstring"""
__UpperCAmelCase =int(np.ceil((x_end - xa) / step_size ) )
__UpperCAmelCase =np.zeros((n + 1,) )
__UpperCAmelCase =ya
__UpperCAmelCase =xa
for k in range(A_ ):
__UpperCAmelCase =y[k] + step_size * ode_func(A_ , y[k] )
__UpperCAmelCase =y[k] + (
(step_size / 2) * (ode_func(A_ , y[k] ) + ode_func(x + step_size , A_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 68 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Any = "xlnet"
a__ : Dict = ["mems"]
a__ : List[str] = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , __lowerCAmelCase : Dict=3_20_00 , __lowerCAmelCase : List[str]=10_24 , __lowerCAmelCase : Dict=24 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=40_96 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]="bi" , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Optional[Any]=5_12 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Any="last" , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Tuple="tanh" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : str=5 , __lowerCAmelCase : str=5 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=2 , **__lowerCAmelCase : List[str] , ) -> Tuple:
_A = vocab_size
_A = d_model
_A = n_layer
_A = n_head
if d_model % n_head != 0:
raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __lowerCAmelCase , )
_A = kwargs['''use_cache''']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def snake_case_ ( self : Optional[Any] ) -> Union[str, Any]:
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 snake_case_ ( self : Tuple , __lowerCAmelCase : Optional[Any] ) -> Dict:
# 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.''' )
| 2 | 0 |
'''simple docstring'''
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
a : Dict = True
from torch.cuda.amp import autocast
a : str = logging.getLogger(__name__)
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : Any=None ) -> Optional[int]:
return field(default_factory=lambda: default , metadata=_UpperCAmelCase )
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
__SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"""help""": """The dropout ratio for the attention probabilities."""} )
__SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""} )
__SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={
"""help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
} , )
__SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} , )
__SCREAMING_SNAKE_CASE = field(
default=0.05 , metadata={
"""help""": (
"""Propability of each feature vector along the time axis to be chosen as the start of the vector"""
"""span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
"""vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
)
} , )
__SCREAMING_SNAKE_CASE = field(default=0.0 , metadata={"""help""": """The LayerDrop probability."""} )
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__SCREAMING_SNAKE_CASE = field(
default="""train+validation""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__SCREAMING_SNAKE_CASE = field(
default=_UpperCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of validation examples to this """
"""value if set."""
)
} , )
__SCREAMING_SNAKE_CASE = list_field(
default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] , metadata={"""help""": """A list of characters to remove from the transcripts."""} , )
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
def __call__( self : Tuple , a_ : List[Dict[str, Union[List[int], torch.Tensor]]] ):
"""simple docstring"""
__snake_case = [{"input_values": feature["input_values"]} for feature in features]
__snake_case = [{"input_ids": feature["labels"]} for feature in features]
__snake_case = self.processor.pad(
a_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
__snake_case = self.processor.pad(
labels=a_ , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors="pt" , )
# replace padding with -100 to ignore loss correctly
__snake_case = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
__snake_case = labels
return batch
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : List[str] , a_ : nn.Module , a_ : Dict[str, Union[torch.Tensor, Any]] ):
"""simple docstring"""
model.train()
__snake_case = self._prepare_inputs(a_ )
if self.use_amp:
with autocast():
__snake_case = self.compute_loss(a_ , a_ )
else:
__snake_case = self.compute_loss(a_ , a_ )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__snake_case = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__snake_case = loss.sum() / (inputs["labels"] >= 0).sum()
else:
raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )
if self.args.gradient_accumulation_steps > 1:
__snake_case = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(a_ ).backward()
elif self.use_apex:
with amp.scale_loss(a_ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(a_ )
else:
loss.backward()
return loss.detach()
def __UpperCAmelCase ( ) -> Optional[Any]:
# 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.
__snake_case = 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.
__snake_case , __snake_case , __snake_case = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__snake_case , __snake_case , __snake_case = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__snake_case = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__snake_case = 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:
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." )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}''' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , _UpperCAmelCase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__snake_case = datasets.load_dataset(
"common_voice" , data_args.dataset_config_name , split=data_args.train_split_name )
__snake_case = datasets.load_dataset("common_voice" , data_args.dataset_config_name , split="test" )
# Create and save tokenizer
__snake_case = F'''[{"".join(data_args.chars_to_ignore )}]'''
def remove_special_characters(_UpperCAmelCase : Dict ):
__snake_case = re.sub(_UpperCAmelCase , "" , batch["sentence"] ).lower() + " "
return batch
__snake_case = train_dataset.map(_UpperCAmelCase , remove_columns=["sentence"] )
__snake_case = eval_dataset.map(_UpperCAmelCase , remove_columns=["sentence"] )
def extract_all_chars(_UpperCAmelCase : Tuple ):
__snake_case = " ".join(batch["text"] )
__snake_case = list(set(_UpperCAmelCase ) )
return {"vocab": [vocab], "all_text": [all_text]}
__snake_case = train_dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , batch_size=-1 , keep_in_memory=_UpperCAmelCase , remove_columns=train_dataset.column_names , )
__snake_case = train_dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , batch_size=-1 , keep_in_memory=_UpperCAmelCase , remove_columns=eval_dataset.column_names , )
__snake_case = list(set(vocab_train["vocab"][0] ) | set(vocab_test["vocab"][0] ) )
__snake_case = {v: k for k, v in enumerate(_UpperCAmelCase )}
__snake_case = vocab_dict[" "]
del vocab_dict[" "]
__snake_case = len(_UpperCAmelCase )
__snake_case = len(_UpperCAmelCase )
with open("vocab.json" , "w" ) as vocab_file:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__snake_case = WavaVecaCTCTokenizer(
"vocab.json" , unk_token="[UNK]" , pad_token="[PAD]" , word_delimiter_token="|" , )
__snake_case = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0.0 , do_normalize=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase )
__snake_case = WavaVecaProcessor(feature_extractor=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
__snake_case = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction="mean" , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
__snake_case = min(len(_UpperCAmelCase ) , data_args.max_train_samples )
__snake_case = train_dataset.select(range(_UpperCAmelCase ) )
if data_args.max_val_samples is not None:
__snake_case = eval_dataset.select(range(data_args.max_val_samples ) )
__snake_case = torchaudio.transforms.Resample(4_80_00 , 1_60_00 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(_UpperCAmelCase : Tuple ):
__snake_case , __snake_case = torchaudio.load(batch["path"] )
__snake_case = resampler(_UpperCAmelCase ).squeeze().numpy()
__snake_case = 1_60_00
__snake_case = batch["text"]
return batch
__snake_case = train_dataset.map(
_UpperCAmelCase , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
__snake_case = eval_dataset.map(
_UpperCAmelCase , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(_UpperCAmelCase : Dict ):
# check that all files have the correct sampling rate
assert (
len(set(batch["sampling_rate"] ) ) == 1
), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''
__snake_case = processor(
audio=batch["speech"] , text=batch["target_text"] , sampling_rate=batch["sampling_rate"][0] )
batch.update(_UpperCAmelCase )
return batch
__snake_case = train_dataset.map(
_UpperCAmelCase , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , )
__snake_case = eval_dataset.map(
_UpperCAmelCase , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=_UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , )
# Metric
__snake_case = datasets.load_metric("wer" )
def compute_metrics(_UpperCAmelCase : List[str] ):
__snake_case = pred.predictions
__snake_case = np.argmax(_UpperCAmelCase , axis=-1 )
__snake_case = processor.tokenizer.pad_token_id
__snake_case = processor.batch_decode(_UpperCAmelCase )
# we do not want to group tokens when computing the metrics
__snake_case = processor.batch_decode(pred.label_ids , group_tokens=_UpperCAmelCase )
__snake_case = wer_metric.compute(predictions=_UpperCAmelCase , references=_UpperCAmelCase )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__snake_case = DataCollatorCTCWithPadding(processor=_UpperCAmelCase , padding=_UpperCAmelCase )
# Initialize our Trainer
__snake_case = CTCTrainer(
model=_UpperCAmelCase , data_collator=_UpperCAmelCase , args=_UpperCAmelCase , compute_metrics=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__snake_case = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__snake_case = model_args.model_name_or_path
else:
__snake_case = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__snake_case = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
trainer.save_model()
__snake_case = train_result.metrics
__snake_case = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
__snake_case = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics("train" , _UpperCAmelCase )
trainer.save_metrics("train" , _UpperCAmelCase )
trainer.save_state()
# Evaluation
__snake_case = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__snake_case = trainer.evaluate()
__snake_case = data_args.max_val_samples if data_args.max_val_samples is not None else len(_UpperCAmelCase )
__snake_case = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics("eval" , _UpperCAmelCase )
trainer.save_metrics("eval" , _UpperCAmelCase )
return results
if __name__ == "__main__":
main()
| 69 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(_snake_case ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_snake_case ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_snake_case ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def _SCREAMING_SNAKE_CASE ( lowercase : Any , lowercase : Optional[int]=10 ):
'''simple docstring'''
lowerCamelCase_ = []
for _ in range(lowercase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def _SCREAMING_SNAKE_CASE ( lowercase : Union[str, Any] , lowercase : Any=10 ):
'''simple docstring'''
lowerCamelCase_ = []
for step in range(lowercase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ = os.path.join(lowercase , 'schedule.bin' )
torch.save(scheduler.state_dict() , lowercase )
lowerCamelCase_ = torch.load(lowercase )
scheduler.load_state_dict(lowercase )
return lrs
@require_torch
class A( unittest.TestCase ):
'''simple docstring'''
def a__ ( self : str , A_ : List[str] , A_ : str , A_ : Any ) -> Dict:
"""simple docstring"""
self.assertEqual(len(A_ ) , len(A_ ) )
for a, b in zip(A_ , A_ ):
self.assertAlmostEqual(A_ , A_ , delta=A_ )
def a__ ( self : List[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=A_ )
lowerCamelCase_ = torch.tensor([0.4, 0.2, -0.5] )
lowerCamelCase_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowerCamelCase_ = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(100 ):
lowerCamelCase_ = criterion(A_ , A_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def a__ ( self : Optional[int] ) -> int:
"""simple docstring"""
lowerCamelCase_ = torch.tensor([0.1, -0.2, -0.1] , requires_grad=A_ )
lowerCamelCase_ = torch.tensor([0.4, 0.2, -0.5] )
lowerCamelCase_ = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
lowerCamelCase_ = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=A_ , weight_decay=0.0 , relative_step=A_ , scale_parameter=A_ , warmup_init=A_ , )
for _ in range(1000 ):
lowerCamelCase_ = criterion(A_ , A_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class A( unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = nn.Linear(50 , 50 ) if is_torch_available() else None
UpperCamelCase = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
UpperCamelCase = 10
def a__ ( self : Optional[int] , A_ : List[str] , A_ : Tuple , A_ : Optional[int] , A_ : Dict=None ) -> List[str]:
"""simple docstring"""
self.assertEqual(len(A_ ) , len(A_ ) )
for a, b in zip(A_ , A_ ):
self.assertAlmostEqual(A_ , A_ , delta=A_ , msg=A_ )
def a__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = {'num_warmup_steps': 2, 'num_training_steps': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
lowerCamelCase_ = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'num_warmup_steps': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, 'num_cycles': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'num_warmup_steps': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
lowerCamelCase_ , lowerCamelCase_ = data
lowerCamelCase_ = scheduler_func(self.optimizer , **A_ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
lowerCamelCase_ = unwrap_schedule(A_ , self.num_steps )
self.assertListAlmostEqual(
A_ , A_ , tol=1E-2 , msg=f"""failed for {scheduler_func} in normal scheduler""" , )
lowerCamelCase_ = scheduler_func(self.optimizer , **A_ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(A_ ) # wrap to test picklability of the schedule
lowerCamelCase_ = unwrap_and_save_reload_schedule(A_ , self.num_steps )
self.assertListEqual(A_ , A_ , msg=f"""failed for {scheduler_func} in save and reload""" )
class A:
'''simple docstring'''
def __init__( self : Optional[Any] , A_ : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = fn
def __call__( self : List[str] , *A_ : List[Any] , **A_ : Tuple ) -> Any:
"""simple docstring"""
return self.fn(*A_ , **A_ )
@classmethod
def a__ ( self : Dict , A_ : Optional[Any] ) -> int:
"""simple docstring"""
lowerCamelCase_ = list(map(self , scheduler.lr_lambdas ) )
| 70 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_snake_case ) == 1:
return True
_A = series[1] - series[0]
for index in range(len(_snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> float:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
_A = 0
for val in series:
answer += val
return answer / len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
import math
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00 ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = sum(i * i for i in range(1 , n + 1 ) )
UpperCAmelCase_ : Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 71 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_snake_case ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
_A = QuantumRegister(_snake_case , '''qr''' )
_A = ClassicalRegister(_snake_case , '''cr''' )
_A = QuantumCircuit(_snake_case , _snake_case )
_A = number_of_qubits
for i in range(_snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _snake_case , _snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_snake_case , _snake_case )
# simulate with 10000 shots
_A = Aer.get_backend('''qasm_simulator''' )
_A = execute(_snake_case , _snake_case , shots=10_000 )
return job.result().get_counts(_snake_case )
if __name__ == "__main__":
print(
f'Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'
)
| 2 | 0 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 72 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_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_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :str , _snake_case :Any , _snake_case :int , _snake_case :List[Any] ) -> Optional[int]:
for attribute in key.split('''.''' ):
_A = getattr(_snake_case , _snake_case )
if weight_type is not None:
_A = getattr(_snake_case , _snake_case ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :Any , _snake_case :int ) -> Any:
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_snake_case )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "weight" in name:
_A = '''weight'''
elif "bias" in name:
_A = '''bias'''
else:
_A = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :List[str] , _snake_case :List[str] , _snake_case :Optional[int] , _snake_case :List[Any] ) -> Any:
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = 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.'''
)
_A = 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.'''
)
_A = 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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Tuple:
_A = SEWConfig()
if is_finetuned:
_A = model.wav_encoder.wav_model.cfg
else:
_A = model.cfg
_A = fs_config.conv_bias
_A = eval(fs_config.conv_feature_layers )
_A = [x[0] for x in conv_layers]
_A = [x[1] for x in conv_layers]
_A = [x[2] for x in conv_layers]
_A = '''gelu'''
_A = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_A = 0.0
_A = fs_config.activation_fn.name
_A = fs_config.encoder_embed_dim
_A = 0.02
_A = fs_config.encoder_ffn_embed_dim
_A = 1E-5
_A = fs_config.encoder_layerdrop
_A = fs_config.encoder_attention_heads
_A = fs_config.conv_pos_groups
_A = fs_config.conv_pos
_A = len(_snake_case )
_A = fs_config.encoder_layers
_A = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_A = model.cfg
_A = fs_config.final_dropout
_A = fs_config.layerdrop
_A = fs_config.activation_dropout
_A = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_A = fs_config.attention_dropout
_A = fs_config.dropout_input
_A = fs_config.dropout
_A = fs_config.mask_channel_length
_A = fs_config.mask_channel_prob
_A = fs_config.mask_length
_A = fs_config.mask_prob
_A = '''Wav2Vec2FeatureExtractor'''
_A = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] , _snake_case :Union[str, Any] , _snake_case :Optional[Any]=None , _snake_case :Optional[int]=None , _snake_case :Dict=True ) -> List[Any]:
if is_finetuned:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_A = SEWConfig.from_pretrained(_snake_case )
else:
_A = convert_config(model[0] , _snake_case )
_A = model[0].eval()
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
if is_finetuned:
if dict_path:
_A = Dictionary.load(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
_A = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_snake_case , )
_A = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
_A = SEWForCTC(_snake_case )
else:
_A = SEWModel(_snake_case )
feature_extractor.save_pretrained(_snake_case )
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_model.save_pretrained(_snake_case )
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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 2 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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 transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _snake_case :
def __init__( self , a , a=13 , a=32 , a=3 , a=4 , a=[10, 20, 30, 40] , a=[2, 2, 3, 2] , a=True , a=True , a=37 , a="gelu" , a=10 , a=0.02 , a=["stage2", "stage3", "stage4"] , a=[2, 3, 4] , a=None , ) -> str:
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_stages
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = out_features
SCREAMING_SNAKE_CASE = out_indices
SCREAMING_SNAKE_CASE = scope
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels)
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self) -> str:
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=a , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE__ ( self , a , a , a) -> int:
SCREAMING_SNAKE_CASE = ConvNextModel(config=a)
model.to(a)
model.eval()
SCREAMING_SNAKE_CASE = model(a)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def SCREAMING_SNAKE_CASE__ ( self , a , a , a) -> Any:
SCREAMING_SNAKE_CASE = ConvNextForImageClassification(a)
model.to(a)
model.eval()
SCREAMING_SNAKE_CASE = model(a , labels=a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def SCREAMING_SNAKE_CASE__ ( self , a , a , a) -> Dict:
SCREAMING_SNAKE_CASE = ConvNextBackbone(config=a)
model.to(a)
model.eval()
SCREAMING_SNAKE_CASE = model(a)
# verify hidden states
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
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = ConvNextBackbone(config=a)
model.to(a)
model.eval()
SCREAMING_SNAKE_CASE = model(a)
# 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 SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( A__ , A__ , unittest.TestCase ):
_lowercase : List[str] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
_lowercase : Optional[Any] = (
{'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
if is_torch_available()
else {}
)
_lowercase : str = True
_lowercase : List[Any] = False
_lowercase : Dict = False
_lowercase : Optional[Any] = False
_lowercase : int = False
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
SCREAMING_SNAKE_CASE = ConvNextModelTester(self)
SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37)
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
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 SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
return
@unittest.skip(reason='ConvNext does not use inputs_embeds')
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
pass
@unittest.skip(reason='ConvNext does not support input and output embeddings')
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
pass
@unittest.skip(reason='ConvNext does not use feedforward chunking')
def SCREAMING_SNAKE_CASE__ ( self) -> Any:
pass
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(a)
SCREAMING_SNAKE_CASE = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ['pixel_values']
self.assertListEqual(arg_names[:1] , a)
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a)
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*a)
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
def check_hidden_states_output(a , a , a):
SCREAMING_SNAKE_CASE = model_class(a)
model.to(a)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(a , a))
SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(a) , 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] , )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(a , a , a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(a , a , a)
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a)
@slow
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = ConvNextModel.from_pretrained(a)
self.assertIsNotNone(a)
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224').to(a)
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=a , return_tensors='pt').to(a)
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**a)
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape , a)
SCREAMING_SNAKE_CASE = torch.tensor([-0.02_60, -0.47_39, 0.19_11]).to(a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1E-4))
@require_torch
class _snake_case ( unittest.TestCase , A__ ):
_lowercase : Tuple = (ConvNextBackbone,) if is_torch_available() else ()
_lowercase : Tuple = ConvNextConfig
_lowercase : int = False
def SCREAMING_SNAKE_CASE__ ( self) -> int:
SCREAMING_SNAKE_CASE = ConvNextModelTester(self)
| 73 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Any ) -> Any:
pass
@is_pipeline_test
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@require_torch
def snake_case_ ( self : Tuple ) -> Tuple:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCAmelCase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : int ) -> Optional[int]:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> int:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def snake_case_ ( self : Optional[int] ) -> Dict:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 2 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] ):
"""simple docstring"""
self.test()
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = 0
__SCREAMING_SNAKE_CASE : str = False
while not completed:
if counter == 1:
self.reset()
__SCREAMING_SNAKE_CASE : int = self.advance()
if not self.does_advance(_A ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[int] = self.update(_A )
counter += 1
if counter > 1_0000:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCAmelCase__ ( self : Tuple , _A : int ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCAmelCase__ ( self : Any , _A : int ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCAmelCase__ ( self : Tuple , _A : Dict=False ):
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : int , _A : List[int] ):
"""simple docstring"""
super(_A , self ).__init__()
if not isinstance(_A , _A ) or len(_A ) == 0:
raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(_A , _A ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
__SCREAMING_SNAKE_CASE : Tuple = token_ids
__SCREAMING_SNAKE_CASE : List[Any] = len(self.token_ids )
__SCREAMING_SNAKE_CASE : Optional[Any] = -1 # the index of the currently fulfilled step
__SCREAMING_SNAKE_CASE : int = False
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : Tuple , _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(_A )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCAmelCase__ ( self : str , _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(_A )}''' )
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Union[str, Any] = False
if self.does_advance(_A ):
self.fulfilled_idx += 1
__SCREAMING_SNAKE_CASE : int = True
if self.fulfilled_idx == (self.seqlen - 1):
__SCREAMING_SNAKE_CASE : Any = True
__SCREAMING_SNAKE_CASE : Optional[Any] = completed
else:
# failed to make progress.
__SCREAMING_SNAKE_CASE : Optional[int] = True
self.reset()
return stepped, completed, reset
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMING_SNAKE_CASE : List[Any] = 0
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCAmelCase__ ( self : Union[str, Any] , _A : List[Any]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = PhrasalConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.seqlen
__SCREAMING_SNAKE_CASE : Optional[int] = self.fulfilled_idx
__SCREAMING_SNAKE_CASE : Dict = self.completed
return new_constraint
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Optional[int] , _A : List[List[int]] , _A : Tuple=True ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = max([len(_A ) for one in nested_token_ids] )
__SCREAMING_SNAKE_CASE : List[str] = {}
for token_ids in nested_token_ids:
__SCREAMING_SNAKE_CASE : List[str] = root
for tidx, token_id in enumerate(_A ):
if token_id not in level:
__SCREAMING_SNAKE_CASE : Optional[Any] = {}
__SCREAMING_SNAKE_CASE : Dict = level[token_id]
if no_subsets and self.has_subsets(_A , _A ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
F''' {nested_token_ids}.''' )
__SCREAMING_SNAKE_CASE : Dict = root
def UpperCAmelCase__ ( self : Dict , _A : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.trie
for current_token in current_seq:
__SCREAMING_SNAKE_CASE : str = start[current_token]
__SCREAMING_SNAKE_CASE : Optional[Any] = list(start.keys() )
return next_tokens
def UpperCAmelCase__ ( self : Tuple , _A : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.next_tokens(_A )
return len(_A ) == 0
def UpperCAmelCase__ ( self : int , _A : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = list(root.values() )
if len(_A ) == 0:
return 1
else:
return sum([self.count_leaves(_A ) for nn in next_nodes] )
def UpperCAmelCase__ ( self : Union[str, Any] , _A : List[Any] , _A : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.count_leaves(_A )
return len(_A ) != leaf_count
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : str , _A : List[List[int]] ):
"""simple docstring"""
super(_A , self ).__init__()
if not isinstance(_A , _A ) or len(_A ) == 0:
raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(_A , _A ) for token_ids in nested_token_ids ):
raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(_A , _A ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DisjunctiveTrie(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = nested_token_ids
__SCREAMING_SNAKE_CASE : Any = self.trie.max_height
__SCREAMING_SNAKE_CASE : List[Any] = []
__SCREAMING_SNAKE_CASE : Optional[int] = False
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.trie.next_tokens(self.current_seq )
if len(_A ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : Union[str, Any] , _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_A )}''' )
__SCREAMING_SNAKE_CASE : List[str] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCAmelCase__ ( self : List[str] , _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_A )}''' )
__SCREAMING_SNAKE_CASE : int = False
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Union[str, Any] = False
if self.does_advance(_A ):
self.current_seq.append(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = True
else:
__SCREAMING_SNAKE_CASE : Tuple = True
self.reset()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.trie.reached_leaf(self.current_seq )
__SCREAMING_SNAKE_CASE : Optional[Any] = completed
return stepped, completed, reset
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = False
__SCREAMING_SNAKE_CASE : Any = []
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCAmelCase__ ( self : Dict , _A : List[str]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = DisjunctiveConstraint(self.token_ids )
if stateful:
__SCREAMING_SNAKE_CASE : Dict = self.seqlen
__SCREAMING_SNAKE_CASE : Tuple = self.current_seq
__SCREAMING_SNAKE_CASE : Optional[int] = self.completed
return new_constraint
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Dict , _A : List[Constraint] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = constraints
# max # of steps required to fulfill a given constraint
__SCREAMING_SNAKE_CASE : Dict = max([c.seqlen for c in constraints] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(_A )
__SCREAMING_SNAKE_CASE : List[str] = False
self.init_state()
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : List[Any] = None
__SCREAMING_SNAKE_CASE : Union[str, Any] = [constraint.copy(stateful=_A ) for constraint in self.constraints]
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__SCREAMING_SNAKE_CASE : Any = constraint.advance()
if isinstance(_A , _A ):
token_list.append(_A )
elif isinstance(_A , _A ):
token_list.extend(_A )
else:
__SCREAMING_SNAKE_CASE : Any = self.inprogress_constraint.advance()
if isinstance(_A , _A ):
token_list.append(_A )
elif isinstance(_A , _A ):
token_list.extend(_A )
if len(_A ) == 0:
return None
else:
return token_list
def UpperCAmelCase__ ( self : int , _A : Optional[List[int]] ):
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[Any] = self.add(_A )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCAmelCase__ ( self : Optional[int] , _A : int ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[Any] = False, False
if self.completed:
__SCREAMING_SNAKE_CASE : Dict = True
__SCREAMING_SNAKE_CASE : List[str] = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = self.inprogress_constraint.update(_A )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_A ) )
__SCREAMING_SNAKE_CASE : int = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
if len(self.pending_constraints ) == 0:
# we're done!
__SCREAMING_SNAKE_CASE : List[str] = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_A ):
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = pending_constraint.update(_A )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(_A )
__SCREAMING_SNAKE_CASE : Tuple = None
if not complete and stepped:
__SCREAMING_SNAKE_CASE : List[str] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__SCREAMING_SNAKE_CASE : Optional[int] = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
__SCREAMING_SNAKE_CASE : Any = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCAmelCase__ ( self : Union[str, Any] , _A : int=True ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__SCREAMING_SNAKE_CASE : List[str] = [
constraint.copy(stateful=_A ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__SCREAMING_SNAKE_CASE : Tuple = self.inprogress_constraint.copy(stateful=_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 74 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : Tuple ) -> Optional[int]:
_A = tempfile.mkdtemp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_A = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Dict , **__lowerCAmelCase : int ) -> Optional[int]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : str , **__lowerCAmelCase : Optional[Any] ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Tuple , **__lowerCAmelCase : str ) -> Union[str, Any]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def snake_case_ ( self : int ) -> Optional[Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ ( self : Dict ) -> List[str]:
_A = self.get_tokenizer()
_A = self.get_rust_tokenizer()
_A = self.get_image_processor()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase )
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __lowerCAmelCase )
def snake_case_ ( self : List[Any] ) -> List[str]:
_A = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
_A = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(__lowerCAmelCase , return_tensors='''np''' )
_A = processor(images=__lowerCAmelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = processor(text=__lowerCAmelCase )
_A = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ ( self : List[str] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def snake_case_ ( self : Optional[Any] ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__lowerCAmelCase )
_A = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 2 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
def __init__( self : Optional[Any] , _A : List[Any] , _A : Tuple=12 , _A : Tuple=7 , _A : str=True , _A : List[Any]=True , _A : Any=True , _A : Optional[Any]=99 , _A : Tuple=32 , _A : Dict=32 , _A : Tuple=2 , _A : Any=4 , _A : Dict=37 , _A : int=0.1 , _A : List[str]=0.1 , _A : Optional[int]=512 , _A : int=0.0_2 , _A : List[Any]=0 , _A : Union[str, Any]=None , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : Tuple = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[int] = use_input_mask
UpperCAmelCase__ : Tuple = use_labels
UpperCAmelCase__ : Dict = vocab_size
UpperCAmelCase__ : str = hidden_size
UpperCAmelCase__ : List[str] = projection_dim
UpperCAmelCase__ : Tuple = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : List[Any] = dropout
UpperCAmelCase__ : int = attention_dropout
UpperCAmelCase__ : Optional[int] = max_position_embeddings
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[str] = scope
UpperCAmelCase__ : Union[str, Any] = bos_token_id
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ : Tuple = None
if self.use_input_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
UpperCAmelCase__ : Optional[int] = input_mask.numpy()
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = input_mask.shape
UpperCAmelCase__ : Dict = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_A ):
UpperCAmelCase__ : str = 1
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : Optional[int] = self.get_config()
return config, input_ids, tf.convert_to_tensor(_A )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowercase_ ( self : Optional[Any] , _A : int , _A : Dict , _A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = TFBlipTextModel(config=_A )
UpperCAmelCase__ : Optional[int] = model(_A , attention_mask=_A , training=_A )
UpperCAmelCase__ : List[Any] = model(_A , training=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = config_and_inputs
UpperCAmelCase__ : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( __a , unittest.TestCase ):
lowerCAmelCase__ = (TFBlipTextModel,) if is_tf_available() else ()
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = BlipTextModelTester(self )
UpperCAmelCase__ : str = ConfigTester(self , config_class=_A , hidden_size=37 )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : Any ):
'''simple docstring'''
pass
def lowercase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
pass
@slow
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Any = TFBlipTextModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def lowercase_ ( self : Any , _A : Tuple=True ):
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=_A )
| 75 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = "openai-gpt"
a__ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Union[str, Any] , __lowerCAmelCase : int=4_04_78 , __lowerCAmelCase : Tuple=5_12 , __lowerCAmelCase : str=7_68 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=1E-5 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Optional[Any]="cls_index" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=0.1 , **__lowerCAmelCase : Tuple , ) -> Optional[Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**__lowerCAmelCase )
| 2 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : List[str] = tf.convert_to_tensor(__UpperCamelCase )
__lowercase : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Optional[int] = tf.convert_to_tensor(__UpperCamelCase )
__lowercase : str = tf.cast(math.pi , x.dtype )
__lowercase : Dict = tf.cast(0.044_715 , x.dtype )
__lowercase : Any = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Tuple = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : str = tf.convert_to_tensor(__UpperCamelCase )
__lowercase : int = tf.cast(0.044_715 , x.dtype )
__lowercase : Union[str, Any] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
__lowercase : Dict = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __UpperCAmelCase ( __UpperCamelCase ):
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -10 , 10 )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase=-1 ):
__lowercase ,__lowercase : str = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('2.4'):
def __UpperCAmelCase ( __UpperCamelCase ):
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
a_ = tf.keras.activations.gelu
a_ = approximate_gelu_wrap
else:
a_ = _gelu
a_ = _gelu_new
a_ = {
'gelu': gelu,
'gelu_10': gelu_aa,
'gelu_fast': gelu_fast,
'gelu_new': gelu_new,
'glu': glu,
'mish': mish,
'quick_gelu': quick_gelu,
'relu': tf.keras.activations.relu,
'sigmoid': tf.keras.activations.sigmoid,
'silu': tf.keras.activations.swish,
'swish': tf.keras.activations.swish,
'tanh': tf.keras.activations.tanh,
}
def __UpperCAmelCase ( __UpperCamelCase ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )
| 76 |
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 DeformableDetrImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : int=30 , __lowerCAmelCase : Dict=4_00 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=1 / 2_55 , __lowerCAmelCase : int=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
_A = parent
_A = batch_size
_A = num_channels
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
_A = do_rescale
_A = rescale_factor
_A = do_pad
def snake_case_ ( self : Optional[int] ) -> Any:
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 snake_case_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False ) -> Dict:
if not batched:
_A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
_A , _A = image.size
else:
_A , _A = image.shape[1], image.shape[2]
if w < h:
_A = int(self.size['''shortest_edge'''] * h / w )
_A = self.size['''shortest_edge''']
elif w > h:
_A = self.size['''shortest_edge''']
_A = int(self.size['''shortest_edge'''] * w / h )
else:
_A = self.size['''shortest_edge''']
_A = self.size['''shortest_edge''']
else:
_A = []
for image in image_inputs:
_A , _A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase__ ( _A , unittest.TestCase):
"""simple docstring"""
a__ : Any = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case_ ( self : Optional[int] ) -> Any:
_A = DeformableDetrImageProcessingTester(self )
@property
def snake_case_ ( self : Union[str, Any] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = 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 , '''do_rescale''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def snake_case_ ( self : List[str] ) -> int:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
_A = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def snake_case_ ( self : Any ) -> Union[str, Any]:
pass
def snake_case_ ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
_A = image_processing(__lowerCAmelCase , 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 snake_case_ ( self : Tuple ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
# prepare image and target
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
_A = DeformableDetrImageProcessor()
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def snake_case_ ( self : List[str] ) -> List[str]:
# prepare image, target and masks_path
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
_A = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_A = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
_A = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 2 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
"""configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
"""tokenization_deberta""": ["""DebertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ["""DebertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DebertaForMaskedLM""",
"""DebertaForQuestionAnswering""",
"""DebertaForSequenceClassification""",
"""DebertaForTokenClassification""",
"""DebertaModel""",
"""DebertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
"""TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDebertaForMaskedLM""",
"""TFDebertaForQuestionAnswering""",
"""TFDebertaForSequenceClassification""",
"""TFDebertaForTokenClassification""",
"""TFDebertaModel""",
"""TFDebertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 77 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCAmelCase_ ( snake_case_ : List[Any] ) -> int:
'''simple docstring'''
UpperCAmelCase_ = int(snake_case_ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = t // 36_00, (t // 60) % 60, t % 60
return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""
def lowerCAmelCase_ ( snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int=3_00 ) -> str:
'''simple docstring'''
return f"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = "<table border=\"1\" class=\"dataframe\">\n"
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
UpperCAmelCase_ = f"""{elt:.6f}""" if isinstance(snake_case_ , snake_case_ ) else str(snake_case_ )
html_code += f""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __A :
a__ : Dict = 5
a__ : Any = 0.2
def __init__(self : Optional[Any] , __a : int , __a : Optional[str] = None , __a : bool = True , __a : Optional["NotebookTrainingTracker"] = None , __a : int = 300 , ):
UpperCAmelCase_ = total
UpperCAmelCase_ = "" if prefix is None else prefix
UpperCAmelCase_ = leave
UpperCAmelCase_ = parent
UpperCAmelCase_ = width
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def _lowercase (self : str , __a : int , __a : bool = False , __a : str = None ):
UpperCAmelCase_ = value
if comment is not None:
UpperCAmelCase_ = comment
if self.last_value is None:
UpperCAmelCase_ = UpperCAmelCase_ = time.time()
UpperCAmelCase_ = UpperCAmelCase_ = value
UpperCAmelCase_ = UpperCAmelCase_ = None
UpperCAmelCase_ = self.warmup
UpperCAmelCase_ = 1
self.update_bar(__a )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
UpperCAmelCase_ = time.time()
UpperCAmelCase_ = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
UpperCAmelCase_ = self.elapsed_time / (value - self.start_value)
else:
UpperCAmelCase_ = None
if value >= self.total:
UpperCAmelCase_ = self.total
UpperCAmelCase_ = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
UpperCAmelCase_ = self.average_time_per_item * (self.total - value)
self.update_bar(__a )
UpperCAmelCase_ = value
UpperCAmelCase_ = current_time
if self.average_time_per_item is None:
UpperCAmelCase_ = 1
else:
UpperCAmelCase_ = max(int(self.update_every / self.average_time_per_item ) , 1 )
def _lowercase (self : Optional[int] , __a : Tuple , __a : List[Any]=None ):
UpperCAmelCase_ = " " * (len(str(self.total ) ) - len(str(__a ) )) + str(__a )
if self.elapsed_time is None:
UpperCAmelCase_ = f"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
UpperCAmelCase_ = f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"""
else:
UpperCAmelCase_ = (
f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"""
f""" {format_time(self.predicted_remaining )}"""
)
self.label += f""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f""", {self.comment}]"""
self.display()
def _lowercase (self : Dict ):
UpperCAmelCase_ = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
UpperCAmelCase_ = disp.display(disp.HTML(self.html_code ) , display_id=__a )
else:
self.output.update(disp.HTML(self.html_code ) )
def _lowercase (self : Optional[Any] ):
if self.parent is None and self.output is not None:
self.output.update(disp.HTML("" ) )
class __A ( UpperCamelCase__ ):
def __init__(self : List[Any] , __a : List[Any] , __a : Dict=None ):
super().__init__(__a )
UpperCAmelCase_ = None if column_names is None else [column_names]
UpperCAmelCase_ = None
def _lowercase (self : str ):
UpperCAmelCase_ = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
UpperCAmelCase_ = disp.display(disp.HTML(self.html_code ) , display_id=__a )
else:
self.output.update(disp.HTML(self.html_code ) )
def _lowercase (self : List[str] , __a : Union[str, Any] ):
if self.inner_table is None:
UpperCAmelCase_ = [list(values.keys() ), list(values.values() )]
else:
UpperCAmelCase_ = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(__a )
UpperCAmelCase_ = columns
self.inner_table.append([values[c] for c in columns] )
def _lowercase (self : Dict , __a : Optional[int] , __a : int=None , __a : int=300 ):
UpperCAmelCase_ = NotebookProgressBar(__a , prefix=__a , parent=self , width=__a )
return self.child_bar
def _lowercase (self : Dict ):
UpperCAmelCase_ = None
self.display()
class __A ( UpperCamelCase__ ):
def __init__(self : Dict ):
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = False
def _lowercase (self : int , __a : Optional[Any] , __a : Any , __a : List[str] , **__a : Union[str, Any] ):
UpperCAmelCase_ = "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = [self.first_column] + ["Training Loss"]
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append("Validation Loss" )
UpperCAmelCase_ = NotebookTrainingTracker(state.max_steps , __a )
def _lowercase (self : Union[str, Any] , __a : str , __a : Dict , __a : Dict , **__a : List[Any] ):
UpperCAmelCase_ = int(state.epoch ) if int(state.epoch ) == state.epoch else f"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=f"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
UpperCAmelCase_ = False
def _lowercase (self : List[Any] , __a : str , __a : Optional[Any] , __a : Optional[int] , __a : Tuple=None , **__a : List[str] ):
if not has_length(__a ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
UpperCAmelCase_ = self.training_tracker.add_child(len(__a ) )
else:
UpperCAmelCase_ = NotebookProgressBar(len(__a ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def _lowercase (self : int , __a : List[str] , __a : Union[str, Any] , __a : Optional[int] , **__a : Tuple ):
if self.prediction_bar is not None:
self.prediction_bar.close()
UpperCAmelCase_ = None
def _lowercase (self : Union[str, Any] , __a : Optional[int] , __a : Any , __a : List[Any] , __a : Tuple=None , **__a : List[Any] ):
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
UpperCAmelCase_ = {"Training Loss": logs["loss"]}
# First column is necessarily Step sine we're not in epoch eval strategy
UpperCAmelCase_ = state.global_step
self.training_tracker.write_line(__a )
def _lowercase (self : Optional[int] , __a : str , __a : Tuple , __a : Optional[int] , __a : Union[str, Any]=None , **__a : List[Any] ):
if self.training_tracker is not None:
UpperCAmelCase_ = {"Training Loss": "No log", "Validation Loss": "No log"}
for log in reversed(state.log_history ):
if "loss" in log:
UpperCAmelCase_ = log["loss"]
break
if self.first_column == "Epoch":
UpperCAmelCase_ = int(state.epoch )
else:
UpperCAmelCase_ = state.global_step
UpperCAmelCase_ = "eval"
for k in metrics:
if k.endswith("_loss" ):
UpperCAmelCase_ = re.sub(r"\_loss$" , "" , __a )
UpperCAmelCase_ = metrics.pop("total_flos" , __a )
UpperCAmelCase_ = metrics.pop("epoch" , __a )
UpperCAmelCase_ = metrics.pop(f"""{metric_key_prefix}_runtime""" , __a )
UpperCAmelCase_ = metrics.pop(f"""{metric_key_prefix}_samples_per_second""" , __a )
UpperCAmelCase_ = metrics.pop(f"""{metric_key_prefix}_steps_per_second""" , __a )
UpperCAmelCase_ = metrics.pop(f"""{metric_key_prefix}_jit_compilation_time""" , __a )
for k, v in metrics.items():
if k == f"""{metric_key_prefix}_loss""":
UpperCAmelCase_ = v
else:
UpperCAmelCase_ = k.split("_" )
UpperCAmelCase_ = " ".join([part.capitalize() for part in splits[1:]] )
UpperCAmelCase_ = v
self.training_tracker.write_line(__a )
self.training_tracker.remove_child()
UpperCAmelCase_ = None
# Evaluation takes a long time so we should force the next update.
UpperCAmelCase_ = True
def _lowercase (self : Dict , __a : Dict , __a : Any , __a : Optional[Any] , **__a : Union[str, Any] ):
self.training_tracker.update(
state.global_step , comment=f"""Epoch {int(state.epoch )}/{state.num_train_epochs}""" , force_update=__a )
UpperCAmelCase_ = None
| 78 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
_A = 2
while True:
if is_prime(_snake_case ):
yield num
num += 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 10_001 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , _snake_case ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 2 | 0 |
import baseaa
def _lowerCamelCase ( __lowerCamelCase ) -> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def _lowerCamelCase ( __lowerCamelCase ) -> str:
'''simple docstring'''
return baseaa.baadecode(__lowerCamelCase ).decode("""utf-8""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Dict = """Hello World!"""
SCREAMING_SNAKE_CASE__ : int = baseaa_encode(test)
print(encoded)
SCREAMING_SNAKE_CASE__ : Dict = baseaa_decode(encoded)
print(decoded)
| 79 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase_ = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase_ = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase_ = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase_ = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase_ = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase_ = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase_ = re.compile(r"""^\s*else:""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] ) -> Any:
if _re_test_backend.search(_snake_case ) is None:
return None
_A = [b[0] for b in _re_backend.findall(_snake_case )]
backends.sort()
return "_and_".join(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any ) -> Any:
with open(_snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_A = f.readlines()
_A = 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
_A = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
_A = 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 ):
_A = _re_one_line_import_struct.search(_snake_case ).groups()[0]
_A = re.findall(r'''\[([^\]]+)\]''' , _snake_case )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
_A = _re_import_struct_key_value.search(_snake_case )
if single_line_import_search is not None:
_A = [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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
_A = 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:
_A = _re_import_struct_add_many.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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:
_A = _re_between_brackets.search(_snake_case ).groups()[0].split(''', ''' )
_A = [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
_A = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_A = []
while (
line_index < len(_snake_case )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
_A = lines[line_index]
_A = _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
_A = {'''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.
_A = 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:
_A = 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
_A = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
_A = lines[line_index]
_A = _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
_A = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] , _snake_case :Dict ) -> Any:
def find_duplicates(_snake_case :Any ):
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!"]
_A = []
for key in import_dict_objects.keys():
_A = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_A = 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] ) ):
_A = '''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 SCREAMING_SNAKE_CASE_ ( ) -> int:
_A = []
for root, _, files in os.walk(_snake_case ):
if "__init__.py" in files:
_A = os.path.join(_snake_case , '''__init__.py''' )
_A = parse_init(_snake_case )
if objects is not None:
_A = analyze_results(*_snake_case )
if len(_snake_case ) > 0:
_A = 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 SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_A = []
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
_A = str((Path(_snake_case ) / folder).relative_to(_snake_case ) )
_A = short_path.replace(os.path.sep , '''.''' )
submodules.append(_snake_case )
for fname in files:
if fname == "__init__.py":
continue
_A = str((Path(_snake_case ) / fname).relative_to(_snake_case ) )
_A = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_snake_case )
return submodules
UpperCAmelCase_ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_A = direct_transformers_import(_snake_case )
_A = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_snake_case , '''__init__.py''' ) , '''r''' ) as f:
_A = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , _snake_case ) ) )
_A = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_snake_case ) > 0:
_A = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 2 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
__lowercase = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
model.to(_lowerCAmelCase )
from datasets import load_dataset
__lowercase = load_dataset("""nielsr/rvlcdip-demo""" )
__lowercase = dataset["""train"""][0]["""image"""].convert("""RGB""" )
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = outputs.logits
__lowercase = torch.Size((1, 16) )
self.assertEqual(logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=_lowerCAmelCase , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 80 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(_A)
class lowerCamelCase__ ( _A):
"""simple docstring"""
def __init__( self : Optional[int] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : List[str] ) -> List[str]:
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def snake_case_ ( self : Any , __lowerCAmelCase : Dict=None , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=None ) -> int:
_A = {}
_A = {}
if prompt is not None:
_A = prompt
if generate_kwargs is not None:
_A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
_A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : List[str] , __lowerCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
return super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any]=None ) -> int:
_A = load_image(__lowerCAmelCase )
if prompt is not None:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(__lowerCAmelCase )} - but expected a single string. '''
'''Note also that one single text can be provided for conditional image to text generation.''' )
_A = self.model.config.model_type
if model_type == "git":
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(text=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids
_A = [self.tokenizer.cls_token_id] + input_ids
_A = torch.tensor(__lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
_A = self.image_processor(images=__lowerCAmelCase , header_text=__lowerCAmelCase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
_A = self.tokenizer(__lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(__lowerCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
_A = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A = None
return model_inputs
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict=None ) -> str:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __lowerCAmelCase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
_A = None
if generate_kwargs is None:
_A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A = model_inputs.pop(self.model.main_input_name )
_A = self.model.generate(__lowerCAmelCase , **__lowerCAmelCase , **__lowerCAmelCase )
return model_outputs
def snake_case_ ( self : Dict , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = []
for output_ids in model_outputs:
_A = {
'''generated_text''': self.tokenizer.decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , )
}
records.append(__lowerCAmelCase )
return records
| 2 | 0 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case : Any = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : List[str] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
__snake_case : int = 1_9_2
__snake_case : List[Any] = 7_6_8
__snake_case : Union[str, Any] = 1_2
__snake_case : Optional[Any] = 3
__snake_case : Any = [8_0_0, 1_3_3_3]
__snake_case : List[str] = False
elif yolos_name == "yolos_s_dWr":
__snake_case : List[str] = 3_3_0
__snake_case : Dict = 1_4
__snake_case : List[str] = 6
__snake_case : Union[str, Any] = 1_3_2_0
elif "yolos_s" in yolos_name:
__snake_case : Union[str, Any] = 3_8_4
__snake_case : Dict = 1_5_3_6
__snake_case : int = 1_2
__snake_case : Optional[Any] = 6
elif "yolos_b" in yolos_name:
__snake_case : Union[str, Any] = [8_0_0, 1_3_4_4]
__snake_case : Optional[int] = 9_1
__snake_case : Any = "huggingface/label-files"
__snake_case : Optional[int] = "coco-detection-id2label.json"
__snake_case : Optional[Any] = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="dataset" ) , "r" ) )
__snake_case : Dict = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
__snake_case : str = idalabel
__snake_case : int = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = False ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__snake_case : Any = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
__snake_case : int = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__snake_case : Any = in_proj_weight[: config.hidden_size, :]
__snake_case : Dict = in_proj_bias[: config.hidden_size]
__snake_case : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__snake_case : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__snake_case : List[Any] = in_proj_weight[-config.hidden_size :, :]
__snake_case : Union[str, Any] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __lowerCamelCase ):
if "backbone" in name:
__snake_case : Any = name.replace("backbone" , "vit" )
if "cls_token" in name:
__snake_case : Any = name.replace("cls_token" , "embeddings.cls_token" )
if "det_token" in name:
__snake_case : Optional[Any] = name.replace("det_token" , "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
__snake_case : Optional[int] = name.replace("mid_pos_embed" , "encoder.mid_position_embeddings" )
if "pos_embed" in name:
__snake_case : Optional[Any] = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
__snake_case : List[str] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "blocks" in name:
__snake_case : Dict = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
__snake_case : Union[str, Any] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
__snake_case : Any = name.replace("attn" , "attention.self" )
if "norm1" in name:
__snake_case : Union[str, Any] = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
__snake_case : List[str] = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
__snake_case : int = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
__snake_case : List[str] = name.replace("mlp.fc2" , "output.dense" )
if "class_embed" in name:
__snake_case : Any = name.replace("class_embed" , "class_labels_classifier" )
if "bbox_embed" in name:
__snake_case : List[Any] = name.replace("bbox_embed" , "bbox_predictor" )
if "vit.norm" in name:
__snake_case : Union[str, Any] = name.replace("vit.norm" , "vit.layernorm" )
return name
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
for key in orig_state_dict.copy().keys():
__snake_case : str = orig_state_dict.pop(__lowerCamelCase )
if "qkv" in key:
__snake_case : Tuple = key.split("." )
__snake_case : List[Any] = int(key_split[2] )
__snake_case : Tuple = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
__snake_case : Optional[Any] = val[:dim, :]
__snake_case : Union[str, Any] = val[
dim : dim * 2, :
]
__snake_case : List[Any] = val[-dim:, :]
else:
__snake_case : Tuple = val[:dim]
__snake_case : Optional[int] = val[dim : dim * 2]
__snake_case : Tuple = val[-dim:]
else:
__snake_case : Tuple = val
return orig_state_dict
def lowerCAmelCase_ ( ):
__snake_case : Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
__snake_case : List[str] = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = False ):
__snake_case : str = get_yolos_config(__lowerCamelCase )
# load original state_dict
__snake_case : int = torch.load(__lowerCamelCase , map_location="cpu" )["model"]
# load 🤗 model
__snake_case : str = YolosForObjectDetection(__lowerCamelCase )
model.eval()
__snake_case : Dict = convert_state_dict(__lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# Check outputs on an image, prepared by YolosImageProcessor
__snake_case : Dict = 8_0_0 if yolos_name != "yolos_ti" else 5_1_2
__snake_case : str = YolosImageProcessor(format="coco_detection" , size=__lowerCamelCase )
__snake_case : List[Any] = image_processor(images=prepare_img() , return_tensors="pt" )
__snake_case : Optional[Any] = model(**__lowerCamelCase )
__snake_case , __snake_case : Tuple = outputs.logits, outputs.pred_boxes
__snake_case , __snake_case : Tuple = None, None
if yolos_name == "yolos_ti":
__snake_case : List[str] = torch.tensor(
[[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
__snake_case : str = torch.tensor(
[[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
elif yolos_name == "yolos_s_200_pre":
__snake_case : List[str] = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
__snake_case : Union[str, Any] = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
elif yolos_name == "yolos_s_300_pre":
__snake_case : Tuple = torch.tensor(
[[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
__snake_case : Dict = torch.tensor(
[[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
elif yolos_name == "yolos_s_dWr":
__snake_case : Optional[int] = torch.tensor(
[[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
__snake_case : Dict = torch.tensor(
[[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
elif yolos_name == "yolos_base":
__snake_case : List[str] = torch.tensor(
[[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
__snake_case : Optional[Any] = torch.tensor(
[[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
else:
raise ValueError(F'Unknown yolos_name: {yolos_name}' )
assert torch.allclose(logits[0, :3, :3] , __lowerCamelCase , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __lowerCamelCase , atol=1e-4 )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
__snake_case : Dict = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
__snake_case : Optional[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(__lowerCamelCase , organization="hustvl" )
model.push_to_hub(__lowerCamelCase , organization="hustvl" )
if __name__ == "__main__":
_snake_case : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--yolos_name",
default="yolos_s_200_pre",
type=str,
help=(
"Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
),
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_snake_case : str = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 81 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( _snake_case :str = "AAPL" ) -> str:
_A = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_A = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
_A = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 2 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class lowercase__ ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def lowercase__ ( self : Optional[Any] , _UpperCAmelCase : Tuple=0 ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = floats_tensor((1, 3, 128, 128) , rng=random.Random(_UpperCAmelCase ) )
UpperCAmelCase_ = np.random.RandomState(_UpperCAmelCase )
UpperCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def lowercase__ ( self : str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def lowercase__ ( self : Dict ) -> str:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# warmup pass to apply optimizations
UpperCAmelCase_ = pipe(**self.get_dummy_inputs() )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase__ ( self : Any ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def lowercase__ ( self : List[str] ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
UpperCAmelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs()
UpperCAmelCase_ = pipe(**_UpperCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase_ = np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def lowercase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = False
return options
def lowercase__ ( self : Dict ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
UpperCAmelCase_ = init_image.resize((768, 512) )
# using the PNDM scheduler by default
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = "A fantasy landscape, trending on artstation"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = pipe(
prompt=_UpperCAmelCase , image=_UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=_UpperCAmelCase , output_type="np" , )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
UpperCAmelCase_ = np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def lowercase__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
UpperCAmelCase_ = init_image.resize((768, 512) )
UpperCAmelCase_ = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
UpperCAmelCase_ = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_UpperCAmelCase , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
UpperCAmelCase_ = "A fantasy landscape, trending on artstation"
UpperCAmelCase_ = np.random.RandomState(0 )
UpperCAmelCase_ = pipe(
prompt=_UpperCAmelCase , image=_UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=_UpperCAmelCase , output_type="np" , )
UpperCAmelCase_ = output.images
UpperCAmelCase_ = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
UpperCAmelCase_ = np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 82 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = 9
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_A = kruskal(_snake_case , _snake_case )
_A = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_snake_case ) == sorted(_snake_case )
| 2 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : List[str] ):
'''simple docstring'''
_lowerCamelCase : Tuple = 0
_lowerCamelCase : Union[str, Any] = len(A_ )
for i in range(n - 1 ):
for j in range(i + 1, A_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def snake_case_ ( A_ : List[str] ):
'''simple docstring'''
if len(A_ ) <= 1:
return arr, 0
_lowerCamelCase : int = len(A_ ) // 2
_lowerCamelCase : int = arr[0:mid]
_lowerCamelCase : int = arr[mid:]
_lowerCamelCase , _lowerCamelCase : str = count_inversions_recursive(A_ )
_lowerCamelCase , _lowerCamelCase : int = count_inversions_recursive(A_ )
_lowerCamelCase , _lowerCamelCase : Optional[Any] = _count_cross_inversions(A_, A_ )
_lowerCamelCase : Optional[Any] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def snake_case_ ( A_ : int, A_ : Dict ):
'''simple docstring'''
_lowerCamelCase : Tuple = []
_lowerCamelCase : List[str] = 0
while i < len(A_ ) and j < len(A_ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(A_ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(A_ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Tuple = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
_lowerCamelCase : List[str] = count_inversions_bf(A_ )
_lowerCamelCase , _lowerCamelCase : List[str] = count_inversions_recursive(A_ )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''', A_ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
_lowerCamelCase : List[Any] = count_inversions_bf(A_ )
_lowerCamelCase , _lowerCamelCase : List[Any] = count_inversions_recursive(A_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', A_ )
# an empty list should also have zero inversions
_lowerCamelCase : str = []
_lowerCamelCase : Optional[int] = count_inversions_bf(A_ )
_lowerCamelCase , _lowerCamelCase : Any = count_inversions_recursive(A_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', A_ )
if __name__ == "__main__":
main()
| 83 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
return (data["data"], data["target"])
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Predict target for test data
lowercase = xgb.predict(__SCREAMING_SNAKE_CASE )
lowercase = predictions.reshape(len(__SCREAMING_SNAKE_CASE ) , 1 )
return predictions
def UpperCAmelCase_ ( ):
lowercase = fetch_california_housing()
lowercase , lowercase = data_handling(__SCREAMING_SNAKE_CASE )
lowercase , lowercase , lowercase , lowercase = train_test_split(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , test_size=0.25 , random_state=1 )
lowercase = xgboost(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}''' )
print(F'''Mean Square Error : {mean_squared_error(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 84 |
UpperCAmelCase_ = 2_5_6
# Modulus to hash a string
UpperCAmelCase_ = 1_0_0_0_0_0_3
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :str ) -> bool:
_A = len(_snake_case )
_A = len(_snake_case )
if p_len > t_len:
return False
_A = 0
_A = 0
_A = 1
# Calculating the hash of pattern and substring of text
for i in range(_snake_case ):
_A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def SCREAMING_SNAKE_CASE_ ( ) -> None:
_A = '''abc1abc12'''
_A = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
_A = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(_snake_case , _snake_case ) and not rabin_karp(_snake_case , _snake_case )
# Test 2)
_A = '''ABABX'''
_A = '''ABABZABABYABABX'''
assert rabin_karp(_snake_case , _snake_case )
# Test 3)
_A = '''AAAB'''
_A = '''ABAAAAAB'''
assert rabin_karp(_snake_case , _snake_case )
# Test 4)
_A = '''abcdabcy'''
_A = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(_snake_case , _snake_case )
# Test 5)
_A = '''Lü'''
_A = '''Lüsai'''
assert rabin_karp(_snake_case , _snake_case )
_A = '''Lue'''
assert not rabin_karp(_snake_case , _snake_case )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 2 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_ )
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[str] , **a_ : Tuple )-> List[str]:
"""simple docstring"""
super().__init__(**a_ )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : Union[str, Any] , a_ : Union[str, List[str], "Image", List["Image"]] , **a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
return super().__call__(a_ , **a_ )
def __lowercase( self : Dict , **a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = {}
if "candidate_labels" in kwargs:
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs['candidate_labels']
if "hypothesis_template" in kwargs:
SCREAMING_SNAKE_CASE__ : str = kwargs['hypothesis_template']
return preprocess_params, {}, {}
def __lowercase( self : str , a_ : int , a_ : Optional[Any]=None , a_ : int="This is a photo of {}." )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = load_image(a_ )
SCREAMING_SNAKE_CASE__ : Any = self.image_processor(images=[image] , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : str = candidate_labels
SCREAMING_SNAKE_CASE__ : Any = [hypothesis_template.format(a_ ) for x in candidate_labels]
SCREAMING_SNAKE_CASE__ : List[str] = self.tokenizer(a_ , return_tensors=self.framework , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = [text_inputs]
return inputs
def __lowercase( self : Union[str, Any] , a_ : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = model_inputs.pop('candidate_labels' )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_inputs.pop('text_inputs' )
if isinstance(text_inputs[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = text_inputs[0]
else:
# Batching case.
SCREAMING_SNAKE_CASE__ : Optional[Any] = text_inputs[0][0]
SCREAMING_SNAKE_CASE__ : Any = self.model(**a_ , **a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'candidate_labels': candidate_labels,
'logits': outputs.logits_per_image,
}
return model_outputs
def __lowercase( self : Optional[int] , a_ : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = model_outputs.pop('candidate_labels' )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_outputs['logits'][0]
if self.framework == "pt":
SCREAMING_SNAKE_CASE__ : List[Any] = logits.softmax(dim=-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : str = probs.tolist()
if not isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = [scores]
elif self.framework == "tf":
SCREAMING_SNAKE_CASE__ : Optional[int] = stable_softmax(a_ , axis=-1 )
SCREAMING_SNAKE_CASE__ : int = probs.numpy().tolist()
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = [
{'score': score, 'label': candidate_label}
for score, candidate_label in sorted(zip(a_ , a_ ) , key=lambda a_ : -x[0] )
]
return result
| 85 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
UpperCAmelCase_ = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
UpperCAmelCase_ = """</w>"""
UpperCAmelCase_ = """@@ """
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[Any] ) -> List[str]:
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_A = char
return pairs
# Speech2Text2 has no max input length
UpperCAmelCase_ = {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Dict = VOCAB_FILES_NAMES
a__ : str = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : List[Any] = ["input_ids", "attention_mask"]
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str]="<s>" , __lowerCAmelCase : Tuple="<pad>" , __lowerCAmelCase : Optional[Any]="</s>" , __lowerCAmelCase : Dict="<unk>" , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=None , **__lowerCAmelCase : str , ) -> Dict:
super().__init__(
unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , do_lower_case=__lowerCAmelCase , **__lowerCAmelCase , )
_A = do_lower_case
with open(__lowerCAmelCase , encoding='''utf-8''' ) as vocab_handle:
_A = json.load(__lowerCAmelCase )
_A = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''' )
_A = None
_A = None
else:
with open(__lowerCAmelCase , encoding='''utf-8''' ) as merges_handle:
_A = merges_handle.read().split('''\n''' )[:-1]
_A = [tuple(merge.split()[:2] ) for merge in merges]
_A = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_A = {}
@property
def snake_case_ ( self : List[str] ) -> int:
return len(self.decoder )
def snake_case_ ( self : Dict ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : Any ) -> Union[str, Any]:
_A = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
_A = get_pairs(__lowerCAmelCase )
if not pairs:
return token
while True:
_A = min(__lowerCAmelCase , key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(__lowerCAmelCase ):
try:
_A = word.index(__lowerCAmelCase , __lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_A = j
if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(__lowerCAmelCase )
_A = new_word
if len(__lowerCAmelCase ) == 1:
break
else:
_A = get_pairs(__lowerCAmelCase )
_A = ''' '''.join(__lowerCAmelCase )
if word == "\n " + BPE_TOKEN_MERGES:
_A = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(__lowerCAmelCase ):
_A = word.replace(__lowerCAmelCase , '''''' )
_A = word.replace(''' ''' , __lowerCAmelCase )
_A = word
return word
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Tuple ) -> Optional[int]:
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
_A = text.lower()
_A = text.split()
_A = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(__lowerCAmelCase ).split(''' ''' ) ) )
return split_tokens
def snake_case_ ( self : List[Any] , __lowerCAmelCase : str ) -> int:
return self.encoder.get(__lowerCAmelCase , self.encoder.get(self.unk_token ) )
def snake_case_ ( self : str , __lowerCAmelCase : int ) -> str:
_A = self.decoder.get(__lowerCAmelCase , self.unk_token )
return result
def snake_case_ ( self : List[str] , __lowerCAmelCase : List[str] ) -> str:
_A = ''' '''.join(__lowerCAmelCase )
# make sure @@ tokens are concatenated
_A = ''''''.join(string.split(__lowerCAmelCase ) )
return string
def snake_case_ ( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCAmelCase , ensure_ascii=__lowerCAmelCase ) + '''\n''' )
_A = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
''' Please check that the tokenizer is not corrupted!''' )
_A = token_index
writer.write(''' '''.join(__lowerCAmelCase ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 2 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__a :Tuple = logging.get_logger(__name__)
__a :int = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : str=None , UpperCAmelCase : Union[str, Any]=None , *UpperCAmelCase : int , **UpperCAmelCase : List[str] ):
super().__init__(*UpperCAmelCase , **UpperCAmelCase )
if config is None:
assert isinstance(self.model , UpperCAmelCase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f''' {self.model.__class__}'''
)
A_ = self.model.config
else:
A_ = config
A_ = data_args
A_ = self.config.tgt_vocab_size if isinstance(self.config , UpperCAmelCase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
" padding.." )
if self.args.label_smoothing == 0:
A_ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
A_ = label_smoothed_nll_loss
def __A ( self : Dict , UpperCAmelCase : int ):
if self.optimizer is None:
A_ = ["bias", "LayerNorm.weight"]
A_ = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
A_ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
A_ = Adafactor
A_ = {"scale_parameter": False, "relative_step": False}
else:
A_ = AdamW
A_ = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
A_ = self.args.learning_rate
if self.sharded_ddp:
A_ = OSS(
params=UpperCAmelCase , optim=UpperCAmelCase , **UpperCAmelCase , )
else:
A_ = optimizer_cls(UpperCAmelCase , **UpperCAmelCase )
if self.lr_scheduler is None:
A_ = self._get_lr_scheduler(UpperCAmelCase )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def __A ( self : Tuple , UpperCAmelCase : List[Any] ):
A_ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
A_ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
A_ = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
A_ = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=UpperCAmelCase )
return scheduler
def __A ( self : Optional[int] ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def __A ( self : Dict , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : str ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
A_ = model(**UpperCAmelCase , use_cache=UpperCAmelCase )[0]
A_ = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
A_ , A_ = model(**UpperCAmelCase , labels=UpperCAmelCase , use_cache=UpperCAmelCase )[:2]
else:
# compute label smoothed loss
A_ = model(**UpperCAmelCase , use_cache=UpperCAmelCase )[0]
A_ = torch.nn.functional.log_softmax(UpperCAmelCase , dim=-1 )
A_ , A_ = self.loss_fn(UpperCAmelCase , UpperCAmelCase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def __A ( self : int , UpperCAmelCase : str , UpperCAmelCase : int ):
A_ = inputs.pop("labels" )
A_ , A_ = self._compute_loss(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return loss
def __A ( self : Optional[Any] , UpperCAmelCase : nn.Module , UpperCAmelCase : Dict[str, Union[torch.Tensor, Any]] , UpperCAmelCase : bool , UpperCAmelCase : Optional[List[str]] = None , ):
A_ = self._prepare_inputs(UpperCAmelCase )
A_ = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
A_ = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **UpperCAmelCase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
A_ = self._pad_tensors_to_max_len(UpperCAmelCase , gen_kwargs["max_length"] )
A_ = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
A_ , A_ = self._compute_loss(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
A_ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
A_ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
A_ = self._pad_tensors_to_max_len(UpperCAmelCase , gen_kwargs["max_length"] )
return (loss, logits, labels)
def __A ( self : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Tuple ):
# If PAD token is not defined at least EOS token has to be defined
A_ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
f''' padded to `max_length`={max_length}''' )
A_ = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
A_ = tensor
return padded_tensor | 86 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> int:
return (2 * position) + 2
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Optional[int] ) -> None:
_A = []
_A = {}
_A = 0
def __len__( self : str ) -> int:
return self.elements
def __repr__( self : Optional[int] ) -> str:
return str(self.heap )
def snake_case_ ( self : str ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def snake_case_ ( self : Optional[int] , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
_A = self.elements
self.elements += 1
self._bubble_up(__lowerCAmelCase )
def snake_case_ ( self : Tuple ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
_A , _A = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
_A , _A = self.heap[0]
self._bubble_down(__lowerCAmelCase )
return elem
def snake_case_ ( self : int , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Update the weight of the given key
_A = self.position_map[elem]
_A = (elem, weight)
if position > 0:
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
else:
self._bubble_down(__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
_A = self.position_map[elem]
if curr_pos == 0:
return None
_A = get_parent_position(__lowerCAmelCase )
_A , _A = self.heap[curr_pos]
_A , _A = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_up(__lowerCAmelCase )
return None
def snake_case_ ( self : Dict , __lowerCAmelCase : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
_A = self.position_map[elem]
_A , _A = self.heap[curr_pos]
_A = get_child_left_position(__lowerCAmelCase )
_A = get_child_right_position(__lowerCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
_A , _A = self.heap[child_left_position]
_A , _A = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
if child_left_position < self.elements:
_A , _A = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
else:
return None
if child_right_position < self.elements:
_A , _A = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__lowerCAmelCase , __lowerCAmelCase )
return self._bubble_down(__lowerCAmelCase )
return None
def snake_case_ ( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
# Swap the nodes at the given positions
_A = self.heap[nodea_pos][0]
_A = self.heap[nodea_pos][0]
_A , _A = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
_A = nodea_pos
_A = nodea_pos
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : str ) -> None:
_A = {}
_A = 0
def __repr__( self : str ) -> str:
return str(self.connections )
def __len__( self : Dict ) -> int:
return self.nodes
def snake_case_ ( self : Any , __lowerCAmelCase : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
_A = {}
self.nodes += 1
def snake_case_ ( self : str , __lowerCAmelCase : T , __lowerCAmelCase : T , __lowerCAmelCase : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__lowerCAmelCase )
self.add_node(__lowerCAmelCase )
_A = weight
_A = weight
def SCREAMING_SNAKE_CASE_ ( _snake_case :GraphUndirectedWeighted[T] , ) -> tuple[dict[T, int], dict[T, T | None]]:
_A = {node: maxsize for node in graph.connections}
_A = {node: None for node in graph.connections}
_A = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(_snake_case , _snake_case )
if priority_queue.is_empty():
return dist, parent
# initialization
_A = priority_queue.extract_min()
_A = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
# running prim's algorithm
while not priority_queue.is_empty():
_A = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
_A = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(_snake_case , dist[neighbour] )
_A = node
return dist, parent
| 2 | 0 |
# 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.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''philschmid/bart-large-cnn-samsum'''
UpperCAmelCase__ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
UpperCAmelCase__ = '''summarizer'''
UpperCAmelCase__ = AutoTokenizer
UpperCAmelCase__ = AutoModelForSeqaSeqLM
UpperCAmelCase__ = ['''text''']
UpperCAmelCase__ = ['''text''']
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Optional[Any]) ->List[Any]:
'''simple docstring'''
return self.pre_processor(UpperCAmelCase__ , return_tensors='''pt''' , truncation=UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : Dict) ->Dict:
'''simple docstring'''
return self.model.generate(**UpperCAmelCase__)[0]
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : str) ->str:
'''simple docstring'''
return self.pre_processor.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__)
| 87 |
import os
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 logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [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]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class lowercase__ ( A_ ):
def __init__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE) -> None:
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , SCREAMING_SNAKE_CASE , )
super().__init__(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
| 88 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( _snake_case :dict , _snake_case :str ) -> set[str]:
_A , _A = set(_snake_case ), [start]
while stack:
_A = stack.pop()
explored.add(_snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_snake_case )
return explored
UpperCAmelCase_ = {
"""A""": ["""B""", """C""", """D"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F"""],
"""D""": ["""B""", """D"""],
"""E""": ["""B""", """F"""],
"""F""": ["""C""", """E""", """G"""],
"""G""": ["""F"""],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, """A"""))
| 2 | 0 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 89 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 2 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : Any = "upernet"
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=5_12 , lowerCamelCase_=0.02 , lowerCamelCase_=[1, 2, 3, 6] , lowerCamelCase_=True , lowerCamelCase_=0.4 , lowerCamelCase_=3_84 , lowerCamelCase_=2_56 , lowerCamelCase_=1 , lowerCamelCase_=False , lowerCamelCase_=2_55 , **lowerCamelCase_ , ) -> Union[str, Any]:
super().__init__(**lowerCamelCase_ )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowerCAmelCase__ = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
lowerCAmelCase__ = backbone_config.get('''model_type''' )
lowerCAmelCase__ = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase__ = config_class.from_dict(lowerCamelCase_ )
lowerCAmelCase__ = backbone_config
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = pool_scales
lowerCAmelCase__ = use_auxiliary_head
lowerCAmelCase__ = auxiliary_loss_weight
lowerCAmelCase__ = auxiliary_in_channels
lowerCAmelCase__ = auxiliary_channels
lowerCAmelCase__ = auxiliary_num_convs
lowerCAmelCase__ = auxiliary_concat_input
lowerCAmelCase__ = loss_ignore_index
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ = copy.deepcopy(self.__dict__ )
lowerCAmelCase__ = self.backbone_config.to_dict()
lowerCAmelCase__ = self.__class__.model_type
return output | 90 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : Any = "xlnet"
a__ : Dict = ["mems"]
a__ : List[str] = {
"n_token": "vocab_size", # Backward compatibility
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , __lowerCAmelCase : Dict=3_20_00 , __lowerCAmelCase : List[str]=10_24 , __lowerCAmelCase : Dict=24 , __lowerCAmelCase : Optional[Any]=16 , __lowerCAmelCase : Dict=40_96 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]="bi" , __lowerCAmelCase : Dict=0.02 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Optional[Any]=0.1 , __lowerCAmelCase : Optional[Any]=5_12 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Any="last" , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Tuple="tanh" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : str=5 , __lowerCAmelCase : str=5 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Optional[int]=2 , **__lowerCAmelCase : List[str] , ) -> Tuple:
_A = vocab_size
_A = d_model
_A = n_layer
_A = n_head
if d_model % n_head != 0:
raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
_A = d_model // n_head
_A = ff_activation
_A = d_inner
_A = untie_r
_A = attn_type
_A = initializer_range
_A = layer_norm_eps
_A = dropout
_A = mem_len
_A = reuse_len
_A = bi_data
_A = clamp_len
_A = same_length
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_last_dropout
_A = start_n_top
_A = end_n_top
_A = bos_token_id
_A = pad_token_id
_A = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
''' instead.''' , __lowerCAmelCase , )
_A = kwargs['''use_cache''']
_A = use_mems_eval
_A = use_mems_train
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def snake_case_ ( self : Optional[Any] ) -> Union[str, Any]:
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 snake_case_ ( self : Tuple , __lowerCAmelCase : Optional[Any] ) -> Dict:
# 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.''' )
| 2 | 0 |
"""simple docstring"""
from maths.prime_check import is_prime
def _snake_case ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
A = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case__ )
if is_prime(snake_case__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod() | 91 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :bytes ) -> str:
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(_snake_case ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_snake_case ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_snake_case ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OPTForCausalLM""",
"""OPTModel""",
"""OPTPreTrainedModel""",
"""OPTForSequenceClassification""",
"""OPTForQuestionAnswering""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxOPTForCausalLM""",
"""FlaxOPTModel""",
"""FlaxOPTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 |
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> bool:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(_snake_case ) == 1:
return True
_A = series[1] - series[0]
for index in range(len(_snake_case ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> float:
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_snake_case ) == 0:
raise ValueError('''Input list must be a non empty list''' )
_A = 0
for val in series:
answer += val
return answer / len(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _lowerCAmelCase :
"""simple docstring"""
@staticmethod
def snake_case ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
__magic_name__ :int = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = pipeline(
'zero-shot-object-detection' , model='hf-internal-testing/tiny-random-owlvit-object-detection' )
lowerCAmelCase__ :int = [
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'candidate_labels': ['cat', 'remote', 'couch'],
}
]
return object_detector, examples
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = object_detector(examples[0] , threshold=0.0 )
lowerCAmelCase__ :Optional[Any] = len(__UpperCAmelCase )
self.assertGreater(__UpperCAmelCase , 0 )
self.assertEqual(
__UpperCAmelCase , [
{
'score': ANY(__UpperCAmelCase ),
'label': ANY(__UpperCAmelCase ),
'box': {'xmin': ANY(__UpperCAmelCase ), 'ymin': ANY(__UpperCAmelCase ), 'xmax': ANY(__UpperCAmelCase ), 'ymax': ANY(__UpperCAmelCase )},
}
for i in range(__UpperCAmelCase )
] , )
@require_tf
@unittest.skip('Zero Shot Object Detection not implemented in TF' )
def snake_case ( self ):
'''simple docstring'''
pass
@require_torch
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = pipeline(
'zero-shot-object-detection' , model='hf-internal-testing/tiny-random-owlvit-object-detection' )
lowerCAmelCase__ :str = object_detector(
'./tests/fixtures/tests_samples/COCO/000000039769.png' , candidate_labels=['cat', 'remote', 'couch'] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
{'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
{'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
] , )
lowerCAmelCase__ :Any = object_detector(
[
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'candidate_labels': ['cat', 'remote', 'couch'],
}
] , threshold=0.64 , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
{'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
{'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
{'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
{'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
]
] , )
@require_torch
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = pipeline('zero-shot-object-detection' )
lowerCAmelCase__ :str = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg' , candidate_labels=['cat', 'remote', 'couch'] , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
{'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
{'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
{'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
{'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
] , )
lowerCAmelCase__ :Any = object_detector(
[
{
'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
'candidate_labels': ['cat', 'remote', 'couch'],
},
{
'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
'candidate_labels': ['cat', 'remote', 'couch'],
},
] , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
[
{'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
{'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
{'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
{'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
{'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
],
[
{'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
{'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
{'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
{'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
{'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
],
] , )
@require_tf
@unittest.skip('Zero Shot Object Detection not implemented in TF' )
def snake_case ( self ):
'''simple docstring'''
pass
@require_torch
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :str = 0.2
lowerCAmelCase__ :Optional[Any] = pipeline('zero-shot-object-detection' )
lowerCAmelCase__ :Optional[Any] = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg' , candidate_labels=['cat', 'remote', 'couch'] , threshold=__UpperCAmelCase , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
{'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
{'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
] , )
@require_torch
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = 2
lowerCAmelCase__ :Tuple = pipeline('zero-shot-object-detection' )
lowerCAmelCase__ :str = object_detector(
'http://images.cocodataset.org/val2017/000000039769.jpg' , candidate_labels=['cat', 'remote', 'couch'] , top_k=__UpperCAmelCase , )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
{'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
] , )
| 93 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(_snake_case , _snake_case ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
raise ValueError('''number of qubits must be > 0.''' )
if math.floor(_snake_case ) != number_of_qubits:
raise ValueError('''number of qubits must be exact integer.''' )
if number_of_qubits > 10:
raise ValueError('''number of qubits too large to simulate(>10).''' )
_A = QuantumRegister(_snake_case , '''qr''' )
_A = ClassicalRegister(_snake_case , '''cr''' )
_A = QuantumCircuit(_snake_case , _snake_case )
_A = number_of_qubits
for i in range(_snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _snake_case , _snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_snake_case , _snake_case )
# simulate with 10000 shots
_A = Aer.get_backend('''qasm_simulator''' )
_A = execute(_snake_case , _snake_case , shots=10_000 )
return job.result().get_counts(_snake_case )
if __name__ == "__main__":
print(
f'Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'
)
| 2 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = None
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : Tuple=1 , UpperCAmelCase : List[str]=0 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : List[str]=512 , UpperCAmelCase : Optional[int]="cls" , UpperCAmelCase : Tuple=False , UpperCAmelCase : Union[str, Any]=True , **UpperCAmelCase : str , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
lowercase : Union[str, Any] =project_dim
lowercase : str =pooler_fn
lowercase : Any =learn_encoder
lowercase : Any =use_attention_mask
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = [r'''pooler''', r'''logit_scale''']
UpperCamelCase_ = [r'''position_ids''', r'''predictions.decoder.bias''']
UpperCamelCase_ = '''roberta'''
UpperCamelCase_ = RobertaSeriesConfig
def __init__( self : Dict , UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
super().__init__(UpperCAmelCase )
lowercase : Dict =XLMRobertaModel(UpperCAmelCase )
lowercase : str =nn.Linear(config.hidden_size , config.project_dim )
lowercase : Dict =getattr(UpperCAmelCase , '''has_pre_transformation''' , UpperCAmelCase )
if self.has_pre_transformation:
lowercase : Union[str, Any] =nn.Linear(config.hidden_size , config.project_dim )
lowercase : Any =nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps )
self.post_init()
def A__ ( self : Union[str, Any] , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[torch.Tensor] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , ) -> Union[str, Any]:
'''simple docstring'''
lowercase : int =return_dict if return_dict is not None else self.config.use_return_dict
lowercase : int =self.base_model(
input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , position_ids=UpperCAmelCase , head_mask=UpperCAmelCase , inputs_embeds=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , output_attentions=UpperCAmelCase , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=UpperCAmelCase , )
if self.has_pre_transformation:
lowercase : Union[str, Any] =outputs['''hidden_states'''][-2]
lowercase : Optional[int] =self.pre_LN(UpperCAmelCase )
lowercase : Dict =self.transformation_pre(UpperCAmelCase )
return TransformationModelOutput(
projection_state=UpperCAmelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
else:
lowercase : Dict =self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=UpperCAmelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 94 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""post_extract_proj""": """feature_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_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :str , _snake_case :Any , _snake_case :int , _snake_case :List[Any] ) -> Optional[int]:
for attribute in key.split('''.''' ):
_A = getattr(_snake_case , _snake_case )
if weight_type is not None:
_A = getattr(_snake_case , _snake_case ).shape
else:
_A = 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":
_A = value
elif weight_type == "weight_g":
_A = value
elif weight_type == "weight_v":
_A = value
elif weight_type == "bias":
_A = value
else:
_A = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Union[str, Any] , _snake_case :Any , _snake_case :int ) -> Any:
_A = []
_A = fairseq_model.state_dict()
_A = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_A = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == '''group''' , )
_A = True
else:
for key, mapped_key in MAPPING.items():
_A = '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_A = True
if "*" in mapped_key:
_A = name.split(_snake_case )[0].split('''.''' )[-2]
_A = mapped_key.replace('''*''' , _snake_case )
if "weight_g" in name:
_A = '''weight_g'''
elif "weight_v" in name:
_A = '''weight_v'''
elif "weight" in name:
_A = '''weight'''
elif "bias" in name:
_A = '''bias'''
else:
_A = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :List[str] , _snake_case :List[str] , _snake_case :Optional[int] , _snake_case :List[Any] ) -> Any:
_A = full_name.split('''conv_layers.''' )[-1]
_A = name.split('''.''' )
_A = int(items[0] )
_A = 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.'''
)
_A = 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.'''
)
_A = 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."
)
_A = 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.'''
)
_A = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
def SCREAMING_SNAKE_CASE_ ( _snake_case :str , _snake_case :Dict ) -> Tuple:
_A = SEWConfig()
if is_finetuned:
_A = model.wav_encoder.wav_model.cfg
else:
_A = model.cfg
_A = fs_config.conv_bias
_A = eval(fs_config.conv_feature_layers )
_A = [x[0] for x in conv_layers]
_A = [x[1] for x in conv_layers]
_A = [x[2] for x in conv_layers]
_A = '''gelu'''
_A = '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
_A = 0.0
_A = fs_config.activation_fn.name
_A = fs_config.encoder_embed_dim
_A = 0.02
_A = fs_config.encoder_ffn_embed_dim
_A = 1E-5
_A = fs_config.encoder_layerdrop
_A = fs_config.encoder_attention_heads
_A = fs_config.conv_pos_groups
_A = fs_config.conv_pos
_A = len(_snake_case )
_A = fs_config.encoder_layers
_A = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_A = model.cfg
_A = fs_config.final_dropout
_A = fs_config.layerdrop
_A = fs_config.activation_dropout
_A = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_A = fs_config.attention_dropout
_A = fs_config.dropout_input
_A = fs_config.dropout
_A = fs_config.mask_channel_length
_A = fs_config.mask_channel_prob
_A = fs_config.mask_length
_A = fs_config.mask_prob
_A = '''Wav2Vec2FeatureExtractor'''
_A = '''Wav2Vec2CTCTokenizer'''
return config
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Optional[int] , _snake_case :Union[str, Any] , _snake_case :Optional[Any]=None , _snake_case :Optional[int]=None , _snake_case :Dict=True ) -> List[Any]:
if is_finetuned:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
_A , _A , _A = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_A = SEWConfig.from_pretrained(_snake_case )
else:
_A = convert_config(model[0] , _snake_case )
_A = model[0].eval()
_A = True if config.feat_extract_norm == '''layer''' else False
_A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
if is_finetuned:
if dict_path:
_A = Dictionary.load(_snake_case )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.pad_index
_A = target_dict.bos_index
_A = target_dict.eos_index
_A = len(target_dict.symbols )
_A = os.path.join(_snake_case , '''vocab.json''' )
if not os.path.isdir(_snake_case ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
_A = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_snake_case , )
_A = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
_A = SEWForCTC(_snake_case )
else:
_A = SEWModel(_snake_case )
feature_extractor.save_pretrained(_snake_case )
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_model.save_pretrained(_snake_case )
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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase_ = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 2 | 0 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ : Union[str, Any] = old_name
if "patch_embed" in old_name:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = old_name.split("." )
if layer == "0":
UpperCAmelCase_ : List[Any] = old_name.replace("0" ,"convolution1" )
elif layer == "1":
UpperCAmelCase_ : List[str] = old_name.replace("1" ,"batchnorm_before" )
elif layer == "3":
UpperCAmelCase_ : Union[str, Any] = old_name.replace("3" ,"convolution2" )
else:
UpperCAmelCase_ : Union[str, Any] = old_name.replace("4" ,"batchnorm_after" )
if "network" in old_name and re.search(r"\d\.\d" ,A__ ):
UpperCAmelCase_ : int = r"\b\d{2}\b"
if bool(re.search(A__ ,A__ ) ):
UpperCAmelCase_ : List[Any] = re.search(r"\d\.\d\d." ,A__ ).group()
else:
UpperCAmelCase_ : Tuple = re.search(r"\d\.\d." ,A__ ).group()
if int(match[0] ) < 6:
UpperCAmelCase_ : List[Any] = old_name.replace(A__ ,"" )
UpperCAmelCase_ : str = trimmed_name.replace("network" ,match[0] + ".meta4D_layers.blocks." + match[2:-1] )
UpperCAmelCase_ : List[Any] = "intermediate_stages." + trimmed_name
else:
UpperCAmelCase_ : Dict = old_name.replace(A__ ,"" )
if int(match[2] ) < num_meta4D_last_stage:
UpperCAmelCase_ : List[Any] = trimmed_name.replace("network" ,"meta4D_layers.blocks." + match[2] )
else:
UpperCAmelCase_ : Dict = str(int(match[2] ) - num_meta4D_last_stage )
UpperCAmelCase_ : List[Any] = trimmed_name.replace("network" ,"meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
UpperCAmelCase_ : Tuple = trimmed_name.replace("norm1" ,"layernorm1" )
elif "norm2" in old_name:
UpperCAmelCase_ : Dict = trimmed_name.replace("norm2" ,"layernorm2" )
elif "fc1" in old_name:
UpperCAmelCase_ : Tuple = trimmed_name.replace("fc1" ,"linear_in" )
elif "fc2" in old_name:
UpperCAmelCase_ : str = trimmed_name.replace("fc2" ,"linear_out" )
UpperCAmelCase_ : str = "last_stage." + trimmed_name
elif "network" in old_name and re.search(r".\d." ,A__ ):
UpperCAmelCase_ : Any = old_name.replace("network" ,"intermediate_stages" )
if "fc" in new_name:
UpperCAmelCase_ : Dict = new_name.replace("fc" ,"convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
UpperCAmelCase_ : Optional[Any] = new_name.replace("norm1" ,"batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
UpperCAmelCase_ : List[str] = new_name.replace("norm2" ,"batchnorm_after" )
if "proj" in new_name:
UpperCAmelCase_ : Dict = new_name.replace("proj" ,"projection" )
if "dist_head" in new_name:
UpperCAmelCase_ : Tuple = new_name.replace("dist_head" ,"distillation_classifier" )
elif "head" in new_name:
UpperCAmelCase_ : Any = new_name.replace("head" ,"classifier" )
elif "patch_embed" in new_name:
UpperCAmelCase_ : int = "efficientformer." + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
UpperCAmelCase_ : Tuple = new_name.replace("norm" ,"layernorm" )
UpperCAmelCase_ : Optional[int] = "efficientformer." + new_name
else:
UpperCAmelCase_ : Any = "efficientformer.encoder." + new_name
return new_name
def snake_case ( A__ ,A__ ):
for key in checkpoint.copy().keys():
UpperCAmelCase_ : int = checkpoint.pop(A__ )
UpperCAmelCase_ : Tuple = val
return checkpoint
def snake_case ( ):
UpperCAmelCase_ : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ : int = Image.open(requests.get(A__ ,stream=A__ ).raw )
return image
def snake_case ( A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ : List[Any] = torch.load(A__ ,map_location="cpu" )["model"]
UpperCAmelCase_ : Any = EfficientFormerConfig.from_json_file(A__ )
UpperCAmelCase_ : Optional[Any] = EfficientFormerForImageClassificationWithTeacher(A__ )
UpperCAmelCase_ : Optional[int] = "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
UpperCAmelCase_ : Optional[Any] = config.depths[-1] - config.num_metaad_blocks + 1
UpperCAmelCase_ : List[str] = convert_torch_checkpoint(A__ ,A__ )
model.load_state_dict(A__ )
model.eval()
UpperCAmelCase_ : Any = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
# prepare image
UpperCAmelCase_ : Tuple = prepare_img()
UpperCAmelCase_ : Tuple = 2_56
UpperCAmelCase_ : Any = 2_24
UpperCAmelCase_ : str = EfficientFormerImageProcessor(
size={"shortest_edge": image_size} ,crop_size={"height": crop_size, "width": crop_size} ,resample=pillow_resamplings["bicubic"] ,)
UpperCAmelCase_ : List[str] = processor(images=A__ ,return_tensors="pt" ).pixel_values
# original processing pipeline
UpperCAmelCase_ : List[Any] = Compose(
[
Resize(A__ ,interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(A__ ),
ToTensor(),
Normalize(A__ ,A__ ),
] )
UpperCAmelCase_ : str = image_transforms(A__ ).unsqueeze(0 )
assert torch.allclose(A__ ,A__ )
UpperCAmelCase_ : Optional[Any] = model(A__ )
UpperCAmelCase_ : int = outputs.logits
UpperCAmelCase_ : Tuple = (1, 10_00)
if "l1" in model_name:
UpperCAmelCase_ : Dict = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] ,A__ ,atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
UpperCAmelCase_ : Optional[Any] = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] ,A__ ,atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
UpperCAmelCase_ : int = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )
# Save Checkpoints
Path(A__ ).mkdir(exist_ok=A__ )
model.save_pretrained(A__ )
print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
processor.save_pretrained(A__ )
print(F"""Processor successfuly saved at {pytorch_dump_path}""" )
if push_to_hub:
print("Pushing model to the hub..." )
model.push_to_hub(
repo_id=F"""Bearnardd/{pytorch_dump_path}""" ,commit_message="Add model" ,use_temp_dir=A__ ,)
processor.push_to_hub(
repo_id=F"""Bearnardd/{pytorch_dump_path}""" ,commit_message="Add image processor" ,use_temp_dir=A__ ,)
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''',
default=None,
type=str,
required=True,
help='''Path to EfficientFormer pytorch checkpoint.''',
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for EfficientFormer model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
parser.set_defaults(push_to_hub=True)
lowerCamelCase_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 95 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Any ) -> Any:
pass
@is_pipeline_test
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@require_torch
def snake_case_ ( self : Tuple ) -> Tuple:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowerCAmelCase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : int ) -> Optional[int]:
_A = pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
_A = image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
{'''score''': 0.333, '''label''': ANY(__lowerCAmelCase )},
],
] , )
@slow
@require_torch
def snake_case_ ( self : Optional[int] ) -> int:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def snake_case_ ( self : Optional[int] ) -> Dict:
_A = pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_A = image_classifier(__lowerCAmelCase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
_A = image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 2 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__lowerCamelCase = 'pt'
elif is_tf_available():
__lowerCamelCase = 'tf'
else:
__lowerCamelCase = 'jax'
class __A ( SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
UpperCAmelCase__ = ByTaTokenizer
UpperCAmelCase__ = False
def lowerCamelCase__ ( self : Optional[Any] ) -> List[Any]:
super().setUp()
__magic_name__: Union[str, Any] = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase__ ( self : str ) -> str:
return ByTaTokenizer.from_pretrained("""google/byt5-small""" )
def lowerCamelCase__ ( self : str , **__snake_case : List[str] ) -> ByTaTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__snake_case )
def lowerCamelCase__ ( self : Optional[int] , __snake_case : List[Any] , __snake_case : List[str]=False , __snake_case : str=2_0 , __snake_case : Dict=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for ByT5 because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
__magic_name__: List[Any] = []
for i in range(len(__snake_case ) ):
try:
__magic_name__: Any = tokenizer.decode([i] , clean_up_tokenization_spaces=__snake_case )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
__magic_name__: List[Any] = list(filter(lambda __snake_case : re.match(R"""^[ a-zA-Z]+$""" , t[1] ) , __snake_case ) )
__magic_name__: Union[str, Any] = list(filter(lambda __snake_case : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=__snake_case ) , __snake_case ) )
if max_length is not None and len(__snake_case ) > max_length:
__magic_name__: Dict = toks[:max_length]
if min_length is not None and len(__snake_case ) < min_length and len(__snake_case ) > 0:
while len(__snake_case ) < min_length:
__magic_name__: int = toks + toks
# toks_str = [t[1] for t in toks]
__magic_name__: str = [t[0] for t in toks]
# Ensure consistency
__magic_name__: int = tokenizer.decode(__snake_case , clean_up_tokenization_spaces=__snake_case )
if " " not in output_txt and len(__snake_case ) > 1:
__magic_name__: List[Any] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__snake_case )
+ """ """
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__snake_case )
)
if with_prefix_space:
__magic_name__: Any = """ """ + output_txt
__magic_name__: List[Any] = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
return output_txt, output_ids
def lowerCamelCase__ ( self : Any ) -> Optional[Any]:
__magic_name__: int = self.ta_base_tokenizer
__magic_name__: List[Any] = tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""] )
__magic_name__: List[Any] = tokenizer(["""hi""", """I went to the gym""", """"""] )
self.assertListEqual(batch_with_eos_added["""input_ids"""] , batch_without_eos_added["""input_ids"""] )
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
__magic_name__: Any = self.ta_base_tokenizer
__magic_name__: str = """Unicode €."""
__magic_name__: Any = tokenizer(__snake_case )
__magic_name__: Any = [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
self.assertEqual(encoded["""input_ids"""] , __snake_case )
# decoding
__magic_name__: Optional[Any] = tokenizer.decode(__snake_case )
self.assertEqual(__snake_case , """Unicode €.</s>""" )
__magic_name__: str = tokenizer("""e è é ê ë""" )
__magic_name__: List[str] = [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
self.assertEqual(encoded["""input_ids"""] , __snake_case )
# decoding
__magic_name__: Optional[int] = tokenizer.decode(__snake_case )
self.assertEqual(__snake_case , """e è é ê ë</s>""" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) ) , """e è é ê ë</s>""" )
def lowerCamelCase__ ( self : str ) -> List[Any]:
__magic_name__: Optional[int] = self.ta_base_tokenizer
__magic_name__: Any = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
# fmt: off
__magic_name__: Any = [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
# fmt: on
__magic_name__: Dict = tokenizer(__snake_case , padding=__snake_case , return_tensors=__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
if FRAMEWORK != "jax":
__magic_name__: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
__magic_name__: List[str] = list(batch.input_ids.tolist()[0] )
self.assertListEqual(__snake_case , __snake_case )
self.assertEqual((2, 3_7) , batch.input_ids.shape )
self.assertEqual((2, 3_7) , batch.attention_mask.shape )
def lowerCamelCase__ ( self : List[str] ) -> int:
__magic_name__: Tuple = self.ta_base_tokenizer
__magic_name__: Union[str, Any] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
__magic_name__: Tuple = tokenizer(__snake_case , padding=__snake_case , return_tensors=__snake_case )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("""input_ids""" , __snake_case )
self.assertIn("""attention_mask""" , __snake_case )
self.assertNotIn("""decoder_input_ids""" , __snake_case )
self.assertNotIn("""decoder_attention_mask""" , __snake_case )
def lowerCamelCase__ ( self : List[str] ) -> List[Any]:
__magic_name__: Optional[int] = self.ta_base_tokenizer
__magic_name__: Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
__magic_name__: List[str] = tokenizer(
text_target=__snake_case , max_length=3_2 , padding="""max_length""" , truncation=__snake_case , return_tensors=__snake_case )
self.assertEqual(3_2 , targets["""input_ids"""].shape[1] )
def lowerCamelCase__ ( self : List[str] ) -> int:
__magic_name__: str = self.ta_base_tokenizer
__magic_name__: Union[str, Any] = ["""A long paragraph for summarization. </s>"""]
__magic_name__: Any = ["""Summary of the text. </s>"""]
# fmt: off
__magic_name__: int = [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
__magic_name__: List[str] = [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
# fmt: on
__magic_name__: Optional[int] = tokenizer(__snake_case , text_target=__snake_case )
self.assertEqual(__snake_case , batch["""input_ids"""][0] )
self.assertEqual(__snake_case , batch["""labels"""][0] )
def lowerCamelCase__ ( self : Dict ) -> str:
# safety check on max_len default value so we are sure the test works
__magic_name__: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
self.assertNotEqual(tokenizer.model_max_length , 4_2 )
# Now let's start the test
__magic_name__: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
__magic_name__: Optional[Any] = tempfile.mkdtemp()
__magic_name__: str = """ He is very happy, UNwant\u00E9d,running"""
__magic_name__: Dict = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
tokenizer.save_pretrained(__snake_case )
__magic_name__: int = tokenizer.__class__.from_pretrained(__snake_case )
__magic_name__: Any = after_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
shutil.rmtree(__snake_case )
__magic_name__: Optional[int] = self.get_tokenizers(model_max_length=4_2 )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
__magic_name__: Optional[int] = tempfile.mkdtemp()
__magic_name__: str = """ He is very happy, UNwant\u00E9d,running"""
tokenizer.add_tokens(["""bim""", """bambam"""] )
__magic_name__: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append("""new_additional_special_token""" )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
__magic_name__: List[Any] = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
tokenizer.save_pretrained(__snake_case )
__magic_name__: List[str] = tokenizer.__class__.from_pretrained(__snake_case )
__magic_name__: Any = after_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
self.assertIn("""new_additional_special_token""" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 4_2 )
__magic_name__: Optional[int] = tokenizer.__class__.from_pretrained(__snake_case , model_max_length=4_3 )
self.assertEqual(tokenizer.model_max_length , 4_3 )
shutil.rmtree(__snake_case )
def lowerCamelCase__ ( self : str ) -> int:
__magic_name__: Optional[int] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__snake_case )
with open(os.path.join(__snake_case , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
__magic_name__: Any = json.load(__snake_case )
with open(os.path.join(__snake_case , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
__magic_name__: Any = json.load(__snake_case )
__magic_name__: Union[str, Any] = [F'<extra_id_{i}>' for i in range(1_2_5 )]
__magic_name__: List[str] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
__magic_name__: List[Any] = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
with open(os.path.join(__snake_case , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__snake_case , __snake_case )
with open(os.path.join(__snake_case , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__snake_case , __snake_case )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__magic_name__: Dict = tokenizer_class.from_pretrained(
__snake_case , )
self.assertIn(
"""an_additional_special_token""" , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
["""an_additional_special_token"""] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__magic_name__: Optional[int] = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" , lstrip=__snake_case )]
__magic_name__: List[Any] = tokenizer_class.from_pretrained(
__snake_case , additional_special_tokens=__snake_case , )
self.assertIn("""a_new_additional_special_token""" , tokenizer.additional_special_tokens )
self.assertEqual(
["""a_new_additional_special_token"""] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) ) , )
def lowerCamelCase__ ( self : Dict ) -> Union[str, Any]:
__magic_name__: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__snake_case )
__magic_name__: Dict = tokenizer_class.from_pretrained(__snake_case )
self.assertTrue(tokenizer.decode([2_5_5] ) == """""" )
def lowerCamelCase__ ( self : str ) -> str:
pass
def lowerCamelCase__ ( self : str ) -> Tuple:
pass
def lowerCamelCase__ ( self : Dict ) -> Optional[Any]:
pass
def lowerCamelCase__ ( self : Optional[Any] ) -> Optional[Any]:
pass
def lowerCamelCase__ ( self : List[Any] ) -> List[str]:
# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
# and special added tokens as tokens
__magic_name__: Tuple = self.get_tokenizers(fast=__snake_case , do_lower_case=__snake_case )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__: Any = ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
__magic_name__: Optional[Any] = tokenizer.convert_tokens_to_string(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
def lowerCamelCase__ ( self : List[Any] ) -> Any:
__magic_name__: Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__: str = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
__magic_name__: List[Any] = 0
__magic_name__: Optional[int] = tokenizer.convert_ids_to_tokens(
__snake_case , skip_special_tokens=__snake_case )
for attr in attributes_list:
setattr(__snake_case , attr + """_id""" , __snake_case )
self.assertEqual(getattr(__snake_case , __snake_case ) , __snake_case )
self.assertEqual(getattr(__snake_case , attr + """_id""" ) , __snake_case )
setattr(__snake_case , attr + """_id""" , __snake_case )
self.assertEqual(getattr(__snake_case , __snake_case ) , __snake_case )
self.assertEqual(getattr(__snake_case , attr + """_id""" ) , __snake_case )
setattr(__snake_case , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens_ids""" ) , [] )
setattr(__snake_case , """additional_special_tokens_ids""" , [token_id_to_test_setters] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens""" ) , [token_to_test_setters] )
self.assertListEqual(getattr(__snake_case , """additional_special_tokens_ids""" ) , [token_id_to_test_setters] )
| 96 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def snake_case_ ( self : Tuple ) -> Optional[int]:
_A = tempfile.mkdtemp()
_A = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_A = 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] ) )
_A = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
'''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_A = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Dict , **__lowerCAmelCase : int ) -> Optional[int]:
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : str , **__lowerCAmelCase : Optional[Any] ) -> Tuple:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Tuple , **__lowerCAmelCase : str ) -> Union[str, Any]:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def snake_case_ ( self : int ) -> Optional[Any]:
_A = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_A = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def snake_case_ ( self : Dict ) -> List[str]:
_A = self.get_tokenizer()
_A = self.get_rust_tokenizer()
_A = self.get_image_processor()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase )
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
_A = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , __lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , __lowerCAmelCase )
def snake_case_ ( self : List[Any] ) -> List[str]:
_A = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_A = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_A = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
_A = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = self.prepare_image_inputs()
_A = image_processor(__lowerCAmelCase , return_tensors='''np''' )
_A = processor(images=__lowerCAmelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case_ ( self : Union[str, Any] ) -> Dict:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = processor(text=__lowerCAmelCase )
_A = tokenizer(__lowerCAmelCase , padding='''max_length''' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case_ ( self : List[str] ) -> Any:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowerCAmelCase ):
processor()
def snake_case_ ( self : Optional[Any] ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_A = processor.batch_decode(__lowerCAmelCase )
_A = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : str ) -> str:
_A = self.get_image_processor()
_A = self.get_tokenizer()
_A = AlignProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
_A = '''lower newer'''
_A = self.prepare_image_inputs()
_A = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 2 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> Optional[int]:
lowercase_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''tf_padding''' ) )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''depth_multiplier''' ) )
class lowercase__:
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int]=1_3 , SCREAMING_SNAKE_CASE_ : Dict=3 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_2 , SCREAMING_SNAKE_CASE_ : Any=0.25 , SCREAMING_SNAKE_CASE_ : Dict=8 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=8 , SCREAMING_SNAKE_CASE_ : List[str]=6 , SCREAMING_SNAKE_CASE_ : str=3_2 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : str="relu6" , SCREAMING_SNAKE_CASE_ : int=1_2_8_0 , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[int]=1_0 , SCREAMING_SNAKE_CASE_ : Tuple=None , ) -> Union[str, Any]:
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = num_channels
lowercase_ = image_size
lowercase_ = depth_multiplier
lowercase_ = depth_divisible_by
lowercase_ = min_depth
lowercase_ = expand_ratio
lowercase_ = tf_padding
lowercase_ = output_stride
lowercase_ = first_layer_is_expansion
lowercase_ = finegrained_output
lowercase_ = hidden_act
lowercase_ = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
lowercase_ = classifier_dropout_prob
lowercase_ = use_labels
lowercase_ = is_training
lowercase_ = num_labels
lowercase_ = initializer_range
lowercase_ = scope
def _lowercase ( self : int ) -> str:
lowercase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ = None
lowercase_ = None
if self.use_labels:
lowercase_ = ids_tensor([self.batch_size] , self.num_labels )
lowercase_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowercase ( self : int ) -> Tuple:
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ) -> str:
lowercase_ = MobileNetVaModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowercase_ = model(SCREAMING_SNAKE_CASE_ )
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,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def _lowercase ( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Dict ) -> List[str]:
lowercase_ = self.num_labels
lowercase_ = MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowercase_ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Dict:
lowercase_ = self.num_labels
lowercase_ = MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowercase_ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase_ = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
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 _lowercase ( self : Optional[Any] ) -> Dict:
lowercase_ = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ , lowercase_ = config_and_inputs
lowercase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :List[str] = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
a :Optional[Any] = (
{
'feature-extraction': MobileNetVaModel,
'image-classification': MobileNetVaForImageClassification,
'image-segmentation': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
a :Optional[Any] = False
a :List[str] = False
a :List[Any] = False
a :Any = False
def _lowercase ( self : int ) -> str:
lowercase_ = MobileNetVaModelTester(self )
lowercase_ = MobileNetVaConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[int] ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''' )
def _lowercase ( self : str ) -> str:
pass
@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''' )
def _lowercase ( self : List[Any] ) -> Any:
pass
@unittest.skip(reason='''MobileNetV2 does not output attentions''' )
def _lowercase ( self : str ) -> Optional[Any]:
pass
def _lowercase ( self : Tuple ) -> int:
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(SCREAMING_SNAKE_CASE_ )
lowercase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int ) -> List[Any]:
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : List[str] ) -> List[Any]:
def check_hidden_states_output(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ):
lowercase_ = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
lowercase_ = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowercase_ = outputs.hidden_states
lowercase_ = 1_6
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : str ) -> List[Any]:
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Tuple ) -> Optional[Any]:
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_ )
@slow
def _lowercase ( self : Tuple ) -> Optional[int]:
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = MobileNetVaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a ( ):
'''simple docstring'''
lowercase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase__( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowercase ( self : Union[str, Any] ) -> List[Any]:
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''' ) if is_vision_available() else None
)
@slow
def _lowercase ( self : Dict ) -> List[str]:
lowercase_ = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''' ).to(SCREAMING_SNAKE_CASE_ )
lowercase_ = self.default_image_processor
lowercase_ = prepare_img()
lowercase_ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
lowercase_ = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
lowercase_ = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
lowercase_ = torch.tensor([0.24_45, -1.19_93, 0.19_05] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
@slow
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
lowercase_ = MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
lowercase_ = model.to(SCREAMING_SNAKE_CASE_ )
lowercase_ = MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''' )
lowercase_ = prepare_img()
lowercase_ = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
lowercase_ = model(**SCREAMING_SNAKE_CASE_ )
lowercase_ = outputs.logits
# verify the logits
lowercase_ = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
lowercase_ = torch.tensor(
[
[[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
[[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
[[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
] , device=SCREAMING_SNAKE_CASE_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 97 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = "openai-gpt"
a__ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Union[str, Any] , __lowerCAmelCase : int=4_04_78 , __lowerCAmelCase : Tuple=5_12 , __lowerCAmelCase : str=7_68 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=1E-5 , __lowerCAmelCase : Optional[int]=0.02 , __lowerCAmelCase : Optional[Any]="cls_index" , __lowerCAmelCase : str=True , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=0.1 , **__lowerCAmelCase : Tuple , ) -> Optional[Any]:
_A = vocab_size
_A = n_positions
_A = n_embd
_A = n_layer
_A = n_head
_A = afn
_A = resid_pdrop
_A = embd_pdrop
_A = attn_pdrop
_A = layer_norm_epsilon
_A = initializer_range
_A = summary_type
_A = summary_use_proj
_A = summary_activation
_A = summary_first_dropout
_A = summary_proj_to_labels
super().__init__(**__lowerCAmelCase )
| 2 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowercase__ : Union[str, Any] = 0
lowercase__ : Optional[Any] = [
[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],
]
lowercase__ : Optional[int] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
lowercase__ : Any = tuple[int, int]
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Node | None , ) -> None:
'''simple docstring'''
_UpperCamelCase = pos_x
_UpperCamelCase = pos_y
_UpperCamelCase = (pos_y, pos_x)
_UpperCamelCase = goal_x
_UpperCamelCase = goal_y
_UpperCamelCase = g_cost
_UpperCamelCase = parent
_UpperCamelCase = self.calculate_heuristic()
_UpperCamelCase = self.g_cost + self.h_cost
def snake_case__ ( self : Union[str, Any] ) -> float:
'''simple docstring'''
_UpperCamelCase = self.pos_x - self.goal_x
_UpperCamelCase = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(lowerCAmelCase__ ) + abs(lowerCAmelCase__ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : str , lowerCAmelCase__ : Node ) -> bool:
'''simple docstring'''
return self.f_cost < other.f_cost
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : TPosition , lowerCAmelCase__ : TPosition ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowerCAmelCase__ )
_UpperCamelCase = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , lowerCAmelCase__ )
_UpperCamelCase = [self.start]
_UpperCamelCase = []
_UpperCamelCase = False
def snake_case__ ( self : List[Any] ) -> list[TPosition]:
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
_UpperCamelCase = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(lowerCAmelCase__ )
self.closed_nodes.append(lowerCAmelCase__ )
_UpperCamelCase = self.get_successors(lowerCAmelCase__ )
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(lowerCAmelCase__ )
else:
# retrieve the best current path
_UpperCamelCase = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowerCAmelCase__ )
else:
self.open_nodes.append(lowerCAmelCase__ )
return [self.start.pos]
def snake_case__ ( self : Optional[Any] , lowerCAmelCase__ : Node ) -> list[Node]:
'''simple docstring'''
_UpperCamelCase = []
for action in delta:
_UpperCamelCase = parent.pos_x + action[1]
_UpperCamelCase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowerCAmelCase__ , lowerCAmelCase__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowerCAmelCase__ , ) )
return successors
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : Node | None ) -> list[TPosition]:
'''simple docstring'''
_UpperCamelCase = node
_UpperCamelCase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
_UpperCamelCase = current_node.parent
path.reverse()
return path
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : TPosition , lowerCAmelCase__ : TPosition ) -> None:
'''simple docstring'''
_UpperCamelCase = AStar(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = AStar(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase = False
def snake_case__ ( self : int ) -> 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()
_UpperCamelCase = self.fwd_astar.open_nodes.pop(0 )
_UpperCamelCase = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
lowerCAmelCase__ , lowerCAmelCase__ )
self.fwd_astar.closed_nodes.append(lowerCAmelCase__ )
self.bwd_astar.closed_nodes.append(lowerCAmelCase__ )
_UpperCamelCase = current_bwd_node
_UpperCamelCase = current_fwd_node
_UpperCamelCase = {
self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase__ ),
self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase__ ),
}
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(lowerCAmelCase__ )
else:
# retrieve the best current path
_UpperCamelCase = astar.open_nodes.pop(
astar.open_nodes.index(lowerCAmelCase__ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(lowerCAmelCase__ )
else:
astar.open_nodes.append(lowerCAmelCase__ )
return [self.fwd_astar.start.pos]
def snake_case__ ( self : List[str] , lowerCAmelCase__ : Node , lowerCAmelCase__ : Node ) -> list[TPosition]:
'''simple docstring'''
_UpperCamelCase = self.fwd_astar.retrace_path(lowerCAmelCase__ )
_UpperCamelCase = self.bwd_astar.retrace_path(lowerCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
_UpperCamelCase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
lowercase__ : List[Any] = (0, 0)
lowercase__ : Optional[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
lowercase__ : Any = time.time()
lowercase__ : Union[str, Any] = AStar(init, goal)
lowercase__ : Optional[Any] = a_star.search()
lowercase__ : List[Any] = time.time() - start_time
print(F"""AStar execution time = {end_time:f} seconds""")
lowercase__ : Tuple = time.time()
lowercase__ : List[str] = BidirectionalAStar(init, goal)
lowercase__ : List[Any] = time.time() - bd_start_time
print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 98 |
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 DeformableDetrImageProcessor
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : int=30 , __lowerCAmelCase : Dict=4_00 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=1 / 2_55 , __lowerCAmelCase : int=True , ) -> List[str]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_A = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
_A = parent
_A = batch_size
_A = num_channels
_A = min_resolution
_A = max_resolution
_A = do_resize
_A = size
_A = do_normalize
_A = image_mean
_A = image_std
_A = do_rescale
_A = rescale_factor
_A = do_pad
def snake_case_ ( self : Optional[int] ) -> Any:
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 snake_case_ ( self : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str=False ) -> Dict:
if not batched:
_A = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
_A , _A = image.size
else:
_A , _A = image.shape[1], image.shape[2]
if w < h:
_A = int(self.size['''shortest_edge'''] * h / w )
_A = self.size['''shortest_edge''']
elif w > h:
_A = self.size['''shortest_edge''']
_A = int(self.size['''shortest_edge'''] * w / h )
else:
_A = self.size['''shortest_edge''']
_A = self.size['''shortest_edge''']
else:
_A = []
for image in image_inputs:
_A , _A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
_A = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase__ ( _A , unittest.TestCase):
"""simple docstring"""
a__ : Any = DeformableDetrImageProcessor if is_vision_available() else None
def snake_case_ ( self : Optional[int] ) -> Any:
_A = DeformableDetrImageProcessingTester(self )
@property
def snake_case_ ( self : Union[str, Any] ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = 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 , '''do_rescale''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_pad''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def snake_case_ ( self : List[str] ) -> int:
_A = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
_A = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def snake_case_ ( self : Any ) -> Union[str, Any]:
pass
def snake_case_ ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
_A = image_processing(__lowerCAmelCase , 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 snake_case_ ( self : Tuple ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[Any] ) -> int:
# Initialize image_processing
_A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_A = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
_A = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_A = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
_A , _A = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
# prepare image and target
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''image_id''': 3_97_69, '''annotations''': target}
# encode them
_A = DeformableDetrImageProcessor()
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def snake_case_ ( self : List[str] ) -> List[str]:
# prepare image, target and masks_path
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
_A = json.loads(f.read() )
_A = {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}
_A = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
_A = DeformableDetrImageProcessor(format='''coco_panoptic''' )
_A = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
_A = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
_A = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
_A = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
_A = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
_A = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
_A = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
_A = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
_A = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
_A = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
_A = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 2 | 0 |
def a (lowerCAmelCase__ ):
assert column_title.isupper()
__a = 0
__a = len(lowerCAmelCase__ ) - 1
__a = 0
while index >= 0:
__a = (ord(column_title[index] ) - 64) * pow(26 , lowerCAmelCase__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 99 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Dict = BioGptTokenizer
lowerCamelCase__ : List[Any] = False
def lowercase_ ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE__ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
SCREAMING_SNAKE_CASE__ = dict(zip(A_ , range(len(A_ ) ) ) )
SCREAMING_SNAKE_CASE__ = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' ) as fp:
fp.write(json.dumps(A_ ) )
with open(self.merges_file , '''w''' ) as fp:
fp.write('''\n'''.join(A_ ) )
def lowercase_ ( self , A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''lower newer'''
SCREAMING_SNAKE_CASE__ = '''lower newer'''
return input_text, output_text
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = BioGptTokenizer(self.vocab_file , self.merges_file )
SCREAMING_SNAKE_CASE__ = '''lower'''
SCREAMING_SNAKE_CASE__ = ['''low''', '''er</w>''']
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokens + ['''<unk>''']
SCREAMING_SNAKE_CASE__ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ )
@slow
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
SCREAMING_SNAKE_CASE__ = tokenizer.encode('''sequence builders''' , add_special_tokens=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.encode('''multi-sequence build''' , add_special_tokens=A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.build_inputs_with_special_tokens(A_ )
SCREAMING_SNAKE_CASE__ = tokenizer.build_inputs_with_special_tokens(A_ , A_ )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 100 |
import itertools
import math
def SCREAMING_SNAKE_CASE_ ( _snake_case :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
_A = 2
while True:
if is_prime(_snake_case ):
yield num
num += 1
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 10_001 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , _snake_case ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 2 | 0 |
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