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'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Tuple = len(lowerCAmelCase_ )
print("""The following activities are selected:""" )
# The first activity is always selected
_UpperCAmelCase : List[str] = 0
print(lowerCAmelCase_ , end=""",""" )
# Consider rest of the activities
for j in range(lowerCAmelCase_ ):
# 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(lowerCAmelCase_ , end=""",""" )
_UpperCAmelCase : List[str] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Any = [1, 3, 0, 5, 8, 5]
A_ : List[str] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 349 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Union[str, Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(a_ )
return image
@property
def _snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def _snake_case ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def _snake_case ( self ) -> Dict:
torch.manual_seed(0 )
_UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
return CLIPTextModel(a_ )
@property
def _snake_case ( self ) -> Union[str, Any]:
def extract(*a_ ,**a_ ):
class lowercase :
"""simple docstring"""
def __init__( self ) -> Any:
_UpperCAmelCase : str = torch.ones([0] )
def _snake_case ( self ,a_ ) -> Any:
self.pixel_values.to(a_ )
return self
return Out()
return extract
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Union[str, Any] = self.dummy_cond_unet
_UpperCAmelCase : int = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
_UpperCAmelCase : Optional[int] = self.dummy_vae
_UpperCAmelCase : Optional[int] = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : int = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Tuple = self.dummy_cond_unet
_UpperCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : int = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : str = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : List[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=a_ )
assert isinstance(a_ ,a_ )
assert isinstance(pipe.scheduler ,a_ )
assert pipe.safety_checker is None
_UpperCAmelCase : Dict = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained(a_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase : Union[str, Any] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.dummy_cond_unet
_UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : List[str] = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_UpperCAmelCase : str = unet.half()
_UpperCAmelCase : List[str] = vae.half()
_UpperCAmelCase : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : Dict = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : int = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : List[Any] = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCAmelCase : Any = 4_003_660_346
_UpperCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCAmelCase : Optional[Any] = 2_734_971_755
_UpperCAmelCase : Optional[int] = 7
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[int] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCAmelCase : Optional[int] = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Union[str, Any] = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCAmelCase : Dict = 1_044_355_234
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : List[str] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCAmelCase : Tuple = torch.manual_seed(a_ )
_UpperCAmelCase : Dict = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ : Optional[Any] = 1_6
A_ : Tuple = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = 16 , lowerCAmelCase_ = "bert-base-cased" )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(lowerCAmelCase_ )
_UpperCAmelCase : Any = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : Any = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_UpperCAmelCase : int = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCAmelCase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCAmelCase_ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCAmelCase_ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
_UpperCAmelCase : Optional[int] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : int = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
_UpperCAmelCase : Tuple = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Optional[int] = config["""lr"""]
_UpperCAmelCase : List[str] = int(config["""num_epochs"""] )
_UpperCAmelCase : Dict = int(config["""seed"""] )
_UpperCAmelCase : List[str] = int(config["""batch_size"""] )
_UpperCAmelCase : int = args.model_name_or_path
set_seed(lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = get_dataloaders(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_ , return_dict=lowerCAmelCase_ )
# Instantiate optimizer
_UpperCAmelCase : List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_UpperCAmelCase : Tuple = optimizer_cls(params=model.parameters() , lr=lowerCAmelCase_ )
if accelerator.state.deepspeed_plugin is not None:
_UpperCAmelCase : Optional[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
_UpperCAmelCase : Union[str, Any] = 1
_UpperCAmelCase : Union[str, Any] = (len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_UpperCAmelCase : str = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=0 , num_training_steps=lowerCAmelCase_ , )
else:
_UpperCAmelCase : List[Any] = DummyScheduler(lowerCAmelCase_ , total_num_steps=lowerCAmelCase_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# We need to keep track of how many total steps we have iterated over
_UpperCAmelCase : Optional[int] = 0
# We also need to keep track of the stating epoch so files are named properly
_UpperCAmelCase : Optional[Any] = 0
# Now we train the model
_UpperCAmelCase : Optional[Any] = evaluate.load("""glue""" , """mrpc""" )
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = {}
for epoch in range(lowerCAmelCase_ , lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.loss
_UpperCAmelCase : List[str] = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_UpperCAmelCase : Union[str, Any] = 0
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCAmelCase_ ) - 1:
_UpperCAmelCase : List[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_UpperCAmelCase : str = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
_UpperCAmelCase : List[str] = eval_metric["""accuracy"""]
if best_performance < eval_metric["accuracy"]:
_UpperCAmelCase : str = eval_metric["""accuracy"""]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , """all_results.json""" ) , """w""" ) as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Any = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCAmelCase_ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCAmelCase_ , )
parser.add_argument(
"""--output_dir""" , type=lowerCAmelCase_ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--performance_lower_bound""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCAmelCase_ , default=3 , help="""Number of train epochs.""" , )
_UpperCAmelCase : Tuple = parser.parse_args()
_UpperCAmelCase : Any = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 | 1 |
'''simple docstring'''
import random
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = a[left_index]
_UpperCAmelCase : Optional[int] = left_index + 1
for j in range(left_index + 1 , lowerCAmelCase_ ):
if a[j] < pivot:
_UpperCAmelCase ,_UpperCAmelCase : Any = a[i], a[j]
i += 1
_UpperCAmelCase ,_UpperCAmelCase : Any = a[i - 1], a[left_index]
return i - 1
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
if left < right:
_UpperCAmelCase : Tuple = random.randint(lowerCAmelCase_ , right - 1 )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase : Tuple = partition(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
quick_sort_random(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # recursive quicksort to the left of the pivot point
quick_sort_random(
lowerCAmelCase_ , pivot_index + 1 , lowerCAmelCase_ ) # recursive quicksort to the right of the pivot point
def snake_case_ ( )-> Dict:
'''simple docstring'''
_UpperCAmelCase : str = input("""Enter numbers separated by a comma:\n""" ).strip()
_UpperCAmelCase : int = [int(lowerCAmelCase_ ) for item in user_input.split(""",""" )]
quick_sort_random(lowerCAmelCase_ , 0 , len(lowerCAmelCase_ ) )
print(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Dict = [2, 4, 6, 8, 10, 12]
_UpperCAmelCase : Optional[int] = 100
self.assertEqual(kp.calc_profit(a_ ,a_ ,a_ ) ,210 )
def _snake_case ( self ) -> Union[str, Any]:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Any:
self.assertRaisesRegex(a_ ,"""Weight can not be negative.""" )
def _snake_case ( self ) -> Optional[Any]:
self.assertRaisesRegex(a_ ,"""Profit can not be negative.""" )
def _snake_case ( self ) -> Dict:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Tuple:
self.assertRaisesRegex(
a_ ,"""The length of profit and weight must be same.""" )
if __name__ == "__main__":
unittest.main()
| 349 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 0.0
UpperCAmelCase = 1
UpperCAmelCase = 1
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : str = []
_UpperCAmelCase : Dict = []
for i in range(self.num_layers ):
_UpperCAmelCase : Optional[int] = self.in_channels if i == 0 else self.out_channels
_UpperCAmelCase : Dict = FlaxResnetBlockaD(
in_channels=a_ ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(a_ )
_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(a_ )
_UpperCAmelCase : Dict = resnets
_UpperCAmelCase : Any = attentions
if self.add_downsample:
_UpperCAmelCase : List[str] = FlaxDownsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,a_ ,a_ ,a_ ,a_=True ) -> Any:
_UpperCAmelCase : Optional[Any] = ()
for resnet, attn in zip(self.resnets ,self.attentions ):
_UpperCAmelCase : Any = resnet(a_ ,a_ ,deterministic=a_ )
_UpperCAmelCase : List[str] = attn(a_ ,a_ ,deterministic=a_ )
output_states += (hidden_states,)
if self.add_downsample:
_UpperCAmelCase : Any = self.downsamplers_a(a_ )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 0.0
UpperCAmelCase = 1
UpperCAmelCase = True
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Optional[int] = []
for i in range(self.num_layers ):
_UpperCAmelCase : str = self.in_channels if i == 0 else self.out_channels
_UpperCAmelCase : List[str] = FlaxResnetBlockaD(
in_channels=a_ ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(a_ )
_UpperCAmelCase : Optional[int] = resnets
if self.add_downsample:
_UpperCAmelCase : List[Any] = FlaxDownsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,a_ ,a_ ,a_=True ) -> str:
_UpperCAmelCase : str = ()
for resnet in self.resnets:
_UpperCAmelCase : Optional[Any] = resnet(a_ ,a_ ,deterministic=a_ )
output_states += (hidden_states,)
if self.add_downsample:
_UpperCAmelCase : int = self.downsamplers_a(a_ )
output_states += (hidden_states,)
return hidden_states, output_states
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 0.0
UpperCAmelCase = 1
UpperCAmelCase = 1
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : List[Any] = []
for i in range(self.num_layers ):
_UpperCAmelCase : Tuple = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_UpperCAmelCase : int = self.prev_output_channel if i == 0 else self.out_channels
_UpperCAmelCase : Optional[int] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(a_ )
_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(a_ )
_UpperCAmelCase : List[str] = resnets
_UpperCAmelCase : Tuple = attentions
if self.add_upsample:
_UpperCAmelCase : int = FlaxUpsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,a_ ,a_ ,a_ ,a_ ,a_=True ) -> Optional[Any]:
for resnet, attn in zip(self.resnets ,self.attentions ):
# pop res hidden states
_UpperCAmelCase : Dict = res_hidden_states_tuple[-1]
_UpperCAmelCase : str = res_hidden_states_tuple[:-1]
_UpperCAmelCase : Union[str, Any] = jnp.concatenate((hidden_states, res_hidden_states) ,axis=-1 )
_UpperCAmelCase : List[str] = resnet(a_ ,a_ ,deterministic=a_ )
_UpperCAmelCase : str = attn(a_ ,a_ ,deterministic=a_ )
if self.add_upsample:
_UpperCAmelCase : Optional[int] = self.upsamplers_a(a_ )
return hidden_states
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 42
UpperCAmelCase = 0.0
UpperCAmelCase = 1
UpperCAmelCase = True
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Union[str, Any] = []
for i in range(self.num_layers ):
_UpperCAmelCase : int = self.in_channels if (i == self.num_layers - 1) else self.out_channels
_UpperCAmelCase : str = self.prev_output_channel if i == 0 else self.out_channels
_UpperCAmelCase : List[Any] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels ,out_channels=self.out_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(a_ )
_UpperCAmelCase : Dict = resnets
if self.add_upsample:
_UpperCAmelCase : Dict = FlaxUpsampleaD(self.out_channels ,dtype=self.dtype )
def __call__( self ,a_ ,a_ ,a_ ,a_=True ) -> int:
for resnet in self.resnets:
# pop res hidden states
_UpperCAmelCase : Any = res_hidden_states_tuple[-1]
_UpperCAmelCase : List[str] = res_hidden_states_tuple[:-1]
_UpperCAmelCase : Optional[Any] = jnp.concatenate((hidden_states, res_hidden_states) ,axis=-1 )
_UpperCAmelCase : Dict = resnet(a_ ,a_ ,deterministic=a_ )
if self.add_upsample:
_UpperCAmelCase : str = self.upsamplers_a(a_ )
return hidden_states
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 0.0
UpperCAmelCase = 1
UpperCAmelCase = 1
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> List[str]:
# there is always at least one resnet
_UpperCAmelCase : Optional[int] = [
FlaxResnetBlockaD(
in_channels=self.in_channels ,out_channels=self.in_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
]
_UpperCAmelCase : List[Any] = []
for _ in range(self.num_layers ):
_UpperCAmelCase : str = 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(a_ )
_UpperCAmelCase : List[Any] = FlaxResnetBlockaD(
in_channels=self.in_channels ,out_channels=self.in_channels ,dropout_prob=self.dropout ,dtype=self.dtype ,)
resnets.append(a_ )
_UpperCAmelCase : Optional[Any] = resnets
_UpperCAmelCase : List[str] = attentions
def __call__( self ,a_ ,a_ ,a_ ,a_=True ) -> List[Any]:
_UpperCAmelCase : Any = self.resnets[0](a_ ,a_ )
for attn, resnet in zip(self.attentions ,self.resnets[1:] ):
_UpperCAmelCase : int = attn(a_ ,a_ ,deterministic=a_ )
_UpperCAmelCase : List[Any] = resnet(a_ ,a_ ,deterministic=a_ )
return hidden_states
| 349 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 1 |
'''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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A_ : List[str] = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=False )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
("""text_embeddings.word_embeddings.weight""", """vilt.embeddings.text_embeddings.word_embeddings.weight"""),
(
"""text_embeddings.position_embeddings.weight""",
"""vilt.embeddings.text_embeddings.position_embeddings.weight""",
),
("""text_embeddings.position_ids""", """vilt.embeddings.text_embeddings.position_ids"""),
(
"""text_embeddings.token_type_embeddings.weight""",
"""vilt.embeddings.text_embeddings.token_type_embeddings.weight""",
),
("""text_embeddings.LayerNorm.weight""", """vilt.embeddings.text_embeddings.LayerNorm.weight"""),
("""text_embeddings.LayerNorm.bias""", """vilt.embeddings.text_embeddings.LayerNorm.bias"""),
# patch embeddings
("""transformer.cls_token""", """vilt.embeddings.cls_token"""),
("""transformer.patch_embed.proj.weight""", """vilt.embeddings.patch_embeddings.projection.weight"""),
("""transformer.patch_embed.proj.bias""", """vilt.embeddings.patch_embeddings.projection.bias"""),
("""transformer.pos_embed""", """vilt.embeddings.position_embeddings"""),
# token type embeddings
("""token_type_embeddings.weight""", """vilt.embeddings.token_type_embeddings.weight"""),
] )
# final layernorm + pooler
rename_keys.extend(
[
("""transformer.norm.weight""", """vilt.layernorm.weight"""),
("""transformer.norm.bias""", """vilt.layernorm.bias"""),
("""pooler.dense.weight""", """vilt.pooler.dense.weight"""),
("""pooler.dense.bias""", """vilt.pooler.dense.bias"""),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
("""vqa_classifier.0.weight""", """classifier.0.weight"""),
("""vqa_classifier.0.bias""", """classifier.0.bias"""),
("""vqa_classifier.1.weight""", """classifier.1.weight"""),
("""vqa_classifier.1.bias""", """classifier.1.bias"""),
("""vqa_classifier.3.weight""", """classifier.3.weight"""),
("""vqa_classifier.3.bias""", """classifier.3.bias"""),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
("""nlvr2_classifier.0.weight""", """classifier.0.weight"""),
("""nlvr2_classifier.0.bias""", """classifier.0.bias"""),
("""nlvr2_classifier.1.weight""", """classifier.1.weight"""),
("""nlvr2_classifier.1.bias""", """classifier.1.bias"""),
("""nlvr2_classifier.3.weight""", """classifier.3.weight"""),
("""nlvr2_classifier.3.bias""", """classifier.3.bias"""),
] )
else:
pass
return rename_keys
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
_UpperCAmelCase : Optional[Any] = """vilt."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_UpperCAmelCase : int = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight''' )
_UpperCAmelCase : Tuple = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[Any] = in_proj_weight[
: config.hidden_size, :
]
_UpperCAmelCase : Tuple = in_proj_bias[: config.hidden_size]
_UpperCAmelCase : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_UpperCAmelCase : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_UpperCAmelCase : str = in_proj_weight[
-config.hidden_size :, :
]
_UpperCAmelCase : Any = in_proj_bias[-config.hidden_size :]
def snake_case_ ( lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
_UpperCAmelCase : Tuple = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = dct.pop(lowerCAmelCase_ )
_UpperCAmelCase : int = val
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = ViltConfig(image_size=384 , patch_size=32 , tie_word_embeddings=lowerCAmelCase_ )
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : Dict = False
_UpperCAmelCase : Dict = False
_UpperCAmelCase : List[Any] = False
if "vqa" in checkpoint_url:
_UpperCAmelCase : Any = True
_UpperCAmelCase : Union[str, Any] = 3129
_UpperCAmelCase : List[str] = """huggingface/label-files"""
_UpperCAmelCase : Dict = """vqa2-id2label.json"""
_UpperCAmelCase : Union[str, Any] = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase : Dict = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
_UpperCAmelCase : int = idalabel
_UpperCAmelCase : int = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Union[str, Any] = ViltForQuestionAnswering(lowerCAmelCase_ )
elif "nlvr" in checkpoint_url:
_UpperCAmelCase : int = True
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Tuple = {0: """False""", 1: """True"""}
_UpperCAmelCase : Optional[int] = {v: k for k, v in config.idalabel.items()}
_UpperCAmelCase : str = 3
_UpperCAmelCase : Any = ViltForImagesAndTextClassification(lowerCAmelCase_ )
elif "irtr" in checkpoint_url:
_UpperCAmelCase : Union[str, Any] = True
_UpperCAmelCase : List[Any] = ViltForImageAndTextRetrieval(lowerCAmelCase_ )
elif "mlm_itm" in checkpoint_url:
_UpperCAmelCase : Union[str, Any] = True
_UpperCAmelCase : int = ViltForMaskedLM(lowerCAmelCase_ )
else:
raise ValueError("""Unknown model type""" )
# load state_dict of original model, remove and rename some keys
_UpperCAmelCase : List[str] = torch.hub.load_state_dict_from_url(lowerCAmelCase_ , map_location="""cpu""" )["""state_dict"""]
_UpperCAmelCase : Tuple = create_rename_keys(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
read_in_q_k_v(lowerCAmelCase_ , lowerCAmelCase_ )
if mlm_model or irtr_model:
_UpperCAmelCase : int = ["""itm_score.fc.weight""", """itm_score.fc.bias"""]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_UpperCAmelCase ,_UpperCAmelCase : List[str] = model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowerCAmelCase_ )
# Define processor
_UpperCAmelCase : Dict = ViltImageProcessor(size=384 )
_UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_UpperCAmelCase : int = ViltProcessor(lowerCAmelCase_ , lowerCAmelCase_ )
# Forward pass on example inputs (image + text)
if nlvr_model:
_UpperCAmelCase : str = Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg""" , stream=lowerCAmelCase_ ).raw )
_UpperCAmelCase : Optional[int] = Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg""" , stream=lowerCAmelCase_ ).raw )
_UpperCAmelCase : Any = (
"""The left image contains twice the number of dogs as the right image, and at least two dogs in total are"""
""" standing."""
)
_UpperCAmelCase : Any = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors="""pt""" )
_UpperCAmelCase : Optional[Any] = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors="""pt""" )
_UpperCAmelCase : Union[str, Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_UpperCAmelCase : Tuple = Image.open(requests.get("""http://images.cocodataset.org/val2017/000000039769.jpg""" , stream=lowerCAmelCase_ ).raw )
if mlm_model:
_UpperCAmelCase : int = """a bunch of [MASK] laying on a [MASK]."""
else:
_UpperCAmelCase : Union[str, Any] = """How many cats are there?"""
_UpperCAmelCase : str = processor(lowerCAmelCase_ , lowerCAmelCase_ , return_tensors="""pt""" )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
# Verify outputs
if mlm_model:
_UpperCAmelCase : Tuple = torch.Size([1, 11, 30522] )
_UpperCAmelCase : Optional[int] = torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowerCAmelCase_ , atol=1e-4 )
# verify masked token prediction equals "cats"
_UpperCAmelCase : Any = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_UpperCAmelCase : int = torch.Size([1, 3129] )
_UpperCAmelCase : Union[str, Any] = torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1] )
assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowerCAmelCase_ , atol=1e-4 )
# verify vqa prediction equals "2"
_UpperCAmelCase : str = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_UpperCAmelCase : Any = torch.Size([1, 2] )
_UpperCAmelCase : Union[str, Any] = torch.tensor([-2.8_7_2_1, 2.1_2_9_1] )
assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 )
assert outputs.logits.shape == expected_shape
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
type=str,
help="""URL of the checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
A_ : Optional[int] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 349 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_ : Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
UpperCAmelCase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Any = {}
if self.train_dir is not None:
_UpperCAmelCase : Dict = self.train_dir
if self.validation_dir is not None:
_UpperCAmelCase : List[Any] = self.validation_dir
_UpperCAmelCase : Tuple = data_files if data_files else None
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
UpperCAmelCase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
UpperCAmelCase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Tuple = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , lowerCAmelCase_ , lowerCAmelCase_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCAmelCase : Tuple = training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase_ )
transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
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.
_UpperCAmelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase : Optional[Any] = 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.""" )
# Initialize our dataset.
_UpperCAmelCase : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_UpperCAmelCase : List[str] = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowerCAmelCase_ ) and data_args.train_val_split > 0.0:
_UpperCAmelCase : str = ds["""train"""].train_test_split(data_args.train_val_split )
_UpperCAmelCase : Union[str, Any] = split["""train"""]
_UpperCAmelCase : int = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase : Any = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
_UpperCAmelCase : Optional[int] = ViTMAEConfig.from_pretrained(model_args.config_name , **lowerCAmelCase_ )
elif model_args.model_name_or_path:
_UpperCAmelCase : Optional[Any] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowerCAmelCase_ )
else:
_UpperCAmelCase : str = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_UpperCAmelCase : int = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowerCAmelCase_ )
elif model_args.model_name_or_path:
_UpperCAmelCase : int = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowerCAmelCase_ )
else:
_UpperCAmelCase : Any = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_UpperCAmelCase : Any = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowerCAmelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
_UpperCAmelCase : Union[str, Any] = ViTMAEForPreTraining(lowerCAmelCase_ )
if training_args.do_train:
_UpperCAmelCase : Union[str, Any] = ds["""train"""].column_names
else:
_UpperCAmelCase : Optional[int] = ds["""validation"""].column_names
if data_args.image_column_name is not None:
_UpperCAmelCase : Optional[int] = data_args.image_column_name
elif "image" in column_names:
_UpperCAmelCase : Optional[int] = """image"""
elif "img" in column_names:
_UpperCAmelCase : List[str] = """img"""
else:
_UpperCAmelCase : str = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_UpperCAmelCase : Optional[int] = image_processor.size["""shortest_edge"""]
else:
_UpperCAmelCase : str = (image_processor.size["""height"""], image_processor.size["""width"""])
_UpperCAmelCase : List[Any] = Compose(
[
Lambda(lambda lowerCAmelCase_ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowerCAmelCase_ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(lowerCAmelCase_ ):
_UpperCAmelCase : int = [transforms(lowerCAmelCase_ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
_UpperCAmelCase : int = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowerCAmelCase_ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
_UpperCAmelCase : List[Any] = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowerCAmelCase_ )
# Compute absolute learning rate
_UpperCAmelCase : Optional[Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_UpperCAmelCase : Optional[Any] = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
_UpperCAmelCase : str = Trainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=lowerCAmelCase_ , data_collator=lowerCAmelCase_ , )
# Training
if training_args.do_train:
_UpperCAmelCase : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase : Any = last_checkpoint
_UpperCAmelCase : List[str] = trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCAmelCase : List[Any] = trainer.evaluate()
trainer.log_metrics("""eval""" , lowerCAmelCase_ )
trainer.save_metrics("""eval""" , lowerCAmelCase_ )
# Write model card and (optionally) push to hub
_UpperCAmelCase : Dict = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase_ )
else:
trainer.create_model_card(**lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 | 1 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[Any] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
"""LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LiltForQuestionAnswering""",
"""LiltForSequenceClassification""",
"""LiltForTokenClassification""",
"""LiltModel""",
"""LiltPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
A_ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined for non-natural numbers""" )
_UpperCAmelCase : int = precision
_UpperCAmelCase : List[Any] = ceil(precision / 14 )
_UpperCAmelCase : Optional[int] = 426880 * Decimal(10005 ).sqrt()
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : Union[str, Any] = 13591409
_UpperCAmelCase : List[Any] = Decimal(lowerCAmelCase_ )
for k in range(1 , lowerCAmelCase_ ):
_UpperCAmelCase : str = factorial(6 * k ) // (factorial(3 * k ) * factorial(lowerCAmelCase_ ) ** 3)
linear_term += 545140134
exponential_term *= -262537412640768000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
A_ : Tuple = 5_0
print(f"""The first {n} digits of pi is: {pi(n)}""")
| 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A_ : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
A_ : Dict = """ \"\"\"
Output class for the scheduler's step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"\"\"
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
"""
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : List[str] = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir ,"""schedulers/""" ) )
_UpperCAmelCase : int = self.diffusers_dir
shutil.copy(
os.path.join(a_ ,"""src/diffusers/schedulers/scheduling_ddpm.py""" ) ,os.path.join(self.diffusers_dir ,"""schedulers/scheduling_ddpm.py""" ) ,)
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Tuple = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_=None ) -> Optional[Any]:
_UpperCAmelCase : List[str] = comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
if overwrite_result is not None:
_UpperCAmelCase : Union[str, Any] = comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
_UpperCAmelCase : int = black.Mode(target_versions={black.TargetVersion.PYaa} ,line_length=119 )
_UpperCAmelCase : List[str] = black.format_str(a_ ,mode=a_ )
_UpperCAmelCase : Tuple = os.path.join(self.diffusers_dir ,"""new_code.py""" )
with open(a_ ,"""w""" ,newline="""\n""" ) as f:
f.write(a_ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(a_ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name ,overwrite=a_ )
with open(a_ ,"""r""" ) as f:
self.assertTrue(f.read() ,a_ )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(a_ ,a_ )
def _snake_case ( self ) -> Optional[int]:
# Base copy consistency
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" ,"""DDPMSchedulerOutput""" ,REFERENCE_CODE + """\n""" ,)
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" ,"""DDPMSchedulerOutput""" ,a_ ,)
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" ,"""TestSchedulerOutput""" ,re.sub("""DDPM""" ,"""Test""" ,a_ ) ,)
# Copy consistency with a really long name
_UpperCAmelCase : List[Any] = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,f'''{long_class_name}SchedulerOutput''' ,re.sub("""Bert""" ,a_ ,a_ ) ,)
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" ,"""TestSchedulerOutput""" ,a_ ,overwrite_result=re.sub("""DDPM""" ,"""Test""" ,a_ ) ,)
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = SwinvaConfig()
_UpperCAmelCase : Optional[Any] = swinva_name.split("""_""" )
_UpperCAmelCase : Dict = name_split[1]
if "to" in name_split[3]:
_UpperCAmelCase : Optional[Any] = int(name_split[3][-3:] )
else:
_UpperCAmelCase : List[str] = int(name_split[3] )
if "to" in name_split[2]:
_UpperCAmelCase : List[Any] = int(name_split[2][-2:] )
else:
_UpperCAmelCase : List[Any] = int(name_split[2][6:] )
if model_size == "tiny":
_UpperCAmelCase : Tuple = 96
_UpperCAmelCase : Dict = (2, 2, 6, 2)
_UpperCAmelCase : Union[str, Any] = (3, 6, 12, 24)
elif model_size == "small":
_UpperCAmelCase : Tuple = 96
_UpperCAmelCase : List[str] = (2, 2, 18, 2)
_UpperCAmelCase : List[str] = (3, 6, 12, 24)
elif model_size == "base":
_UpperCAmelCase : List[str] = 128
_UpperCAmelCase : Optional[Any] = (2, 2, 18, 2)
_UpperCAmelCase : Optional[Any] = (4, 8, 16, 32)
else:
_UpperCAmelCase : Optional[Any] = 192
_UpperCAmelCase : Dict = (2, 2, 18, 2)
_UpperCAmelCase : Any = (6, 12, 24, 48)
if "to" in swinva_name:
_UpperCAmelCase : Optional[Any] = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
_UpperCAmelCase : str = 21841
_UpperCAmelCase : Optional[int] = """huggingface/label-files"""
_UpperCAmelCase : int = """imagenet-22k-id2label.json"""
_UpperCAmelCase : int = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase : Union[str, Any] = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
_UpperCAmelCase : Optional[int] = idalabel
_UpperCAmelCase : Any = {v: k for k, v in idalabel.items()}
else:
_UpperCAmelCase : Dict = 1000
_UpperCAmelCase : Tuple = """huggingface/label-files"""
_UpperCAmelCase : Any = """imagenet-1k-id2label.json"""
_UpperCAmelCase : Dict = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase : Optional[Any] = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
_UpperCAmelCase : Any = idalabel
_UpperCAmelCase : Optional[Any] = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Union[str, Any] = img_size
_UpperCAmelCase : Union[str, Any] = num_classes
_UpperCAmelCase : List[str] = embed_dim
_UpperCAmelCase : Tuple = depths
_UpperCAmelCase : Optional[Any] = num_heads
_UpperCAmelCase : List[str] = window_size
return config
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
_UpperCAmelCase : Any = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
_UpperCAmelCase : Optional[Any] = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
_UpperCAmelCase : List[str] = """encoder.""" + name
if "attn.proj" in name:
_UpperCAmelCase : Dict = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
_UpperCAmelCase : Optional[Any] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
_UpperCAmelCase : int = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_UpperCAmelCase : str = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
_UpperCAmelCase : Optional[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_UpperCAmelCase : List[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
_UpperCAmelCase : Optional[Any] = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
_UpperCAmelCase : Dict = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
_UpperCAmelCase : Dict = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
_UpperCAmelCase : List[Any] = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if name == "norm.weight":
_UpperCAmelCase : Optional[Any] = """layernorm.weight"""
if name == "norm.bias":
_UpperCAmelCase : Union[str, Any] = """layernorm.bias"""
if "head" in name:
_UpperCAmelCase : Union[str, Any] = name.replace("""head""" , """classifier""" )
else:
_UpperCAmelCase : Optional[Any] = """swinv2.""" + name
return name
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_UpperCAmelCase : List[str] = orig_state_dict.pop(lowerCAmelCase_ )
if "mask" in key:
continue
elif "qkv" in key:
_UpperCAmelCase : List[Any] = key.split(""".""" )
_UpperCAmelCase : Optional[Any] = int(key_split[1] )
_UpperCAmelCase : Optional[int] = int(key_split[3] )
_UpperCAmelCase : List[Any] = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCAmelCase : List[str] = val[:dim, :]
_UpperCAmelCase : Union[str, Any] = val[dim : dim * 2, :]
_UpperCAmelCase : List[Any] = val[-dim:, :]
else:
_UpperCAmelCase : Any = val[:dim]
_UpperCAmelCase : Optional[Any] = val[
dim : dim * 2
]
_UpperCAmelCase : int = val[-dim:]
else:
_UpperCAmelCase : Union[str, Any] = val
return orig_state_dict
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Tuple = timm.create_model(lowerCAmelCase_ , pretrained=lowerCAmelCase_ )
timm_model.eval()
_UpperCAmelCase : Optional[int] = get_swinva_config(lowerCAmelCase_ )
_UpperCAmelCase : str = SwinvaForImageClassification(lowerCAmelCase_ )
model.eval()
_UpperCAmelCase : Tuple = convert_state_dict(timm_model.state_dict() , lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_""" , """-""" ) ) )
_UpperCAmelCase : List[Any] = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
_UpperCAmelCase : int = image_processor(images=lowerCAmelCase_ , return_tensors="""pt""" )
_UpperCAmelCase : List[str] = timm_model(inputs["""pixel_values"""] )
_UpperCAmelCase : str = model(**lowerCAmelCase_ ).logits
assert torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1e-3 )
print(F'''Saving model {swinva_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_ )
model.push_to_hub(
repo_path_or_name=Path(lowerCAmelCase_ , lowerCAmelCase_ ) , organization="""nandwalritik""" , commit_message="""Add model""" , )
if __name__ == "__main__":
A_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
A_ : Optional[int] = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import pytest
A_ : Optional[int] = """__dummy_dataset1__"""
A_ : Any = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def snake_case_ ( )-> Tuple:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = dataset_loading_script_name
_UpperCAmelCase : Tuple = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=lowerCAmelCase_ )
_UpperCAmelCase : Tuple = script_dir / F'''{script_name}.py'''
with open(lowerCAmelCase_ , """w""" ) as f:
f.write(lowerCAmelCase_ )
return str(lowerCAmelCase_ )
| 349 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A_ : Dict = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """sequence-classification"""
def __init__( self ,a_ ) -> Dict:
if type(a_ ) == dict:
_UpperCAmelCase : Tuple = Namespace(**a_ )
_UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task]
_UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(a_ ,a_ ,self.mode )
def _snake_case ( self ,**a_ ) -> Optional[Any]:
return self.model(**a_ )
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : Any = self(**a_ )
_UpperCAmelCase : int = outputs[0]
_UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""]
_UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = self.hparams
_UpperCAmelCase : int = processors[args.task]()
_UpperCAmelCase : str = processor.get_labels()
for mode in ["train", "dev"]:
_UpperCAmelCase : Tuple = self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,a_ )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
_UpperCAmelCase : List[Any] = (
processor.get_dev_examples(args.data_dir )
if mode == """dev"""
else processor.get_train_examples(args.data_dir )
)
_UpperCAmelCase : Union[str, Any] = convert_examples_to_features(
a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,)
logger.info("""Saving features into cached file %s""" ,a_ )
torch.save(a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader:
_UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode
_UpperCAmelCase : Tuple = self._feature_file(a_ )
logger.info("""Loading features from cached file %s""" ,a_ )
_UpperCAmelCase : Union[str, Any] = torch.load(a_ )
_UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
_UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
_UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float )
return DataLoader(
TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,)
def _snake_case ( self ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : List[str] = self(**a_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2]
_UpperCAmelCase : List[str] = logits.detach().cpu().numpy()
_UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self ,a_ ) -> tuple:
_UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
_UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : int = np.argmax(a_ ,axis=1 )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : Union[str, Any] = np.squeeze(a_ )
_UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
_UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )}
_UpperCAmelCase : Dict = dict(results.items() )
_UpperCAmelCase : Any = results
return ret, preds_list, out_label_list
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( a_ ,a_ ) -> Any:
BaseTransformer.add_model_specific_args(a_ ,a_ )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=a_ ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
add_generic_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_UpperCAmelCase : Optional[int] = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ )
_UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) )
_UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import math
import qiskit
def snake_case_ ( lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
or isinstance(lowerCAmelCase_ , lowerCAmelCase_ )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(lowerCAmelCase_ ) != input_a)
or (math.floor(lowerCAmelCase_ ) != input_a)
or (math.floor(lowerCAmelCase_ ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
_UpperCAmelCase : List[str] = qiskit.QuantumRegister(4 , """qr""" )
_UpperCAmelCase : str = qiskit.ClassicalRegister(2 , """cr""" )
# list the entries
_UpperCAmelCase : Optional[int] = [input_a, input_a, carry_in]
_UpperCAmelCase : Any = qiskit.QuantumCircuit(lowerCAmelCase_ , lowerCAmelCase_ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(lowerCAmelCase_ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(lowerCAmelCase_ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(lowerCAmelCase_ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , lowerCAmelCase_ ) # measure the last two qbits
_UpperCAmelCase : List[str] = qiskit.Aer.get_backend("""aer_simulator""" )
_UpperCAmelCase : List[Any] = qiskit.execute(lowerCAmelCase_ , lowerCAmelCase_ , shots=1000 )
return job.result().get_counts(lowerCAmelCase_ )
if __name__ == "__main__":
print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """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-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
A_ : Optional[int] = """src/transformers"""
A_ : List[Any] = """docs/source/en"""
A_ : List[str] = """."""
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
with open(lowerCAmelCase_ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_UpperCAmelCase : Union[str, Any] = f.readlines()
# Find the start prompt.
_UpperCAmelCase : Union[str, Any] = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
_UpperCAmelCase : List[Any] = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
A_ : Any = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A_ : Any = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A_ : Union[str, Any] = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A_ : int = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# This is to make sure the transformers module imported is the one in the repo.
A_ : str = direct_transformers_import(TRANSFORMERS_PATH)
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Any = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
'''simple docstring'''
_UpperCAmelCase : int = 2 if text == """✅""" or text == """❌""" else len(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = (width - text_length) // 2
_UpperCAmelCase : int = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def snake_case_ ( )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_UpperCAmelCase : Tuple = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
_UpperCAmelCase : Dict = {name: config.replace("""Config""" , """""" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
_UpperCAmelCase : Union[str, Any] = collections.defaultdict(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = collections.defaultdict(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = collections.defaultdict(lowerCAmelCase_ )
_UpperCAmelCase : int = collections.defaultdict(lowerCAmelCase_ )
_UpperCAmelCase : Any = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = None
if attr_name.endswith("""Tokenizer""" ):
_UpperCAmelCase : Tuple = slow_tokenizers
_UpperCAmelCase : Optional[int] = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
_UpperCAmelCase : str = fast_tokenizers
_UpperCAmelCase : Union[str, Any] = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
_UpperCAmelCase : int = tf_models
_UpperCAmelCase : Optional[int] = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
_UpperCAmelCase : Optional[Any] = flax_models
_UpperCAmelCase : Tuple = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
_UpperCAmelCase : str = pt_models
_UpperCAmelCase : Dict = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
_UpperCAmelCase : str = True
break
# Try again after removing the last word in the name
_UpperCAmelCase : str = """""".join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
_UpperCAmelCase : Tuple = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
_UpperCAmelCase : int = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
_UpperCAmelCase : int = [len(lowerCAmelCase_ ) + 2 for c in columns]
_UpperCAmelCase : Union[str, Any] = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
_UpperCAmelCase : Optional[Any] = """|""" + """|""".join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
_UpperCAmelCase : int = {True: """✅""", False: """❌"""}
for name in model_names:
_UpperCAmelCase : Any = model_name_to_prefix[name]
_UpperCAmelCase : Tuple = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def snake_case_ ( lowerCAmelCase_=False )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
_UpperCAmelCase : Union[str, Any] = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , """index.md""" ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"""The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )
if __name__ == "__main__":
A_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
A_ : List[str] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 349 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@slow
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,"""prajjwal1/bert-tiny""" )
_UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_UpperCAmelCase : List[Any] = bertabert.config.encoder.vocab_size
_UpperCAmelCase : Optional[int] = tokenizer.sep_token_id
_UpperCAmelCase : Union[str, Any] = tokenizer.cls_token_id
_UpperCAmelCase : str = 128
_UpperCAmelCase : List[str] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""train[:1%]""" )
_UpperCAmelCase : Union[str, Any] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""validation[:1%]""" )
_UpperCAmelCase : Any = train_dataset.select(range(32 ) )
_UpperCAmelCase : Any = val_dataset.select(range(16 ) )
_UpperCAmelCase : List[Any] = 4
def _map_to_encoder_decoder_inputs(a_ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_UpperCAmelCase : int = tokenizer(batch["""article"""] ,padding="""max_length""" ,truncation=a_ ,max_length=512 )
_UpperCAmelCase : Tuple = tokenizer(batch["""highlights"""] ,padding="""max_length""" ,truncation=a_ ,max_length=128 )
_UpperCAmelCase : int = inputs.input_ids
_UpperCAmelCase : Union[str, Any] = inputs.attention_mask
_UpperCAmelCase : Union[str, Any] = outputs.input_ids
_UpperCAmelCase : Dict = outputs.input_ids.copy()
_UpperCAmelCase : Dict = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
_UpperCAmelCase : Optional[int] = outputs.attention_mask
assert all(len(a_ ) == 512 for x in inputs.input_ids )
assert all(len(a_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(a_ ):
_UpperCAmelCase : Optional[int] = pred.label_ids
_UpperCAmelCase : Optional[int] = pred.predictions
# all unnecessary tokens are removed
_UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : str = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : Tuple = sum([int(pred_str[i] == label_str[i] ) for i in range(len(a_ ) )] ) / len(a_ )
return {"accuracy": accuracy}
# map train dataset
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
train_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
# same for validation dataset
_UpperCAmelCase : List[str] = val_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
val_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
_UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : List[str] = SeqaSeqTrainingArguments(
output_dir=a_ ,per_device_train_batch_size=a_ ,per_device_eval_batch_size=a_ ,predict_with_generate=a_ ,evaluation_strategy="""steps""" ,do_train=a_ ,do_eval=a_ ,warmup_steps=0 ,eval_steps=2 ,logging_steps=2 ,)
# instantiate trainer
_UpperCAmelCase : int = SeqaSeqTrainer(
model=a_ ,args=a_ ,compute_metrics=_compute_metrics ,train_dataset=a_ ,eval_dataset=a_ ,tokenizer=a_ ,)
# start training
trainer.train()
| 349 | 1 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ )-> bool:
'''simple docstring'''
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(lowerCAmelCase_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case_ ( lowerCAmelCase_ = 10001 )-> int:
'''simple docstring'''
try:
_UpperCAmelCase : Any = int(lowerCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
_UpperCAmelCase : list[int] = []
_UpperCAmelCase : Any = 2
while len(lowerCAmelCase_ ) < nth:
if is_prime(lowerCAmelCase_ ):
primes.append(lowerCAmelCase_ )
num += 1
else:
num += 1
return primes[len(lowerCAmelCase_ ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 349 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A_ : List[Any] = 637_8137.0
A_ : Dict = 635_6752.31_4245
A_ : int = 6_3_7_8_1_3_7
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> float:
'''simple docstring'''
_UpperCAmelCase : Tuple = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_UpperCAmelCase : Any = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
_UpperCAmelCase : Optional[Any] = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_UpperCAmelCase : Union[str, Any] = haversine_distance(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_UpperCAmelCase : Optional[int] = (b_lata + b_lata) / 2
_UpperCAmelCase : Any = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_UpperCAmelCase : List[str] = (sin(lowerCAmelCase_ ) ** 2) * (cos(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = cos(sigma / 2 ) ** 2
_UpperCAmelCase : Dict = (sigma - sin(lowerCAmelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_UpperCAmelCase : Union[str, Any] = (cos(lowerCAmelCase_ ) ** 2) * (sin(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = sin(sigma / 2 ) ** 2
_UpperCAmelCase : Optional[Any] = (sigma + sin(lowerCAmelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """gptj"""
UpperCAmelCase = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self ,a_=50_400 ,a_=2_048 ,a_=4_096 ,a_=28 ,a_=16 ,a_=64 ,a_=None ,a_="gelu_new" ,a_=0.0 ,a_=0.0 ,a_=0.0 ,a_=1E-5 ,a_=0.02 ,a_=True ,a_=50_256 ,a_=50_256 ,a_=False ,**a_ ,) -> Dict:
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : str = n_positions
_UpperCAmelCase : Optional[int] = n_embd
_UpperCAmelCase : List[str] = n_layer
_UpperCAmelCase : List[Any] = n_head
_UpperCAmelCase : Tuple = n_inner
_UpperCAmelCase : List[str] = rotary_dim
_UpperCAmelCase : List[str] = activation_function
_UpperCAmelCase : Union[str, Any] = resid_pdrop
_UpperCAmelCase : Union[str, Any] = embd_pdrop
_UpperCAmelCase : Optional[int] = attn_pdrop
_UpperCAmelCase : List[str] = layer_norm_epsilon
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : str = use_cache
_UpperCAmelCase : Any = bos_token_id
_UpperCAmelCase : List[Any] = eos_token_id
super().__init__(
bos_token_id=a_ ,eos_token_id=a_ ,tie_word_embeddings=a_ ,**a_ )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = "default" ,a_ = None ,a_ = False ,) -> int:
super().__init__(a_ ,task=a_ ,patching_specs=a_ ,use_past=a_ )
if not getattr(self._config ,"""pad_token_id""" ,a_ ):
# TODO: how to do that better?
_UpperCAmelCase : List[str] = 0
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
_UpperCAmelCase : List[str] = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(a_ ,direction="""inputs""" )
_UpperCAmelCase : Any = {0: """batch""", 1: """past_sequence + sequence"""}
else:
_UpperCAmelCase : Optional[int] = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _snake_case ( self ) -> int:
return self._config.n_layer
@property
def _snake_case ( self ) -> int:
return self._config.n_head
def _snake_case ( self ,a_ ,a_ = -1 ,a_ = -1 ,a_ = False ,a_ = None ,) -> Mapping[str, Any]:
_UpperCAmelCase : Any = super(a_ ,self ).generate_dummy_inputs(
a_ ,batch_size=a_ ,seq_length=a_ ,is_pair=a_ ,framework=a_ )
# We need to order the input in the way they appears in the forward()
_UpperCAmelCase : Optional[int] = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_UpperCAmelCase ,_UpperCAmelCase : Dict = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_UpperCAmelCase : Union[str, Any] = seqlen + 2
_UpperCAmelCase : List[str] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_UpperCAmelCase : Optional[Any] = [
(torch.zeros(a_ ), torch.zeros(a_ )) for _ in range(self.num_layers )
]
_UpperCAmelCase : int = common_inputs["""attention_mask"""]
if self.use_past:
_UpperCAmelCase : Any = ordered_inputs["""attention_mask"""].dtype
_UpperCAmelCase : Optional[int] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(a_ ,a_ ,dtype=a_ )] ,dim=1 )
return ordered_inputs
@property
def _snake_case ( self ) -> int:
return 13
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict = str(abs(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = [list(lowerCAmelCase_ ) for char in range(len(lowerCAmelCase_ ) )]
for index in range(len(lowerCAmelCase_ ) ):
num_transpositions[index].pop(lowerCAmelCase_ )
return max(
int("""""".join(list(lowerCAmelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowerCAmelCase_ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowerCAmelCase_ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowerCAmelCase_ )
return parser.parse_args()
def snake_case_ ( )-> str:
'''simple docstring'''
_UpperCAmelCase : List[str] = parse_args()
# Import training_script as a module.
_UpperCAmelCase : List[Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase : Optional[Any] = script_fpath.stem
_UpperCAmelCase : List[str] = importlib.import_module(lowerCAmelCase_ )
# Patch sys.argv
_UpperCAmelCase : Dict = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A_ : List[str] = logging.getLogger(__name__)
class lowercase :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
_UpperCAmelCase : str = False
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ) -> Dict:
if not self.initialized:
_UpperCAmelCase : Any = RagRetriever(
a_ ,question_encoder_tokenizer=a_ ,generator_tokenizer=a_ ,index=a_ ,init_retrieval=a_ ,)
_UpperCAmelCase : str = True
def _snake_case ( self ) -> Optional[int]:
self.retriever.index.init_index()
def _snake_case ( self ,a_ ,a_ ) -> str:
_UpperCAmelCase ,_UpperCAmelCase : Any = self.retriever._main_retrieve(a_ ,a_ )
return doc_ids, retrieved_doc_embeds
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ ,a_ ,a_ ,a_=None ) -> int:
if index is not None and index.is_initialized() and len(a_ ) > 0:
raise ValueError(
"""When using Ray for distributed fine-tuning, """
"""you'll need to provide the paths instead, """
"""as the dataset and the index are loaded """
"""separately. More info in examples/rag/use_own_knowledge_dataset.py """ )
super().__init__(
a_ ,question_encoder_tokenizer=a_ ,generator_tokenizer=a_ ,index=a_ ,init_retrieval=a_ ,)
_UpperCAmelCase : List[str] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(a_ ,a_ ,a_ ,a_ )
for worker in self.retrieval_workers
] )
def _snake_case ( self ) -> List[Any]:
logger.info("""initializing retrieval""" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _snake_case ( self ,a_ ,a_ ) -> str:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
_UpperCAmelCase : Tuple = self.retrieval_workers[random.randint(0 ,len(self.retrieval_workers ) - 1 )]
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = ray.get(random_worker.retrieve.remote(a_ ,a_ ) )
else:
_UpperCAmelCase ,_UpperCAmelCase : int = self._main_retrieve(a_ ,a_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a_ )
@classmethod
def _snake_case ( cls ,a_ ,a_=None ,**a_ ) -> int:
return super(a_ ,cls ).get_tokenizers(a_ ,a_ ,**a_ )
@classmethod
def _snake_case ( cls ,a_ ,a_ ,a_=None ,**a_ ) -> Tuple:
_UpperCAmelCase : Optional[int] = kwargs.pop("""config""" ,a_ ) or RagConfig.from_pretrained(a_ ,**a_ )
_UpperCAmelCase : List[Any] = RagTokenizer.from_pretrained(a_ ,config=a_ )
_UpperCAmelCase : Dict = rag_tokenizer.question_encoder
_UpperCAmelCase : Union[str, Any] = rag_tokenizer.generator
if indexed_dataset is not None:
_UpperCAmelCase : List[str] = """custom"""
_UpperCAmelCase : List[Any] = CustomHFIndex(config.retrieval_vector_size ,a_ )
else:
_UpperCAmelCase : Dict = cls._build_index(a_ )
return cls(
a_ ,question_encoder_tokenizer=a_ ,generator_tokenizer=a_ ,retrieval_workers=a_ ,index=a_ ,)
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict = str(abs(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = [list(lowerCAmelCase_ ) for char in range(len(lowerCAmelCase_ ) )]
for index in range(len(lowerCAmelCase_ ) ):
num_transpositions[index].pop(lowerCAmelCase_ )
return max(
int("""""".join(list(lowerCAmelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
A_ : str = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """albert"""
def __init__( self ,a_=30_000 ,a_=128 ,a_=4_096 ,a_=12 ,a_=1 ,a_=64 ,a_=16_384 ,a_=1 ,a_="gelu_new" ,a_=0 ,a_=0 ,a_=512 ,a_=2 ,a_=0.02 ,a_=1E-1_2 ,a_=0.1 ,a_="absolute" ,a_=0 ,a_=2 ,a_=3 ,**a_ ,) -> List[Any]:
super().__init__(pad_token_id=a_ ,bos_token_id=a_ ,eos_token_id=a_ ,**a_ )
_UpperCAmelCase : Any = vocab_size
_UpperCAmelCase : str = embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : Dict = num_hidden_groups
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Any = inner_group_num
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Tuple = intermediate_size
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : int = attention_probs_dropout_prob
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Dict = initializer_range
_UpperCAmelCase : List[str] = layer_norm_eps
_UpperCAmelCase : int = classifier_dropout_prob
_UpperCAmelCase : Any = position_embedding_type
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : Dict = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Dict = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), F'''{len(lowerCAmelCase_ )} != {len(lowerCAmelCase_ )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A_ : Union[str, Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A_ : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
try:
_UpperCAmelCase : Any = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[int]:
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(lowerCAmelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = "student" , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ , )-> Tuple[PreTrainedModel, List[int], List[int]]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
AutoTokenizer.from_pretrained(lowerCAmelCase_ ).save_pretrained(lowerCAmelCase_ ) # purely for convenience
_UpperCAmelCase : Any = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_ ).eval()
else:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), F'''teacher must be a model or string got type {type(lowerCAmelCase_ )}'''
_UpperCAmelCase : str = teacher.config.to_diff_dict()
try:
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
_UpperCAmelCase : Tuple = teacher_e
if d is None:
_UpperCAmelCase : Dict = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
_UpperCAmelCase : List[str] = teacher_e
if d is None:
_UpperCAmelCase : str = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase_ )
# Copy weights
_UpperCAmelCase : Any = teacher.config_class(**lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
_UpperCAmelCase : Optional[Any] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = list(range(lowerCAmelCase_ ) ), list(range(lowerCAmelCase_ ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(lowerCAmelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
if d_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
try:
if hasattr(
lowerCAmelCase_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase_ )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
_UpperCAmelCase : Dict = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 349 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
A_ : int = logging.get_logger(__name__)
A_ : List[str] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
A_ : str = {
"""vocab_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
),
"""squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
),
},
}
A_ : List[Any] = {
"""squeezebert/squeezebert-uncased""": 5_1_2,
"""squeezebert/squeezebert-mnli""": 5_1_2,
"""squeezebert/squeezebert-mnli-headless""": 5_1_2,
}
A_ : Dict = {
"""squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = VOCAB_FILES_NAMES
UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase = SqueezeBertTokenizer
def __init__( self ,a_=None ,a_=None ,a_=True ,a_="[UNK]" ,a_="[SEP]" ,a_="[PAD]" ,a_="[CLS]" ,a_="[MASK]" ,a_=True ,a_=None ,**a_ ,) -> Optional[int]:
super().__init__(
a_ ,tokenizer_file=a_ ,do_lower_case=a_ ,unk_token=a_ ,sep_token=a_ ,pad_token=a_ ,cls_token=a_ ,mask_token=a_ ,tokenize_chinese_chars=a_ ,strip_accents=a_ ,**a_ ,)
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,a_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,a_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,a_ ) != tokenize_chinese_chars
):
_UpperCAmelCase : List[Any] = getattr(a_ ,normalizer_state.pop("""type""" ) )
_UpperCAmelCase : Optional[Any] = do_lower_case
_UpperCAmelCase : List[Any] = strip_accents
_UpperCAmelCase : Optional[Any] = tokenize_chinese_chars
_UpperCAmelCase : Tuple = normalizer_class(**a_ )
_UpperCAmelCase : str = do_lower_case
def _snake_case ( self ,a_ ,a_=None ) -> int:
_UpperCAmelCase : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _snake_case ( self ,a_ ,a_ = None ) -> List[int]:
_UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
_UpperCAmelCase : List[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 ) * [0] + len(token_ids_a + sep ) * [1]
def _snake_case ( self ,a_ ,a_ = None ) -> Tuple[str]:
_UpperCAmelCase : str = self._tokenizer.model.save(a_ ,name=a_ )
return tuple(a_ )
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase_ , lowerCAmelCase_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
A_ : Any = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
A_ : str = """main"""
# Default branch name
A_ : Tuple = """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
A_ : Optional[int] = """aaaaaaa"""
# This commit does not exist, so we should 404.
A_ : List[Any] = """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
A_ : List[str] = """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""
@contextlib.contextmanager
def snake_case_ ( )-> int:
'''simple docstring'''
print("""Welcome!""" )
yield
print("""Bye!""" )
@contextlib.contextmanager
def snake_case_ ( )-> Tuple:
'''simple docstring'''
print("""Bonjour!""" )
yield
print("""Au revoir!""" )
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> int:
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec("""transformers""" ) is not None
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@unittest.mock.patch("""sys.stdout""" ,new_callable=io.StringIO )
def _snake_case ( self ,a_ ) -> int:
with ContextManagers([] ):
print("""Transformers are awesome!""" )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() ,"""Transformers are awesome!\n""" )
@unittest.mock.patch("""sys.stdout""" ,new_callable=io.StringIO )
def _snake_case ( self ,a_ ) -> List[Any]:
with ContextManagers([context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() ,"""Welcome!\nTransformers are awesome!\nBye!\n""" )
@unittest.mock.patch("""sys.stdout""" ,new_callable=io.StringIO )
def _snake_case ( self ,a_ ) -> Dict:
with ContextManagers([context_fr(), context_en()] ):
print("""Transformers are awesome!""" )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() ,"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n""" )
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
self.assertEqual(find_labels(a_ ) ,["""labels"""] )
self.assertEqual(find_labels(a_ ) ,["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(a_ ) ,["""start_positions""", """end_positions"""] )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
pass
self.assertEqual(find_labels(a_ ) ,["""labels"""] )
@require_tf
def _snake_case ( self ) -> List[str]:
self.assertEqual(find_labels(a_ ) ,["""labels"""] )
self.assertEqual(find_labels(a_ ) ,["""labels""", """next_sentence_label"""] )
self.assertEqual(find_labels(a_ ) ,["""start_positions""", """end_positions"""] )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
pass
self.assertEqual(find_labels(a_ ) ,["""labels"""] )
@require_flax
def _snake_case ( self ) -> int:
# Flax models don't have labels
self.assertEqual(find_labels(a_ ) ,[] )
self.assertEqual(find_labels(a_ ) ,[] )
self.assertEqual(find_labels(a_ ) ,[] )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
pass
self.assertEqual(find_labels(a_ ) ,[] )
| 349 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_UpperCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter Video/IGTV url: """).strip()
A_ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 349 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_=7 ,a_=3 ,a_=18 ,a_=30 ,a_=400 ,a_=True ,a_=None ,a_=True ,) -> Any:
_UpperCAmelCase : List[str] = size if size is not None else {"""height""": 18, """width""": 18}
_UpperCAmelCase : Any = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = image_size
_UpperCAmelCase : int = min_resolution
_UpperCAmelCase : Union[str, Any] = max_resolution
_UpperCAmelCase : List[str] = do_resize
_UpperCAmelCase : Union[str, Any] = size
_UpperCAmelCase : Tuple = do_normalize
def _snake_case ( self ) -> Optional[int]:
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
[-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class lowercase ( _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = ImageGPTImageProcessor if is_vision_available() else None
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Optional[int] = ImageGPTImageProcessingTester(self )
@property
def _snake_case ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case ( self ) -> str:
_UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ ,"""clusters""" ) )
self.assertTrue(hasattr(a_ ,"""do_resize""" ) )
self.assertTrue(hasattr(a_ ,"""size""" ) )
self.assertTrue(hasattr(a_ ,"""do_normalize""" ) )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : str = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase : List[Any] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(a_ ,obj[key] ) )
else:
self.assertEqual(obj[key] ,a_ )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[int] = os.path.join(a_ ,"""image_processor.json""" )
image_processor_first.to_json_file(a_ )
_UpperCAmelCase : Any = self.image_processing_class.from_json_file(a_ ).to_dict()
_UpperCAmelCase : List[Any] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(a_ ,image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] ,a_ )
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(a_ )
_UpperCAmelCase : Dict = self.image_processing_class.from_pretrained(a_ ).to_dict()
_UpperCAmelCase : Union[str, Any] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(a_ ,image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] ,a_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def _snake_case ( self ) -> Dict:
pass
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
_UpperCAmelCase : int = Image.open(dataset[4]["""file"""] )
_UpperCAmelCase : Optional[Any] = Image.open(dataset[5]["""file"""] )
_UpperCAmelCase : Optional[int] = [imagea, imagea]
return images
@require_vision
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Tuple = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
_UpperCAmelCase : List[str] = prepare_images()
# test non-batched
_UpperCAmelCase : List[Any] = image_processing(images[0] ,return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids ,torch.LongTensor )
self.assertEqual(encoding.input_ids.shape ,(1, 1_024) )
_UpperCAmelCase : Dict = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() ,a_ )
# test batched
_UpperCAmelCase : int = image_processing(a_ ,return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids ,torch.LongTensor )
self.assertEqual(encoding.input_ids.shape ,(2, 1_024) )
_UpperCAmelCase : Union[str, Any] = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() ,a_ )
| 349 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Union[str, Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(a_ )
return image
@property
def _snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def _snake_case ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def _snake_case ( self ) -> Dict:
torch.manual_seed(0 )
_UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
return CLIPTextModel(a_ )
@property
def _snake_case ( self ) -> Union[str, Any]:
def extract(*a_ ,**a_ ):
class lowercase :
"""simple docstring"""
def __init__( self ) -> Any:
_UpperCAmelCase : str = torch.ones([0] )
def _snake_case ( self ,a_ ) -> Any:
self.pixel_values.to(a_ )
return self
return Out()
return extract
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Union[str, Any] = self.dummy_cond_unet
_UpperCAmelCase : int = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
_UpperCAmelCase : Optional[int] = self.dummy_vae
_UpperCAmelCase : Optional[int] = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : int = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Tuple = self.dummy_cond_unet
_UpperCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : int = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : str = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : List[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=a_ )
assert isinstance(a_ ,a_ )
assert isinstance(pipe.scheduler ,a_ )
assert pipe.safety_checker is None
_UpperCAmelCase : Dict = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained(a_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase : Union[str, Any] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.dummy_cond_unet
_UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : List[str] = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_UpperCAmelCase : str = unet.half()
_UpperCAmelCase : List[str] = vae.half()
_UpperCAmelCase : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : Dict = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : int = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : List[Any] = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCAmelCase : Any = 4_003_660_346
_UpperCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCAmelCase : Optional[Any] = 2_734_971_755
_UpperCAmelCase : Optional[int] = 7
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[int] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCAmelCase : Optional[int] = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Union[str, Any] = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCAmelCase : Dict = 1_044_355_234
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : List[str] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCAmelCase : Tuple = torch.manual_seed(a_ )
_UpperCAmelCase : Dict = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowercase :
"""simple docstring"""
def __init__( self ) -> Dict:
_UpperCAmelCase : int = {}
def _snake_case ( self ,a_ ,a_ ,a_=1 ) -> Optional[int]:
if self.graph.get(a_ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
_UpperCAmelCase : Tuple = [[w, v]]
if not self.graph.get(a_ ):
_UpperCAmelCase : Optional[Any] = []
def _snake_case ( self ) -> Optional[Any]:
return list(self.graph )
def _snake_case ( self ,a_ ,a_ ) -> int:
if self.graph.get(a_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(a_ )
def _snake_case ( self ,a_=-2 ,a_=-1 ) -> Any:
if s == d:
return []
_UpperCAmelCase : Dict = []
_UpperCAmelCase : List[Any] = []
if s == -2:
_UpperCAmelCase : Dict = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(a_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(a_ ) != 0:
_UpperCAmelCase : Optional[Any] = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : int = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return visited
def _snake_case ( self ,a_=-1 ) -> Union[str, Any]:
if c == -1:
_UpperCAmelCase : str = floor(random() * 10_000 ) + 10
for i in range(a_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_UpperCAmelCase : Optional[int] = floor(random() * c ) + 1
if n != i:
self.add_pair(a_ ,a_ ,1 )
def _snake_case ( self ,a_=-2 ) -> str:
_UpperCAmelCase : Any = deque()
_UpperCAmelCase : int = []
if s == -2:
_UpperCAmelCase : Dict = list(self.graph )[0]
d.append(a_ )
visited.append(a_ )
while d:
_UpperCAmelCase : Optional[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _snake_case ( self ,a_ ) -> Optional[int]:
_UpperCAmelCase : List[str] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def _snake_case ( self ,a_ ) -> Optional[Any]:
return len(self.graph[u] )
def _snake_case ( self ,a_=-2 ) -> int:
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Dict = []
if s == -2:
_UpperCAmelCase : Optional[int] = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : int = s
_UpperCAmelCase : int = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(a_ ) != 0:
_UpperCAmelCase : Any = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return sorted_nodes
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : int = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : Union[str, Any] = -2
_UpperCAmelCase : str = []
_UpperCAmelCase : Union[str, Any] = s
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : str = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_UpperCAmelCase : List[Any] = len(a_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_UpperCAmelCase : Tuple = True
if len(a_ ) != 0:
_UpperCAmelCase : str = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : Any = False
indirect_parents.append(a_ )
_UpperCAmelCase : Optional[int] = s
_UpperCAmelCase : List[Any] = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return list(a_ )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Optional[Any] = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : Dict = -2
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Dict = s
_UpperCAmelCase : str = False
_UpperCAmelCase : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Union[str, Any] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_UpperCAmelCase : int = len(a_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_UpperCAmelCase : Dict = True
if len(a_ ) != 0:
_UpperCAmelCase : Optional[int] = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : Union[str, Any] = False
indirect_parents.append(a_ )
_UpperCAmelCase : Any = s
_UpperCAmelCase : Any = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return False
def _snake_case ( self ,a_=-2 ,a_=-1 ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = time()
self.dfs(a_ ,a_ )
_UpperCAmelCase : Dict = time()
return end - begin
def _snake_case ( self ,a_=-2 ) -> int:
_UpperCAmelCase : int = time()
self.bfs(a_ )
_UpperCAmelCase : Union[str, Any] = time()
return end - begin
class lowercase :
"""simple docstring"""
def __init__( self ) -> str:
_UpperCAmelCase : List[Any] = {}
def _snake_case ( self ,a_ ,a_ ,a_=1 ) -> Union[str, Any]:
# check if the u exists
if self.graph.get(a_ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
_UpperCAmelCase : Optional[int] = [[w, v]]
# add the other way
if self.graph.get(a_ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
_UpperCAmelCase : Optional[int] = [[w, u]]
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
if self.graph.get(a_ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(a_ )
# the other way round
if self.graph.get(a_ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(a_ )
def _snake_case ( self ,a_=-2 ,a_=-1 ) -> Tuple:
if s == d:
return []
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Tuple = []
if s == -2:
_UpperCAmelCase : Dict = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Optional[int] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(a_ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(a_ ) != 0:
_UpperCAmelCase : Tuple = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : int = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return visited
def _snake_case ( self ,a_=-1 ) -> List[Any]:
if c == -1:
_UpperCAmelCase : Optional[int] = floor(random() * 10_000 ) + 10
for i in range(a_ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_UpperCAmelCase : List[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(a_ ,a_ ,1 )
def _snake_case ( self ,a_=-2 ) -> int:
_UpperCAmelCase : Optional[int] = deque()
_UpperCAmelCase : Any = []
if s == -2:
_UpperCAmelCase : Tuple = list(self.graph )[0]
d.append(a_ )
visited.append(a_ )
while d:
_UpperCAmelCase : str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _snake_case ( self ,a_ ) -> Tuple:
return len(self.graph[u] )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Union[str, Any] = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : Tuple = -2
_UpperCAmelCase : Optional[Any] = []
_UpperCAmelCase : Optional[Any] = s
_UpperCAmelCase : int = False
_UpperCAmelCase : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_UpperCAmelCase : Union[str, Any] = len(a_ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : Dict = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_UpperCAmelCase : Any = True
if len(a_ ) != 0:
_UpperCAmelCase : Any = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : Tuple = False
indirect_parents.append(a_ )
_UpperCAmelCase : Dict = s
_UpperCAmelCase : str = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return list(a_ )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Tuple = list(self.graph )[0]
stack.append(a_ )
visited.append(a_ )
_UpperCAmelCase : Dict = -2
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : str = s
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : Optional[int] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_UpperCAmelCase : Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_UpperCAmelCase : List[str] = len(a_ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_UpperCAmelCase : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_UpperCAmelCase : Optional[int] = True
if len(a_ ) != 0:
_UpperCAmelCase : Tuple = stack[len(a_ ) - 1]
else:
_UpperCAmelCase : Dict = False
indirect_parents.append(a_ )
_UpperCAmelCase : List[str] = s
_UpperCAmelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(a_ ) == 0:
return False
def _snake_case ( self ) -> List[Any]:
return list(self.graph )
def _snake_case ( self ,a_=-2 ,a_=-1 ) -> Optional[int]:
_UpperCAmelCase : Any = time()
self.dfs(a_ ,a_ )
_UpperCAmelCase : Optional[int] = time()
return end - begin
def _snake_case ( self ,a_=-2 ) -> Dict:
_UpperCAmelCase : Dict = time()
self.bfs(a_ )
_UpperCAmelCase : Union[str, Any] = time()
return end - begin
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
A_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(_lowerCamelCase )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,**a_ ) -> Optional[Any]:
super().__init__(**a_ )
requires_backends(self ,"""vision""" )
requires_backends(self ,"""torch""" )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
self.check_model_type(a_ )
def _snake_case ( self ,**a_ ) -> Tuple:
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : int = {}
# preprocess args
if "points_per_batch" in kwargs:
_UpperCAmelCase : List[str] = kwargs["""points_per_batch"""]
if "points_per_crop" in kwargs:
_UpperCAmelCase : List[str] = kwargs["""points_per_crop"""]
if "crops_n_layers" in kwargs:
_UpperCAmelCase : Optional[int] = kwargs["""crops_n_layers"""]
if "crop_overlap_ratio" in kwargs:
_UpperCAmelCase : Union[str, Any] = kwargs["""crop_overlap_ratio"""]
if "crop_n_points_downscale_factor" in kwargs:
_UpperCAmelCase : Optional[Any] = kwargs["""crop_n_points_downscale_factor"""]
# postprocess args
if "pred_iou_thresh" in kwargs:
_UpperCAmelCase : int = kwargs["""pred_iou_thresh"""]
if "stability_score_offset" in kwargs:
_UpperCAmelCase : Dict = kwargs["""stability_score_offset"""]
if "mask_threshold" in kwargs:
_UpperCAmelCase : Union[str, Any] = kwargs["""mask_threshold"""]
if "stability_score_thresh" in kwargs:
_UpperCAmelCase : List[Any] = kwargs["""stability_score_thresh"""]
if "crops_nms_thresh" in kwargs:
_UpperCAmelCase : Union[str, Any] = kwargs["""crops_nms_thresh"""]
if "output_rle_mask" in kwargs:
_UpperCAmelCase : Optional[Any] = kwargs["""output_rle_mask"""]
if "output_bboxes_mask" in kwargs:
_UpperCAmelCase : Union[str, Any] = kwargs["""output_bboxes_mask"""]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self ,a_ ,*a_ ,a_=None ,a_=None ,**a_ ) -> Union[str, Any]:
return super().__call__(a_ ,*a_ ,num_workers=a_ ,batch_size=a_ ,**a_ )
def _snake_case ( self ,a_ ,a_=64 ,a_ = 0 ,a_ = 512 / 1_500 ,a_ = 32 ,a_ = 1 ,) -> int:
_UpperCAmelCase : Any = load_image(a_ )
_UpperCAmelCase : Dict = self.image_processor.size["""longest_edge"""]
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Tuple = self.image_processor.generate_crop_boxes(
a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
_UpperCAmelCase : Union[str, Any] = self.image_processor(images=a_ ,return_tensors="""pt""" )
with self.device_placement():
if self.framework == "pt":
_UpperCAmelCase : Optional[Any] = self.get_inference_context()
with inference_context():
_UpperCAmelCase : int = self._ensure_tensor_on_device(a_ ,device=self.device )
_UpperCAmelCase : Dict = self.model.get_image_embeddings(model_inputs.pop("""pixel_values""" ) )
_UpperCAmelCase : str = image_embeddings
_UpperCAmelCase : Optional[int] = grid_points.shape[1]
_UpperCAmelCase : Any = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
"""Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
"""To return all points at once, set points_per_batch to None""" )
for i in range(0 ,a_ ,a_ ):
_UpperCAmelCase : Any = grid_points[:, i : i + points_per_batch, :, :]
_UpperCAmelCase : List[str] = input_labels[:, i : i + points_per_batch]
_UpperCAmelCase : str = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _snake_case ( self ,a_ ,a_=0.88 ,a_=0.95 ,a_=0 ,a_=1 ,) -> Any:
_UpperCAmelCase : Optional[int] = model_inputs.pop("""input_boxes""" )
_UpperCAmelCase : Union[str, Any] = model_inputs.pop("""is_last""" )
_UpperCAmelCase : List[Any] = model_inputs.pop("""original_sizes""" ).tolist()
_UpperCAmelCase : Tuple = model_inputs.pop("""reshaped_input_sizes""" ).tolist()
_UpperCAmelCase : Optional[Any] = self.model(**a_ )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
_UpperCAmelCase : Union[str, Any] = model_outputs["""pred_masks"""]
_UpperCAmelCase : List[Any] = self.image_processor.post_process_masks(
a_ ,a_ ,a_ ,a_ ,binarize=a_ )
_UpperCAmelCase : Any = model_outputs["""iou_scores"""]
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = self.image_processor.filter_masks(
masks[0] ,iou_scores[0] ,original_sizes[0] ,input_boxes[0] ,a_ ,a_ ,a_ ,a_ ,)
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _snake_case ( self ,a_ ,a_=False ,a_=False ,a_=0.7 ,) -> Dict:
_UpperCAmelCase : str = []
_UpperCAmelCase : Any = []
_UpperCAmelCase : str = []
for model_output in model_outputs:
all_scores.append(model_output.pop("""iou_scores""" ) )
all_masks.extend(model_output.pop("""masks""" ) )
all_boxes.append(model_output.pop("""boxes""" ) )
_UpperCAmelCase : Any = torch.cat(a_ )
_UpperCAmelCase : int = torch.cat(a_ )
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : List[Any] = self.image_processor.post_process_for_mask_generation(
a_ ,a_ ,a_ ,a_ )
_UpperCAmelCase : List[Any] = defaultdict(a_ )
for output in model_outputs:
for k, v in output.items():
extra[k].append(a_ )
_UpperCAmelCase : Optional[int] = {}
if output_rle_mask:
_UpperCAmelCase : Tuple = rle_mask
if output_bboxes_mask:
_UpperCAmelCase : Dict = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
A_ : Dict = 8
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=BITS )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Dict = x.device
_UpperCAmelCase : int = (x * 255).int().clamp(0 , 255 )
_UpperCAmelCase : Tuple = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = rearrange(lowerCAmelCase_ , """d -> d 1 1""" )
_UpperCAmelCase : List[Any] = rearrange(lowerCAmelCase_ , """b c h w -> b c 1 h w""" )
_UpperCAmelCase : Optional[Any] = ((x & mask) != 0).float()
_UpperCAmelCase : int = rearrange(lowerCAmelCase_ , """b c d h w -> b (c d) h w""" )
_UpperCAmelCase : Tuple = bits * 2 - 1
return bits
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=BITS )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = x.device
_UpperCAmelCase : Optional[int] = (x > 0).int()
_UpperCAmelCase : Any = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowerCAmelCase_ , dtype=torch.intaa )
_UpperCAmelCase : Tuple = rearrange(lowerCAmelCase_ , """d -> d 1 1""" )
_UpperCAmelCase : Dict = rearrange(lowerCAmelCase_ , """b (c d) h w -> b c d h w""" , d=8 )
_UpperCAmelCase : Optional[Any] = reduce(x * mask , """b c d h w -> b c h w""" , """sum""" )
return (dec / 255).clamp(0.0 , 1.0 )
def snake_case_ ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = True , lowerCAmelCase_=None , lowerCAmelCase_ = True , )-> Union[DDIMSchedulerOutput, Tuple]:
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
_UpperCAmelCase : str = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
_UpperCAmelCase : List[str] = self.alphas_cumprod[timestep]
_UpperCAmelCase : List[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
_UpperCAmelCase : List[Any] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase : List[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
_UpperCAmelCase : List[Any] = self.bit_scale
if self.config.clip_sample:
_UpperCAmelCase : Union[str, Any] = torch.clamp(lowerCAmelCase_ , -scale , lowerCAmelCase_ )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
_UpperCAmelCase : Union[str, Any] = self._get_variance(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : int = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
_UpperCAmelCase : str = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase : Optional[int] = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase : Any = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
_UpperCAmelCase : Dict = model_output.device if torch.is_tensor(lowerCAmelCase_ ) else """cpu"""
_UpperCAmelCase : Union[str, Any] = torch.randn(model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase_ ).to(lowerCAmelCase_ )
_UpperCAmelCase : int = self._get_variance(lowerCAmelCase_ , lowerCAmelCase_ ) ** 0.5 * eta * noise
_UpperCAmelCase : Tuple = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_ , pred_original_sample=lowerCAmelCase_ )
def snake_case_ ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="epsilon" , lowerCAmelCase_=None , lowerCAmelCase_ = True , )-> Union[DDPMSchedulerOutput, Tuple]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
_UpperCAmelCase ,_UpperCAmelCase : Any = torch.split(lowerCAmelCase_ , sample.shape[1] , dim=1 )
else:
_UpperCAmelCase : List[Any] = None
# 1. compute alphas, betas
_UpperCAmelCase : Union[str, Any] = self.alphas_cumprod[t]
_UpperCAmelCase : int = self.alphas_cumprod[t - 1] if t > 0 else self.one
_UpperCAmelCase : Tuple = 1 - alpha_prod_t
_UpperCAmelCase : List[str] = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
_UpperCAmelCase : Optional[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
_UpperCAmelCase : str = model_output
else:
raise ValueError(F'''Unsupported prediction_type {prediction_type}.''' )
# 3. Clip "predicted x_0"
_UpperCAmelCase : int = self.bit_scale
if self.config.clip_sample:
_UpperCAmelCase : Any = torch.clamp(lowerCAmelCase_ , -scale , lowerCAmelCase_ )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCAmelCase : Any = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
_UpperCAmelCase : int = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCAmelCase : Any = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_UpperCAmelCase : List[str] = 0
if t > 0:
_UpperCAmelCase : List[Any] = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=lowerCAmelCase_ ).to(model_output.device )
_UpperCAmelCase : Tuple = (self._get_variance(lowerCAmelCase_ , predicted_variance=lowerCAmelCase_ ) ** 0.5) * noise
_UpperCAmelCase : List[str] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_ , pred_original_sample=lowerCAmelCase_ )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ ,a_ = 1.0 ,) -> Any:
super().__init__()
_UpperCAmelCase : List[Any] = bit_scale
_UpperCAmelCase : Any = (
ddim_bit_scheduler_step if isinstance(a_ ,a_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=a_ ,scheduler=a_ )
@torch.no_grad()
def __call__( self ,a_ = 256 ,a_ = 256 ,a_ = 50 ,a_ = None ,a_ = 1 ,a_ = "pil" ,a_ = True ,**a_ ,) -> Union[Tuple, ImagePipelineOutput]:
_UpperCAmelCase : int = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) ,generator=a_ ,)
_UpperCAmelCase : int = decimal_to_bits(a_ ) * self.bit_scale
_UpperCAmelCase : int = latents.to(self.device )
self.scheduler.set_timesteps(a_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
_UpperCAmelCase : Union[str, Any] = self.unet(a_ ,a_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase : Optional[Any] = self.scheduler.step(a_ ,a_ ,a_ ).prev_sample
_UpperCAmelCase : List[str] = bits_to_decimal(a_ )
if output_type == "pil":
_UpperCAmelCase : List[str] = self.numpy_to_pil(a_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a_ )
| 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Dict = [2, 4, 6, 8, 10, 12]
_UpperCAmelCase : Optional[int] = 100
self.assertEqual(kp.calc_profit(a_ ,a_ ,a_ ) ,210 )
def _snake_case ( self ) -> Union[str, Any]:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Any:
self.assertRaisesRegex(a_ ,"""Weight can not be negative.""" )
def _snake_case ( self ) -> Optional[Any]:
self.assertRaisesRegex(a_ ,"""Profit can not be negative.""" )
def _snake_case ( self ) -> Dict:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Tuple:
self.assertRaisesRegex(
a_ ,"""The length of profit and weight must be same.""" )
if __name__ == "__main__":
unittest.main()
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
A_ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> tuple[list[list[int]], list[list[int]]]:
'''simple docstring'''
_UpperCAmelCase : str = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase_ ) )
] # the reference grid
_UpperCAmelCase : Any = 1
_UpperCAmelCase : List[str] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(lowerCAmelCase_ ) )
] # the action grid
_UpperCAmelCase : str = init[0]
_UpperCAmelCase : Tuple = init[1]
_UpperCAmelCase : str = 0
_UpperCAmelCase : List[str] = g + heuristic[x][y] # cost from starting cell to destination cell
_UpperCAmelCase : List[str] = [[f, g, x, y]]
_UpperCAmelCase : Dict = False # flag that is set when search is complete
_UpperCAmelCase : Dict = False # flag set if we can't find expand
while not found and not resign:
if len(lowerCAmelCase_ ) == 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()
_UpperCAmelCase : str = cell.pop()
_UpperCAmelCase : Tuple = next_cell[2]
_UpperCAmelCase : Optional[int] = next_cell[3]
_UpperCAmelCase : Any = next_cell[1]
if x == goal[0] and y == goal[1]:
_UpperCAmelCase : Tuple = True
else:
for i in range(len(lowerCAmelCase_ ) ): # to try out different valid actions
_UpperCAmelCase : Dict = x + DIRECTIONS[i][0]
_UpperCAmelCase : Optional[Any] = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(lowerCAmelCase_ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
_UpperCAmelCase : int = g + cost
_UpperCAmelCase : Dict = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
_UpperCAmelCase : Dict = 1
_UpperCAmelCase : Optional[Any] = i
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Any = goal[0]
_UpperCAmelCase : List[Any] = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
_UpperCAmelCase : Union[str, Any] = x - DIRECTIONS[action[x][y]][0]
_UpperCAmelCase : Optional[int] = y - DIRECTIONS[action[x][y]][1]
_UpperCAmelCase : str = xa
_UpperCAmelCase : int = ya
invpath.append([x, y] )
_UpperCAmelCase : Optional[Any] = []
for i in range(len(lowerCAmelCase_ ) ):
path.append(invpath[len(lowerCAmelCase_ ) - 1 - i] )
return path, action
if __name__ == "__main__":
A_ : Optional[Any] = [
[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],
]
A_ : int = [0, 0]
# all coordinates are given in format [y,x]
A_ : Optional[int] = [len(grid) - 1, len(grid[0]) - 1]
A_ : Any = 1
# the cost map which pushes the path closer to the goal
A_ : Optional[Any] = [[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])):
A_ : Optional[Any] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
A_ : str = 9_9
A_ , A_ : str = 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])
| 349 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_UpperCAmelCase : Optional[int] = flax_key_tuple[:-1] + ("""weight""",)
_UpperCAmelCase : Tuple = torch.permute(lowerCAmelCase_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase_ ):
# linear layer
_UpperCAmelCase : Optional[int] = flax_key_tuple[:-1] + ("""weight""",)
_UpperCAmelCase : Tuple = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_UpperCAmelCase : List[Any] = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
if "metadata" in layer:
_UpperCAmelCase : List[str] = layer.split("""metadata""" )
_UpperCAmelCase : int = """""".join(split_layer[0] )[:-1]
_UpperCAmelCase : Any = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
_UpperCAmelCase : Tuple = layer.split("""kvstore""" )
_UpperCAmelCase : List[str] = """""".join(split_layer[0] )[:-1]
_UpperCAmelCase : Dict = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
_UpperCAmelCase : str = layer.split("""/""" )
_UpperCAmelCase : Optional[int] = """/""".join(split_layer[:-1] )
_UpperCAmelCase : int = (split_layer[-1],)
if "kvstore/path" in layer:
_UpperCAmelCase : Union[str, Any] = F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
_UpperCAmelCase : Any = """file"""
else:
_UpperCAmelCase : Optional[int] = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = rename_keys(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = {}
for k, v in current_block.items():
_UpperCAmelCase : List[str] = v
_UpperCAmelCase : Optional[int] = new_current_block
torch.save(lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = WEIGHTS_NAME )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = convert_file_size_to_int(lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = []
_UpperCAmelCase : str = {}
_UpperCAmelCase : Optional[int] = 0
_UpperCAmelCase : List[Any] = 0
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
_UpperCAmelCase : List[str] = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
_UpperCAmelCase : Dict = flatten_dict(lowerCAmelCase_ , sep="""/""" )
_UpperCAmelCase : List[str] = {}
for layer in checkpoint_info.keys():
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : int = get_key_and_tensorstore_dict(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if curr_real_layer_name in all_layers:
_UpperCAmelCase : str = content
else:
_UpperCAmelCase : Any = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_UpperCAmelCase : str = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_UpperCAmelCase : List[str] = torch.tensor(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = rename_base_flax_keys(tuple(key.split("""/""" ) ) , lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = """/""".join(lowerCAmelCase_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_UpperCAmelCase : List[str] = os.path.join(
lowerCAmelCase_ , weights_name.replace(""".bin""" , F'''-{len(lowerCAmelCase_ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(lowerCAmelCase_ , lowerCAmelCase_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
_UpperCAmelCase : List[Any] = {}
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Tuple = raw_weights.to(getattr(lowerCAmelCase_ , lowerCAmelCase_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_UpperCAmelCase : Tuple = os.path.join(lowerCAmelCase_ , weights_name.replace(""".bin""" , F'''-{len(lowerCAmelCase_ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(lowerCAmelCase_ , lowerCAmelCase_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(lowerCAmelCase_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_UpperCAmelCase : List[str] = {}
_UpperCAmelCase : str = {}
for idx, shard in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[int] = weights_name.replace(
""".bin""" , F'''-{idx+1:05d}-of-{len(lowerCAmelCase_ ):05d}.bin''' ) # len(sharded_state_dicts):05d}
_UpperCAmelCase : Optional[int] = os.path.join(lowerCAmelCase_ , weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[int] = shard
for key in shard:
_UpperCAmelCase : List[Any] = shard_file
# Add the metadata
_UpperCAmelCase : str = {"""total_size""": total_size}
_UpperCAmelCase : Tuple = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) , """w""" , encoding="""utf-8""" ) as f:
_UpperCAmelCase : Tuple = json.dumps(lowerCAmelCase_ , indent=2 , sort_keys=lowerCAmelCase_ ) + """\n"""
f.write(lowerCAmelCase_ )
return metadata, index
if __name__ == "__main__":
A_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
A_ : str = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def snake_case_ ( )-> Tuple:
'''simple docstring'''
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_UpperCAmelCase : Tuple = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
_UpperCAmelCase : int = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
_UpperCAmelCase : Dict = TaTokenizer.from_pretrained("""t5-small""" )
_UpperCAmelCase : int = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
_UpperCAmelCase : List[str] = tokenizer(lowerCAmelCase_ , return_tensors="""pt""" ).input_ids
_UpperCAmelCase : Any = model.generate(lowerCAmelCase_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 349 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = int(np.ceil((x_end - xa) / h ) )
_UpperCAmelCase : Optional[Any] = np.zeros((n + 1,) )
_UpperCAmelCase : Any = ya
_UpperCAmelCase : Dict = xa
for k in range(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = f(lowerCAmelCase_ , y[k] )
_UpperCAmelCase : Dict = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_UpperCAmelCase : Dict = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_UpperCAmelCase : Union[str, Any] = f(x + h , y[k] + h * ka )
_UpperCAmelCase : Optional[int] = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False )-> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : Tuple = len(set_a.intersection(lowerCAmelCase_ ) )
if alternative_union:
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase_ ) + len(lowerCAmelCase_ )
else:
_UpperCAmelCase : Union[str, Any] = len(set_a.union(lowerCAmelCase_ ) )
return intersection / union
if isinstance(lowerCAmelCase_ , (list, tuple) ) and isinstance(lowerCAmelCase_ , (list, tuple) ):
_UpperCAmelCase : Any = [element for element in set_a if element in set_b]
if alternative_union:
_UpperCAmelCase : List[Any] = len(lowerCAmelCase_ ) + len(lowerCAmelCase_ )
return len(lowerCAmelCase_ ) / union
else:
_UpperCAmelCase : str = set_a + [element for element in set_b if element not in set_a]
return len(lowerCAmelCase_ ) / len(lowerCAmelCase_ )
return len(lowerCAmelCase_ ) / len(lowerCAmelCase_ )
return None
if __name__ == "__main__":
A_ : List[str] = {"""a""", """b""", """c""", """d""", """e"""}
A_ : Tuple = {"""c""", """d""", """e""", """f""", """h""", """i"""}
print(jaccard_similarity(set_a, set_b))
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A_ : Dict = get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")
@require_sentencepiece
@require_tokenizers
class lowercase ( _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = PegasusTokenizer
UpperCAmelCase = PegasusTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def _snake_case ( self ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Any = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _snake_case ( self ) -> str:
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def _snake_case ( self ,**a_ ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname ,**a_ )
def _snake_case ( self ,a_ ) -> int:
return ("This is a test", "This is a test")
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Tuple = """</s>"""
_UpperCAmelCase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) ,a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) ,a_ )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<pad>""" )
self.assertEqual(vocab_keys[1] ,"""</s>""" )
self.assertEqual(vocab_keys[-1] ,"""v""" )
self.assertEqual(len(a_ ) ,1_103 )
def _snake_case ( self ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size ,1_103 )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Tuple = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase : Tuple = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase : List[Any] = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
_UpperCAmelCase : Tuple = rust_tokenizer([raw_input_str] ,return_tensors=a_ ,add_special_tokens=a_ ).input_ids[0]
_UpperCAmelCase : str = py_tokenizer([raw_input_str] ,return_tensors=a_ ,add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ ,a_ )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : str = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase : Optional[int] = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
_UpperCAmelCase : Optional[int] = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
_UpperCAmelCase : List[Any] = tokenizer([raw_input_str] ,return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ ,a_ )
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
_UpperCAmelCase : Tuple = """To ensure a smooth flow of bank resolutions."""
_UpperCAmelCase : Dict = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
_UpperCAmelCase : List[str] = tokenizer([raw_input_str] ,return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ ,a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Optional[int] = ["""This is going to be way too long.""" * 150, """short example"""]
_UpperCAmelCase : Any = ["""not super long but more than 5 tokens""", """tiny"""]
_UpperCAmelCase : Optional[int] = self._large_tokenizer(a_ ,padding=a_ ,truncation=a_ ,return_tensors="""pt""" )
_UpperCAmelCase : int = self._large_tokenizer(
text_target=a_ ,max_length=5 ,padding=a_ ,truncation=a_ ,return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def _snake_case ( self ) -> int:
# fmt: off
_UpperCAmelCase : List[Any] = {"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ ,model_name="""google/bigbird-pegasus-large-arxiv""" ,revision="""ba85d0851d708441f91440d509690f1ab6353415""" ,)
@require_sentencepiece
@require_tokenizers
class lowercase ( _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = PegasusTokenizer
UpperCAmelCase = PegasusTokenizerFast
UpperCAmelCase = True
UpperCAmelCase = True
def _snake_case ( self ) -> Optional[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : List[Any] = PegasusTokenizer(a_ ,offset=0 ,mask_token_sent=a_ ,mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _snake_case ( self ) -> str:
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def _snake_case ( self ,**a_ ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname ,**a_ )
def _snake_case ( self ,a_ ) -> Union[str, Any]:
return ("This is a test", "This is a test")
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase : List[str] = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase : Dict = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
_UpperCAmelCase : Tuple = rust_tokenizer([raw_input_str] ,return_tensors=a_ ,add_special_tokens=a_ ).input_ids[0]
_UpperCAmelCase : Optional[int] = py_tokenizer([raw_input_str] ,return_tensors=a_ ,add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ ,a_ )
@require_torch
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = ["""This is going to be way too long.""" * 1_000, """short example"""]
_UpperCAmelCase : Tuple = ["""not super long but more than 5 tokens""", """tiny"""]
_UpperCAmelCase : int = self._large_tokenizer(a_ ,padding=a_ ,truncation=a_ ,return_tensors="""pt""" )
_UpperCAmelCase : int = self._large_tokenizer(
text_target=a_ ,max_length=5 ,padding=a_ ,truncation=a_ ,return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
_UpperCAmelCase : List[Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ ,[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] ,)
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Any = sorted(zip(lowerCAmelCase_ , lowerCAmelCase_ ) , key=lambda lowerCAmelCase_ : x[0] / x[1] , reverse=lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = [i[0] for i in r], [i[1] for i in r]
_UpperCAmelCase : int = list(accumulate(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = bisect(lowerCAmelCase_ , lowerCAmelCase_ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
A_ : str = 4
A_ : List[Any] = 3
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
pass
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
for shard in shards:
for i in range(lowerCAmelCase_ ):
yield {"i": i, "shard": shard}
def snake_case_ ( )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : int = int(os.environ["""RANK"""] )
_UpperCAmelCase : int = int(os.environ["""WORLD_SIZE"""] )
_UpperCAmelCase : Optional[Any] = ArgumentParser()
parser.add_argument("""--streaming""" , type=lowerCAmelCase_ )
parser.add_argument("""--local_rank""" , type=lowerCAmelCase_ )
parser.add_argument("""--num_workers""" , type=lowerCAmelCase_ , default=0 )
_UpperCAmelCase : str = parser.parse_args()
_UpperCAmelCase : Optional[int] = args.streaming
_UpperCAmelCase : Optional[Any] = args.num_workers
_UpperCAmelCase : Optional[int] = {"""shards""": [F'''shard_{shard_idx}''' for shard_idx in range(lowerCAmelCase_ )]}
_UpperCAmelCase : Any = IterableDataset.from_generator(lowerCAmelCase_ , gen_kwargs=lowerCAmelCase_ )
if not streaming:
_UpperCAmelCase : Optional[Any] = Dataset.from_list(list(lowerCAmelCase_ ) )
_UpperCAmelCase : Union[str, Any] = split_dataset_by_node(lowerCAmelCase_ , rank=lowerCAmelCase_ , world_size=lowerCAmelCase_ )
_UpperCAmelCase : int = torch.utils.data.DataLoader(lowerCAmelCase_ , num_workers=lowerCAmelCase_ )
_UpperCAmelCase : Any = NUM_SHARDS * NUM_ITEMS_PER_SHARD
_UpperCAmelCase : int = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
_UpperCAmelCase : Tuple = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F'''local_size {local_size} != expected_local_size {expected_local_size}''' )
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 1e-1_2 , lowerCAmelCase_ = 100 , )-> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(lowerCAmelCase_ )[0] == np.shape(lowerCAmelCase_ )[1]
# Ensure proper dimensionality.
assert np.shape(lowerCAmelCase_ )[0] == np.shape(lowerCAmelCase_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowerCAmelCase_ ) == np.iscomplexobj(lowerCAmelCase_ )
_UpperCAmelCase : Any = np.iscomplexobj(lowerCAmelCase_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowerCAmelCase_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : List[str] = 1e1_2
while not convergence:
# Multiple matrix by the vector.
_UpperCAmelCase : Optional[int] = np.dot(lowerCAmelCase_ , lowerCAmelCase_ )
# Normalize the resulting output vector.
_UpperCAmelCase : Dict = w / np.linalg.norm(lowerCAmelCase_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_UpperCAmelCase : Any = vector.conj().T if is_complex else vector.T
_UpperCAmelCase : Optional[Any] = np.dot(lowerCAmelCase_ , np.dot(lowerCAmelCase_ , lowerCAmelCase_ ) )
# Check convergence.
_UpperCAmelCase : Optional[int] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Any = lambda_
if is_complex:
_UpperCAmelCase : Optional[int] = np.real(lambda_ )
return lambda_, vector
def snake_case_ ( )-> None:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_UpperCAmelCase : Union[str, Any] = np.array([41, 4, 20] )
_UpperCAmelCase : Tuple = real_input_matrix.astype(np.complexaaa )
_UpperCAmelCase : Optional[Any] = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_UpperCAmelCase : Optional[Any] = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_UpperCAmelCase : List[str] = real_input_matrix
_UpperCAmelCase : Any = real_vector
elif problem_type == "complex":
_UpperCAmelCase : Dict = complex_input_matrix
_UpperCAmelCase : List[str] = complex_vector
# Our implementation.
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = power_iteration(lowerCAmelCase_ , lowerCAmelCase_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = np.linalg.eigh(lowerCAmelCase_ )
# Last eigenvalue is the maximum one.
_UpperCAmelCase : List[Any] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_UpperCAmelCase : Any = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowerCAmelCase_ ) - np.abs(lowerCAmelCase_ ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 349 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A_ : Dict = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """sequence-classification"""
def __init__( self ,a_ ) -> Dict:
if type(a_ ) == dict:
_UpperCAmelCase : Tuple = Namespace(**a_ )
_UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task]
_UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(a_ ,a_ ,self.mode )
def _snake_case ( self ,**a_ ) -> Optional[Any]:
return self.model(**a_ )
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : Any = self(**a_ )
_UpperCAmelCase : int = outputs[0]
_UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""]
_UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = self.hparams
_UpperCAmelCase : int = processors[args.task]()
_UpperCAmelCase : str = processor.get_labels()
for mode in ["train", "dev"]:
_UpperCAmelCase : Tuple = self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,a_ )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
_UpperCAmelCase : List[Any] = (
processor.get_dev_examples(args.data_dir )
if mode == """dev"""
else processor.get_train_examples(args.data_dir )
)
_UpperCAmelCase : Union[str, Any] = convert_examples_to_features(
a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,)
logger.info("""Saving features into cached file %s""" ,a_ )
torch.save(a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader:
_UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode
_UpperCAmelCase : Tuple = self._feature_file(a_ )
logger.info("""Loading features from cached file %s""" ,a_ )
_UpperCAmelCase : Union[str, Any] = torch.load(a_ )
_UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
_UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
_UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float )
return DataLoader(
TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,)
def _snake_case ( self ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : List[str] = self(**a_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2]
_UpperCAmelCase : List[str] = logits.detach().cpu().numpy()
_UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self ,a_ ) -> tuple:
_UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
_UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : int = np.argmax(a_ ,axis=1 )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : Union[str, Any] = np.squeeze(a_ )
_UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
_UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )}
_UpperCAmelCase : Dict = dict(results.items() )
_UpperCAmelCase : Any = results
return ret, preds_list, out_label_list
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( a_ ,a_ ) -> Any:
BaseTransformer.add_model_specific_args(a_ ,a_ )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=a_ ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
add_generic_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_UpperCAmelCase : Optional[int] = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ )
_UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) )
_UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_=13 ,a_=7 ,a_=True ,a_=True ,a_=True ,a_=True ,a_=99 ,a_=24 ,a_=2 ,a_=6 ,a_=37 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=16 ,a_=2 ,a_=0.02 ,a_=3 ,a_=None ,a_=1_000 ,) -> Any:
_UpperCAmelCase : Optional[int] = parent
_UpperCAmelCase : Dict = batch_size
_UpperCAmelCase : Tuple = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : Dict = use_input_mask
_UpperCAmelCase : Dict = use_token_type_ids
_UpperCAmelCase : Optional[Any] = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : int = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Any = hidden_act
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Tuple = attention_probs_dropout_prob
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Dict = type_sequence_label_size
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = num_labels
_UpperCAmelCase : List[Any] = scope
_UpperCAmelCase : str = range_bbox
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length, 4] ,self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase : str = bbox[i, j, 3]
_UpperCAmelCase : List[str] = bbox[i, j, 1]
_UpperCAmelCase : List[Any] = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase : Dict = bbox[i, j, 2]
_UpperCAmelCase : Tuple = bbox[i, j, 0]
_UpperCAmelCase : Optional[Any] = t
_UpperCAmelCase : List[Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
_UpperCAmelCase : str = None
if self.use_token_type_ids:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : Any = None
if self.use_labels:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_UpperCAmelCase : str = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def _snake_case ( self ) -> Union[str, Any]:
return LiltConfig(
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 ,)
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> Optional[int]:
_UpperCAmelCase : Optional[int] = LiltModel(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Union[str, Any] = model(a_ ,bbox=a_ ,attention_mask=a_ ,token_type_ids=a_ )
_UpperCAmelCase : Union[str, Any] = model(a_ ,bbox=a_ ,token_type_ids=a_ )
_UpperCAmelCase : Tuple = model(a_ ,bbox=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 _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> str:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : Union[str, Any] = LiltForTokenClassification(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Tuple = model(
a_ ,bbox=a_ ,attention_mask=a_ ,token_type_ids=a_ ,labels=a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> Union[str, Any]:
_UpperCAmelCase : str = LiltForQuestionAnswering(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : List[Any] = model(
a_ ,bbox=a_ ,attention_mask=a_ ,token_type_ids=a_ ,start_positions=a_ ,end_positions=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 _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
_UpperCAmelCase : str = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class lowercase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Tuple:
return True
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : str = LiltModelTester(self )
_UpperCAmelCase : str = ConfigTester(self ,config_class=a_ ,hidden_size=37 )
def _snake_case ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def _snake_case ( self ) -> int:
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : int = type
self.model_tester.create_and_check_model(*a_ )
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a_ )
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a_ )
@slow
def _snake_case ( self ) -> Union[str, Any]:
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Any = LiltModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_torch
@slow
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> str:
_UpperCAmelCase : int = LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base""" ).to(a_ )
_UpperCAmelCase : Tuple = torch.tensor([[1, 2]] ,device=a_ )
_UpperCAmelCase : Dict = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] ,device=a_ )
# forward pass
with torch.no_grad():
_UpperCAmelCase : str = model(input_ids=a_ ,bbox=a_ )
_UpperCAmelCase : Tuple = torch.Size([1, 2, 768] )
_UpperCAmelCase : List[str] = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] ,device=a_ ,)
self.assertTrue(outputs.last_hidden_state.shape ,a_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] ,a_ ,atol=1E-3 ) )
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """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-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> list:
'''simple docstring'''
if len(lowerCAmelCase_ ) <= 1:
return lst
_UpperCAmelCase : str = 1
while i < len(lowerCAmelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase ,_UpperCAmelCase : Any = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase : Union[str, Any] = 1
return lst
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Tuple = [int(item) for item in user_input.split(""",""")]
print(gnome_sort(unsorted))
| 349 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@slow
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,"""prajjwal1/bert-tiny""" )
_UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_UpperCAmelCase : List[Any] = bertabert.config.encoder.vocab_size
_UpperCAmelCase : Optional[int] = tokenizer.sep_token_id
_UpperCAmelCase : Union[str, Any] = tokenizer.cls_token_id
_UpperCAmelCase : str = 128
_UpperCAmelCase : List[str] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""train[:1%]""" )
_UpperCAmelCase : Union[str, Any] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""validation[:1%]""" )
_UpperCAmelCase : Any = train_dataset.select(range(32 ) )
_UpperCAmelCase : Any = val_dataset.select(range(16 ) )
_UpperCAmelCase : List[Any] = 4
def _map_to_encoder_decoder_inputs(a_ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_UpperCAmelCase : int = tokenizer(batch["""article"""] ,padding="""max_length""" ,truncation=a_ ,max_length=512 )
_UpperCAmelCase : Tuple = tokenizer(batch["""highlights"""] ,padding="""max_length""" ,truncation=a_ ,max_length=128 )
_UpperCAmelCase : int = inputs.input_ids
_UpperCAmelCase : Union[str, Any] = inputs.attention_mask
_UpperCAmelCase : Union[str, Any] = outputs.input_ids
_UpperCAmelCase : Dict = outputs.input_ids.copy()
_UpperCAmelCase : Dict = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
_UpperCAmelCase : Optional[int] = outputs.attention_mask
assert all(len(a_ ) == 512 for x in inputs.input_ids )
assert all(len(a_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(a_ ):
_UpperCAmelCase : Optional[int] = pred.label_ids
_UpperCAmelCase : Optional[int] = pred.predictions
# all unnecessary tokens are removed
_UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : str = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : Tuple = sum([int(pred_str[i] == label_str[i] ) for i in range(len(a_ ) )] ) / len(a_ )
return {"accuracy": accuracy}
# map train dataset
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
train_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
# same for validation dataset
_UpperCAmelCase : List[str] = val_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
val_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
_UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : List[str] = SeqaSeqTrainingArguments(
output_dir=a_ ,per_device_train_batch_size=a_ ,per_device_eval_batch_size=a_ ,predict_with_generate=a_ ,evaluation_strategy="""steps""" ,do_train=a_ ,do_eval=a_ ,warmup_steps=0 ,eval_steps=2 ,logging_steps=2 ,)
# instantiate trainer
_UpperCAmelCase : int = SeqaSeqTrainer(
model=a_ ,args=a_ ,compute_metrics=_compute_metrics ,train_dataset=a_ ,eval_dataset=a_ ,tokenizer=a_ ,)
# start training
trainer.train()
| 349 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Dict = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
),
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """gptsan-japanese"""
UpperCAmelCase = [
"""past_key_values""",
]
UpperCAmelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self ,a_=36_000 ,a_=1_280 ,a_=1_024 ,a_=8_192 ,a_=4_096 ,a_=128 ,a_=10 ,a_=0 ,a_=16 ,a_=16 ,a_=128 ,a_=0.0 ,a_=1E-5 ,a_=False ,a_=0.0 ,a_="float32" ,a_=False ,a_=False ,a_=False ,a_=0.002 ,a_=False ,a_=True ,a_=35_998 ,a_=35_995 ,a_=35_999 ,**a_ ,) -> Tuple:
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Dict = d_model
_UpperCAmelCase : str = d_ff
_UpperCAmelCase : List[str] = d_ext
_UpperCAmelCase : Dict = d_spout
_UpperCAmelCase : str = num_switch_layers
_UpperCAmelCase : Optional[Any] = num_ext_layers
_UpperCAmelCase : List[Any] = num_switch_layers + num_ext_layers
_UpperCAmelCase : Dict = num_heads
_UpperCAmelCase : List[Any] = num_experts
_UpperCAmelCase : List[str] = expert_capacity
_UpperCAmelCase : Optional[int] = dropout_rate
_UpperCAmelCase : Any = layer_norm_epsilon
_UpperCAmelCase : Dict = router_bias
_UpperCAmelCase : Optional[Any] = router_jitter_noise
_UpperCAmelCase : List[str] = router_dtype
_UpperCAmelCase : str = router_ignore_padding_tokens
_UpperCAmelCase : Optional[Any] = output_hidden_states
_UpperCAmelCase : Optional[int] = output_attentions
_UpperCAmelCase : Any = initializer_factor
_UpperCAmelCase : Dict = output_router_logits
_UpperCAmelCase : Tuple = use_cache
super().__init__(
separator_token_id=a_ ,pad_token_id=a_ ,eos_token_id=a_ ,**a_ ,)
| 349 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A_ : List[Any] = 637_8137.0
A_ : Dict = 635_6752.31_4245
A_ : int = 6_3_7_8_1_3_7
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> float:
'''simple docstring'''
_UpperCAmelCase : Tuple = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_UpperCAmelCase : Any = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
_UpperCAmelCase : Optional[Any] = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_UpperCAmelCase : Union[str, Any] = haversine_distance(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_UpperCAmelCase : Optional[int] = (b_lata + b_lata) / 2
_UpperCAmelCase : Any = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_UpperCAmelCase : List[str] = (sin(lowerCAmelCase_ ) ** 2) * (cos(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = cos(sigma / 2 ) ** 2
_UpperCAmelCase : Dict = (sigma - sin(lowerCAmelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_UpperCAmelCase : Union[str, Any] = (cos(lowerCAmelCase_ ) ** 2) * (sin(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = sin(sigma / 2 ) ** 2
_UpperCAmelCase : Optional[Any] = (sigma + sin(lowerCAmelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """Wav2Vec2FeatureExtractor"""
UpperCAmelCase = """AutoTokenizer"""
def __init__( self ,a_ ,a_ ) -> Tuple:
super().__init__(a_ ,a_ )
_UpperCAmelCase : Optional[int] = self.feature_extractor
_UpperCAmelCase : List[str] = False
@classmethod
def _snake_case ( cls ,a_ ,**a_ ) -> Dict:
try:
return super().from_pretrained(a_ ,**a_ )
except OSError:
warnings.warn(
f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
""" include a `tokenizer_class` attribute is deprecated and will be """
"""removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
""" attribute to either your `config.json` or `tokenizer_config.json` """
"""file to suppress this warning: """ ,a_ ,)
_UpperCAmelCase : List[Any] = WavaVecaFeatureExtractor.from_pretrained(a_ ,**a_ )
_UpperCAmelCase : Tuple = WavaVecaCTCTokenizer.from_pretrained(a_ ,**a_ )
return cls(feature_extractor=a_ ,tokenizer=a_ )
def __call__( self ,*a_ ,**a_ ) -> int:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*a_ ,**a_ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
_UpperCAmelCase : Dict = kwargs.pop("""raw_speech""" )
else:
_UpperCAmelCase : List[Any] = kwargs.pop("""audio""" ,a_ )
_UpperCAmelCase : List[Any] = kwargs.pop("""sampling_rate""" ,a_ )
_UpperCAmelCase : List[str] = kwargs.pop("""text""" ,a_ )
if len(a_ ) > 0:
_UpperCAmelCase : List[Any] = args[0]
_UpperCAmelCase : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_UpperCAmelCase : Union[str, Any] = self.feature_extractor(a_ ,*a_ ,sampling_rate=a_ ,**a_ )
if text is not None:
_UpperCAmelCase : Dict = self.tokenizer(a_ ,**a_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase : Optional[int] = encodings["""input_ids"""]
return inputs
def _snake_case ( self ,*a_ ,**a_ ) -> Any:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*a_ ,**a_ )
_UpperCAmelCase : List[Any] = kwargs.pop("""input_features""" ,a_ )
_UpperCAmelCase : int = kwargs.pop("""labels""" ,a_ )
if len(a_ ) > 0:
_UpperCAmelCase : Optional[Any] = args[0]
_UpperCAmelCase : Optional[Any] = args[1:]
if input_features is not None:
_UpperCAmelCase : List[str] = self.feature_extractor.pad(a_ ,*a_ ,**a_ )
if labels is not None:
_UpperCAmelCase : List[str] = self.tokenizer.pad(a_ ,**a_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_UpperCAmelCase : Tuple = labels["""input_ids"""]
return input_features
def _snake_case ( self ,*a_ ,**a_ ) -> Tuple:
return self.tokenizer.batch_decode(*a_ ,**a_ )
def _snake_case ( self ,*a_ ,**a_ ) -> Dict:
return self.tokenizer.decode(*a_ ,**a_ )
@contextmanager
def _snake_case ( self ) -> List[Any]:
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
_UpperCAmelCase : str = True
_UpperCAmelCase : List[str] = self.tokenizer
yield
_UpperCAmelCase : List[str] = self.feature_extractor
_UpperCAmelCase : Any = False
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def snake_case_ ( lowerCAmelCase_ = None )-> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
_UpperCAmelCase : Union[str, Any] = nums[0]
for i in range(1 , len(lowerCAmelCase_ ) ):
_UpperCAmelCase : Union[str, Any] = nums[i]
_UpperCAmelCase : Optional[int] = max(lowerCAmelCase_ , ans + num , lowerCAmelCase_ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
A_ : List[Any] = int(input("""Enter number of elements : """).strip())
A_ : Any = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array))
| 349 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowerCAmelCase_ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowerCAmelCase_ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowerCAmelCase_ )
return parser.parse_args()
def snake_case_ ( )-> str:
'''simple docstring'''
_UpperCAmelCase : List[str] = parse_args()
# Import training_script as a module.
_UpperCAmelCase : List[Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase : Optional[Any] = script_fpath.stem
_UpperCAmelCase : List[str] = importlib.import_module(lowerCAmelCase_ )
# Patch sys.argv
_UpperCAmelCase : Dict = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = None
UpperCAmelCase = None
def snake_case_ ( )-> Node | None:
'''simple docstring'''
_UpperCAmelCase : List[str] = Node(1 )
_UpperCAmelCase : Optional[Any] = Node(2 )
_UpperCAmelCase : List[str] = Node(3 )
_UpperCAmelCase : str = Node(4 )
_UpperCAmelCase : int = Node(5 )
return tree
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def snake_case_ ( lowerCAmelCase_ )-> Sequence[Node | None]:
'''simple docstring'''
_UpperCAmelCase : list[Any] = []
if root is None:
return output
_UpperCAmelCase : List[str] = deque([root] )
while process_queue:
_UpperCAmelCase : Dict = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Sequence[Node | None]:
'''simple docstring'''
_UpperCAmelCase : list[Any] = []
def populate_output(lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(lowerCAmelCase_ , lowerCAmelCase_ )
return output
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Sequence[Node | None]:
'''simple docstring'''
_UpperCAmelCase : list[Any] = []
def populate_output(lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(lowerCAmelCase_ , lowerCAmelCase_ )
return output
def snake_case_ ( lowerCAmelCase_ )-> Sequence[Node | None] | list[Any]:
'''simple docstring'''
if root is None:
return []
_UpperCAmelCase : list[Sequence[Node | None]] = []
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : Any = height(lowerCAmelCase_ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(lowerCAmelCase_ , lowerCAmelCase_ ) )
_UpperCAmelCase : Any = 1
else:
output.append(get_nodes_from_right_to_left(lowerCAmelCase_ , lowerCAmelCase_ ) )
_UpperCAmelCase : Tuple = 0
return output
def snake_case_ ( )-> None: # Main function for testing.
'''simple docstring'''
_UpperCAmelCase : Any = make_tree()
print(F'''In-order Traversal: {inorder(lowerCAmelCase_ )}''' )
print(F'''Pre-order Traversal: {preorder(lowerCAmelCase_ )}''' )
print(F'''Post-order Traversal: {postorder(lowerCAmelCase_ )}''' , """\n""" )
print(F'''Height of Tree: {height(lowerCAmelCase_ )}''' , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(lowerCAmelCase_ ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(lowerCAmelCase_ ) + 1 ):
print(F'''Level {level}:''' , get_nodes_from_left_to_right(lowerCAmelCase_ , level=lowerCAmelCase_ ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(lowerCAmelCase_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict = str(abs(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = [list(lowerCAmelCase_ ) for char in range(len(lowerCAmelCase_ ) )]
for index in range(len(lowerCAmelCase_ ) ):
num_transpositions[index].pop(lowerCAmelCase_ )
return max(
int("""""".join(list(lowerCAmelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 | 1 |
'''simple docstring'''
A_ : Tuple = 9.8_0665
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = g )-> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise ValueError("""Impossible Object volume""" )
if gravity <= 0:
raise ValueError("""Impossible Gravity""" )
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 349 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Dict = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), F'''{len(lowerCAmelCase_ )} != {len(lowerCAmelCase_ )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A_ : Union[str, Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A_ : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
try:
_UpperCAmelCase : Any = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[int]:
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(lowerCAmelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = "student" , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ , )-> Tuple[PreTrainedModel, List[int], List[int]]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
AutoTokenizer.from_pretrained(lowerCAmelCase_ ).save_pretrained(lowerCAmelCase_ ) # purely for convenience
_UpperCAmelCase : Any = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_ ).eval()
else:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), F'''teacher must be a model or string got type {type(lowerCAmelCase_ )}'''
_UpperCAmelCase : str = teacher.config.to_diff_dict()
try:
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
_UpperCAmelCase : Tuple = teacher_e
if d is None:
_UpperCAmelCase : Dict = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
_UpperCAmelCase : List[str] = teacher_e
if d is None:
_UpperCAmelCase : str = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase_ )
# Copy weights
_UpperCAmelCase : Any = teacher.config_class(**lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
_UpperCAmelCase : Optional[Any] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = list(range(lowerCAmelCase_ ) ), list(range(lowerCAmelCase_ ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(lowerCAmelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
if d_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
try:
if hasattr(
lowerCAmelCase_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase_ )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
_UpperCAmelCase : Dict = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 349 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
A_ : Union[str, Any] = logging.get_logger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""pixel_values"""]
def __init__( self ,a_ = True ,a_ = None ,a_ = PILImageResampling.BICUBIC ,a_ = True ,a_ = True ,a_ = 1 / 255 ,a_ = None ,a_ = True ,a_ = None ,a_ = None ,**a_ ,) -> None:
super().__init__(**a_ )
_UpperCAmelCase : Tuple = size if size is not None else {"""height""": 224, """width""": 224}
_UpperCAmelCase : Optional[Any] = get_size_dict(a_ )
_UpperCAmelCase : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_UpperCAmelCase : Optional[int] = get_size_dict(a_ ,default_to_square=a_ ,param_name="""crop_size""" )
_UpperCAmelCase : List[Any] = do_resize
_UpperCAmelCase : str = do_rescale
_UpperCAmelCase : str = do_normalize
_UpperCAmelCase : Any = do_center_crop
_UpperCAmelCase : List[str] = crop_size
_UpperCAmelCase : Any = size
_UpperCAmelCase : List[str] = resample
_UpperCAmelCase : Union[str, Any] = rescale_factor
_UpperCAmelCase : Any = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCAmelCase : Any = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _snake_case ( self ,a_ ,a_ ,a_ = PILImageResampling.BILINEAR ,a_ = None ,**a_ ,) -> np.ndarray:
_UpperCAmelCase : Optional[Any] = get_size_dict(a_ )
if "shortest_edge" in size:
_UpperCAmelCase : Any = get_resize_output_image_size(a_ ,size=size["""shortest_edge"""] ,default_to_square=a_ )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCAmelCase : str = (size["""height"""], size["""width"""])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(a_ ,size=a_ ,resample=a_ ,data_format=a_ ,**a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = None ,**a_ ,) -> np.ndarray:
_UpperCAmelCase : Any = get_size_dict(a_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(a_ ,size=(size["""height"""], size["""width"""]) ,data_format=a_ ,**a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = None ,**a_ ) -> np.ndarray:
return rescale(a_ ,scale=a_ ,data_format=a_ ,**a_ )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ = None ,**a_ ,) -> np.ndarray:
return normalize(a_ ,mean=a_ ,std=a_ ,data_format=a_ ,**a_ )
def _snake_case ( self ,a_ ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = None ,a_ = ChannelDimension.FIRST ,**a_ ,) -> BatchFeature:
_UpperCAmelCase : str = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : List[str] = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCAmelCase : Any = crop_size if crop_size is not None else self.crop_size
_UpperCAmelCase : List[Any] = get_size_dict(a_ ,param_name="""crop_size""" ,default_to_square=a_ )
_UpperCAmelCase : Tuple = resample if resample is not None else self.resample
_UpperCAmelCase : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : int = image_std if image_std is not None else self.image_std
_UpperCAmelCase : List[str] = size if size is not None else self.size
_UpperCAmelCase : Tuple = get_size_dict(a_ )
if not is_batched(a_ ):
_UpperCAmelCase : Optional[int] = [images]
if not valid_images(a_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
_UpperCAmelCase : str = [to_numpy_array(a_ ) for image in images]
if do_resize:
_UpperCAmelCase : Tuple = [self.resize(image=a_ ,size=a_ ,resample=a_ ) for image in images]
if do_center_crop:
_UpperCAmelCase : Optional[Any] = [self.center_crop(image=a_ ,size=a_ ) for image in images]
if do_rescale:
_UpperCAmelCase : Optional[int] = [self.rescale(image=a_ ,scale=a_ ) for image in images]
if do_normalize:
_UpperCAmelCase : Optional[int] = [self.normalize(image=a_ ,mean=a_ ,std=a_ ) for image in images]
_UpperCAmelCase : Any = [to_channel_dimension_format(a_ ,a_ ) for image in images]
_UpperCAmelCase : List[str] = {"""pixel_values""": images}
return BatchFeature(data=a_ ,tensor_type=a_ )
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase_ , lowerCAmelCase_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
if len(lowerCAmelCase_ ) != 32:
raise ValueError("""Input must be of length 32""" )
_UpperCAmelCase : Optional[int] = B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
_UpperCAmelCase : str = format(lowerCAmelCase_ , """08x""" )[-8:]
_UpperCAmelCase : Optional[Any] = B""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : int = B""""""
for char in message:
bit_string += format(lowerCAmelCase_ , """08b""" ).encode("""utf-8""" )
_UpperCAmelCase : str = format(len(lowerCAmelCase_ ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(lowerCAmelCase_ ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def snake_case_ ( lowerCAmelCase_ )-> Generator[list[int], None, None]:
'''simple docstring'''
if len(lowerCAmelCase_ ) % 512 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0 , len(lowerCAmelCase_ ) , 512 ):
_UpperCAmelCase : List[Any] = bit_string[pos : pos + 512]
_UpperCAmelCase : Union[str, Any] = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
_UpperCAmelCase : Optional[Any] = format(lowerCAmelCase_ , """032b""" )
_UpperCAmelCase : Union[str, Any] = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(lowerCAmelCase_ , 2 )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
return (a + b) % 2**32
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : List[Any] = preprocess(lowerCAmelCase_ )
_UpperCAmelCase : Any = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
_UpperCAmelCase : List[Any] = 0x67_452_301
_UpperCAmelCase : int = 0xEF_CDA_B89
_UpperCAmelCase : List[Any] = 0x98_BAD_CFE
_UpperCAmelCase : Any = 0x10_325_476
_UpperCAmelCase : Tuple = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = aa
_UpperCAmelCase : str = ba
_UpperCAmelCase : str = ca
_UpperCAmelCase : List[str] = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
_UpperCAmelCase : str = d ^ (b & (c ^ d))
_UpperCAmelCase : str = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
_UpperCAmelCase : Optional[Any] = c ^ (d & (b ^ c))
_UpperCAmelCase : int = (5 * i + 1) % 16
elif i <= 47:
_UpperCAmelCase : int = b ^ c ^ d
_UpperCAmelCase : Optional[int] = (3 * i + 5) % 16
else:
_UpperCAmelCase : List[str] = c ^ (b | not_aa(lowerCAmelCase_ ))
_UpperCAmelCase : Optional[int] = (7 * i) % 16
_UpperCAmelCase : str = (f + a + added_consts[i] + block_words[g]) % 2**32
_UpperCAmelCase : Any = d
_UpperCAmelCase : Optional[int] = c
_UpperCAmelCase : Any = b
_UpperCAmelCase : List[str] = sum_aa(lowerCAmelCase_ , left_rotate_aa(lowerCAmelCase_ , shift_amounts[i] ) )
# Add hashed chunk to running total
_UpperCAmelCase : Optional[int] = sum_aa(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : str = sum_aa(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : List[str] = sum_aa(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : int = sum_aa(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = reformat_hex(lowerCAmelCase_ ) + reformat_hex(lowerCAmelCase_ ) + reformat_hex(lowerCAmelCase_ ) + reformat_hex(lowerCAmelCase_ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_UpperCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter Video/IGTV url: """).strip()
A_ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 349 | 1 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ = 100 )-> int:
'''simple docstring'''
_UpperCAmelCase : int = sum(i * i for i in range(1 , n + 1 ) )
_UpperCAmelCase : List[str] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 349 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Union[str, Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(a_ )
return image
@property
def _snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def _snake_case ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def _snake_case ( self ) -> Dict:
torch.manual_seed(0 )
_UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
return CLIPTextModel(a_ )
@property
def _snake_case ( self ) -> Union[str, Any]:
def extract(*a_ ,**a_ ):
class lowercase :
"""simple docstring"""
def __init__( self ) -> Any:
_UpperCAmelCase : str = torch.ones([0] )
def _snake_case ( self ,a_ ) -> Any:
self.pixel_values.to(a_ )
return self
return Out()
return extract
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Union[str, Any] = self.dummy_cond_unet
_UpperCAmelCase : int = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
_UpperCAmelCase : Optional[int] = self.dummy_vae
_UpperCAmelCase : Optional[int] = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : int = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Tuple = self.dummy_cond_unet
_UpperCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : int = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : str = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : List[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=a_ )
assert isinstance(a_ ,a_ )
assert isinstance(pipe.scheduler ,a_ )
assert pipe.safety_checker is None
_UpperCAmelCase : Dict = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained(a_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase : Union[str, Any] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.dummy_cond_unet
_UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : List[str] = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_UpperCAmelCase : str = unet.half()
_UpperCAmelCase : List[str] = vae.half()
_UpperCAmelCase : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : Dict = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : int = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : List[Any] = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCAmelCase : Any = 4_003_660_346
_UpperCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCAmelCase : Optional[Any] = 2_734_971_755
_UpperCAmelCase : Optional[int] = 7
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[int] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCAmelCase : Optional[int] = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Union[str, Any] = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCAmelCase : Dict = 1_044_355_234
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : List[str] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCAmelCase : Tuple = torch.manual_seed(a_ )
_UpperCAmelCase : Dict = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : str = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """mra"""
def __init__( self ,a_=50_265 ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=1 ,a_=0.02 ,a_=1E-5 ,a_="absolute" ,a_=4 ,a_="full" ,a_=0 ,a_=0 ,a_=1 ,a_=0 ,a_=2 ,**a_ ,) -> int:
super().__init__(pad_token_id=a_ ,bos_token_id=a_ ,eos_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : Union[str, Any] = max_position_embeddings
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Optional[int] = num_attention_heads
_UpperCAmelCase : int = intermediate_size
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : Any = hidden_dropout_prob
_UpperCAmelCase : int = attention_probs_dropout_prob
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Optional[int] = layer_norm_eps
_UpperCAmelCase : Optional[Any] = position_embedding_type
_UpperCAmelCase : List[Any] = block_per_row
_UpperCAmelCase : List[str] = approx_mode
_UpperCAmelCase : List[Any] = initial_prior_first_n_blocks
_UpperCAmelCase : int = initial_prior_diagonal_n_blocks
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : int = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """conditional_detr"""
UpperCAmelCase = ["""past_key_values"""]
UpperCAmelCase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self ,a_=True ,a_=None ,a_=3 ,a_=300 ,a_=6 ,a_=2_048 ,a_=8 ,a_=6 ,a_=2_048 ,a_=8 ,a_=0.0 ,a_=0.0 ,a_=True ,a_="relu" ,a_=256 ,a_=0.1 ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1.0 ,a_=False ,a_="sine" ,a_="resnet50" ,a_=True ,a_=False ,a_=2 ,a_=5 ,a_=2 ,a_=1 ,a_=1 ,a_=2 ,a_=5 ,a_=2 ,a_=0.25 ,**a_ ,) -> List[str]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_UpperCAmelCase : Any = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(a_ ,a_ ):
_UpperCAmelCase : int = backbone_config.get("""model_type""" )
_UpperCAmelCase : Any = CONFIG_MAPPING[backbone_model_type]
_UpperCAmelCase : List[str] = config_class.from_dict(a_ )
_UpperCAmelCase : str = use_timm_backbone
_UpperCAmelCase : Tuple = backbone_config
_UpperCAmelCase : List[str] = num_channels
_UpperCAmelCase : Optional[int] = num_queries
_UpperCAmelCase : Dict = d_model
_UpperCAmelCase : List[Any] = encoder_ffn_dim
_UpperCAmelCase : List[Any] = encoder_layers
_UpperCAmelCase : Tuple = encoder_attention_heads
_UpperCAmelCase : List[str] = decoder_ffn_dim
_UpperCAmelCase : Optional[Any] = decoder_layers
_UpperCAmelCase : Tuple = decoder_attention_heads
_UpperCAmelCase : Optional[int] = dropout
_UpperCAmelCase : Tuple = attention_dropout
_UpperCAmelCase : Optional[int] = activation_dropout
_UpperCAmelCase : Union[str, Any] = activation_function
_UpperCAmelCase : Dict = init_std
_UpperCAmelCase : Dict = init_xavier_std
_UpperCAmelCase : List[Any] = encoder_layerdrop
_UpperCAmelCase : int = decoder_layerdrop
_UpperCAmelCase : int = encoder_layers
_UpperCAmelCase : int = auxiliary_loss
_UpperCAmelCase : Optional[int] = position_embedding_type
_UpperCAmelCase : List[Any] = backbone
_UpperCAmelCase : Optional[int] = use_pretrained_backbone
_UpperCAmelCase : List[Any] = dilation
# Hungarian matcher
_UpperCAmelCase : List[str] = class_cost
_UpperCAmelCase : Optional[Any] = bbox_cost
_UpperCAmelCase : Tuple = giou_cost
# Loss coefficients
_UpperCAmelCase : str = mask_loss_coefficient
_UpperCAmelCase : Tuple = dice_loss_coefficient
_UpperCAmelCase : int = cls_loss_coefficient
_UpperCAmelCase : Tuple = bbox_loss_coefficient
_UpperCAmelCase : Union[str, Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = focal_alpha
super().__init__(is_encoder_decoder=a_ ,**a_ )
@property
def _snake_case ( self ) -> int:
return self.encoder_attention_heads
@property
def _snake_case ( self ) -> int:
return self.d_model
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Optional[Any] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
_UpperCAmelCase : Union[str, Any] = self.backbone_config.to_dict()
_UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-5
@property
def _snake_case ( self ) -> int:
return 12
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 | 1 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class lowercase :
"""simple docstring"""
@staticmethod
def _snake_case ( *a_ ,**a_ ) -> Dict:
pass
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _snake_case ( self ,a_ ,a_ ,a_ ) -> List[Any]:
_UpperCAmelCase : Optional[int] = DepthEstimationPipeline(model=a_ ,image_processor=a_ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : int = depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
self.assertEqual({"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )} ,a_ )
import datasets
_UpperCAmelCase : List[Any] = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" ,"""image""" ,split="""test""" )
_UpperCAmelCase : List[str] = depth_estimator(
[
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
] )
self.assertEqual(
[
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
] ,a_ ,)
@require_tf
@unittest.skip("""Depth estimation is not implemented in TF""" )
def _snake_case ( self ) -> str:
pass
@slow
@require_torch
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = """Intel/dpt-large"""
_UpperCAmelCase : Tuple = pipeline("""depth-estimation""" ,model=a_ )
_UpperCAmelCase : List[str] = depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
_UpperCAmelCase : List[Any] = hashimage(outputs["""depth"""] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item() ) ,29.304 )
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item() ) ,2.662 )
@require_torch
def _snake_case ( self ) -> str:
# This is highly irregular to have no small tests.
self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT""" )
| 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Dict = [2, 4, 6, 8, 10, 12]
_UpperCAmelCase : Optional[int] = 100
self.assertEqual(kp.calc_profit(a_ ,a_ ,a_ ) ,210 )
def _snake_case ( self ) -> Union[str, Any]:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Any:
self.assertRaisesRegex(a_ ,"""Weight can not be negative.""" )
def _snake_case ( self ) -> Optional[Any]:
self.assertRaisesRegex(a_ ,"""Profit can not be negative.""" )
def _snake_case ( self ) -> Dict:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Tuple:
self.assertRaisesRegex(
a_ ,"""The length of profit and weight must be same.""" )
if __name__ == "__main__":
unittest.main()
| 349 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : List[Any] = checkpoint
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : Tuple = vae_state_dict["""encoder.conv_in.weight"""]
_UpperCAmelCase : List[str] = vae_state_dict["""encoder.conv_in.bias"""]
_UpperCAmelCase : Dict = vae_state_dict["""encoder.conv_out.weight"""]
_UpperCAmelCase : List[Any] = vae_state_dict["""encoder.conv_out.bias"""]
_UpperCAmelCase : List[str] = vae_state_dict["""encoder.norm_out.weight"""]
_UpperCAmelCase : Dict = vae_state_dict["""encoder.norm_out.bias"""]
_UpperCAmelCase : Optional[Any] = vae_state_dict["""decoder.conv_in.weight"""]
_UpperCAmelCase : Dict = vae_state_dict["""decoder.conv_in.bias"""]
_UpperCAmelCase : Union[str, Any] = vae_state_dict["""decoder.conv_out.weight"""]
_UpperCAmelCase : List[Any] = vae_state_dict["""decoder.conv_out.bias"""]
_UpperCAmelCase : List[Any] = vae_state_dict["""decoder.norm_out.weight"""]
_UpperCAmelCase : Any = vae_state_dict["""decoder.norm_out.bias"""]
_UpperCAmelCase : Tuple = vae_state_dict["""quant_conv.weight"""]
_UpperCAmelCase : Any = vae_state_dict["""quant_conv.bias"""]
_UpperCAmelCase : List[Any] = vae_state_dict["""post_quant_conv.weight"""]
_UpperCAmelCase : Optional[Any] = vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
_UpperCAmelCase : List[Any] = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
_UpperCAmelCase : Tuple = {
layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(lowerCAmelCase_ )
}
# Retrieves the keys for the decoder up blocks only
_UpperCAmelCase : Any = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
_UpperCAmelCase : List[str] = {
layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(lowerCAmelCase_ )
}
for i in range(lowerCAmelCase_ ):
_UpperCAmelCase : Tuple = [key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]
if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
_UpperCAmelCase : Any = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.weight''' )
_UpperCAmelCase : Any = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.bias''' )
_UpperCAmelCase : Dict = renew_vae_resnet_paths(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = {"""old""": F'''down.{i}.block''', """new""": F'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [key for key in vae_state_dict if """encoder.mid.block""" in key]
_UpperCAmelCase : int = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_UpperCAmelCase : Dict = [key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]
_UpperCAmelCase : int = renew_vae_resnet_paths(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = {"""old""": F'''mid.block_{i}''', """new""": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
_UpperCAmelCase : int = [key for key in vae_state_dict if """encoder.mid.attn""" in key]
_UpperCAmelCase : int = renew_vae_attention_paths(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
conv_attn_to_linear(lowerCAmelCase_ )
for i in range(lowerCAmelCase_ ):
_UpperCAmelCase : List[Any] = num_up_blocks - 1 - i
_UpperCAmelCase : Tuple = [
key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
]
if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
_UpperCAmelCase : Tuple = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.weight'''
]
_UpperCAmelCase : int = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.bias'''
]
_UpperCAmelCase : Any = renew_vae_resnet_paths(lowerCAmelCase_ )
_UpperCAmelCase : Dict = {"""old""": F'''up.{block_id}.block''', """new""": F'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
_UpperCAmelCase : str = [key for key in vae_state_dict if """decoder.mid.block""" in key]
_UpperCAmelCase : Tuple = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_UpperCAmelCase : Tuple = [key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]
_UpperCAmelCase : List[str] = renew_vae_resnet_paths(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = {"""old""": F'''mid.block_{i}''', """new""": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = [key for key in vae_state_dict if """decoder.mid.attn""" in key]
_UpperCAmelCase : List[Any] = renew_vae_attention_paths(lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
conv_attn_to_linear(lowerCAmelCase_ )
return new_checkpoint
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , )-> Any:
'''simple docstring'''
_UpperCAmelCase : str = requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
_UpperCAmelCase : List[Any] = io.BytesIO(r.content )
_UpperCAmelCase : Optional[Any] = OmegaConf.load(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = 512
_UpperCAmelCase : str = """cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
_UpperCAmelCase : Tuple = {}
with safe_open(lowerCAmelCase_ , framework="""pt""" , device="""cpu""" ) as f:
for key in f.keys():
_UpperCAmelCase : Optional[int] = f.get_tensor(lowerCAmelCase_ )
else:
_UpperCAmelCase : List[Any] = torch.load(lowerCAmelCase_ , map_location=lowerCAmelCase_ )["""state_dict"""]
# Convert the VAE model.
_UpperCAmelCase : Any = create_vae_diffusers_config(lowerCAmelCase_ , image_size=lowerCAmelCase_ )
_UpperCAmelCase : str = custom_convert_ldm_vae_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = AutoencoderKL(**lowerCAmelCase_ )
vae.load_state_dict(lowerCAmelCase_ )
vae.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
A_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
A_ : Dict = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 349 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> List[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Union[str, Any]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Optional[int]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> int:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Dict:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> str:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> str:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Union[str, Any]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Any:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Tuple:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Any:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> List[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Dict:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> str:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> List[str]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> str:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Dict:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[int]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Optional[int]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Any:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> str:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Optional[Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> int:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> List[str]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Any:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Dict:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> int:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> str:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[Any]:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> List[str]:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> Union[str, Any]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Any:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> str:
requires_backends(cls ,["""flax"""] )
class lowercase ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["""flax"""]
def __init__( self ,*a_ ,**a_ ) -> List[str]:
requires_backends(self ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> List[str]:
requires_backends(cls ,["""flax"""] )
@classmethod
def _snake_case ( cls ,*a_ ,**a_ ) -> Optional[int]:
requires_backends(cls ,["""flax"""] )
| 349 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class lowercase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""} )
UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _snake_case ( self ) -> Tuple:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
torch.manual_seed(0 )
_UpperCAmelCase : Tuple = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
torch.manual_seed(0 )
_UpperCAmelCase : List[Any] = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
_UpperCAmelCase : str = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
_UpperCAmelCase : Dict = CLIPTextModel(a_ )
_UpperCAmelCase : Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase : List[str] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _snake_case ( self ,a_ ,a_=0 ) -> Union[str, Any]:
if str(a_ ).startswith("""mps""" ):
_UpperCAmelCase : int = torch.manual_seed(a_ )
else:
_UpperCAmelCase : str = torch.Generator(device=a_ ).manual_seed(a_ )
_UpperCAmelCase : int = 2
_UpperCAmelCase : List[str] = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=a_ ,device=torch.device(a_ ) ,)
_UpperCAmelCase : Tuple = floats_tensor(control_image.shape ,rng=random.Random(a_ ) ).to(a_ )
_UpperCAmelCase : List[str] = image.cpu().permute(0 ,2 ,3 ,1 )[0]
_UpperCAmelCase : Dict = Image.fromarray(np.uinta(a_ ) ).convert("""RGB""" ).resize((64, 64) )
_UpperCAmelCase : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def _snake_case ( self ) -> str:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def _snake_case ( self ) -> str:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def _snake_case ( self ) -> int:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = StableDiffusionControlNetImgaImgPipeline
UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
UpperCAmelCase = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def _snake_case ( self ) -> Any:
torch.manual_seed(0 )
_UpperCAmelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
torch.manual_seed(0 )
def init_weights(a_ ):
if isinstance(a_ ,torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
_UpperCAmelCase : int = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
controlneta.controlnet_down_blocks.apply(a_ )
torch.manual_seed(0 )
_UpperCAmelCase : Any = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
controlneta.controlnet_down_blocks.apply(a_ )
torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
torch.manual_seed(0 )
_UpperCAmelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
_UpperCAmelCase : int = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
_UpperCAmelCase : List[Any] = CLIPTextModel(a_ )
_UpperCAmelCase : Optional[int] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase : int = MultiControlNetModel([controlneta, controlneta] )
_UpperCAmelCase : Any = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _snake_case ( self ,a_ ,a_=0 ) -> Optional[Any]:
if str(a_ ).startswith("""mps""" ):
_UpperCAmelCase : Any = torch.manual_seed(a_ )
else:
_UpperCAmelCase : str = torch.Generator(device=a_ ).manual_seed(a_ )
_UpperCAmelCase : int = 2
_UpperCAmelCase : Tuple = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=a_ ,device=torch.device(a_ ) ,),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=a_ ,device=torch.device(a_ ) ,),
]
_UpperCAmelCase : Dict = floats_tensor(control_image[0].shape ,rng=random.Random(a_ ) ).to(a_ )
_UpperCAmelCase : List[str] = image.cpu().permute(0 ,2 ,3 ,1 )[0]
_UpperCAmelCase : Tuple = Image.fromarray(np.uinta(a_ ) ).convert("""RGB""" ).resize((64, 64) )
_UpperCAmelCase : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = self.get_dummy_components()
_UpperCAmelCase : Optional[Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
_UpperCAmelCase : Union[str, Any] = 10.0
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Any = self.get_dummy_inputs(a_ )
_UpperCAmelCase : Any = steps
_UpperCAmelCase : Optional[Any] = scale
_UpperCAmelCase : Any = pipe(**a_ )[0]
_UpperCAmelCase : List[str] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : Any = steps
_UpperCAmelCase : str = scale
_UpperCAmelCase : Any = pipe(**a_ ,control_guidance_start=0.1 ,control_guidance_end=0.2 )[0]
_UpperCAmelCase : Union[str, Any] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : str = steps
_UpperCAmelCase : List[Any] = scale
_UpperCAmelCase : List[str] = pipe(**a_ ,control_guidance_start=[0.1, 0.3] ,control_guidance_end=[0.2, 0.7] )[0]
_UpperCAmelCase : List[str] = self.get_dummy_inputs(a_ )
_UpperCAmelCase : int = steps
_UpperCAmelCase : Optional[int] = scale
_UpperCAmelCase : Dict = pipe(**a_ ,control_guidance_start=0.4 ,control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def _snake_case ( self ) -> Optional[Any]:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def _snake_case ( self ) -> Optional[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def _snake_case ( self ) -> List[str]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Any = self.get_dummy_components()
_UpperCAmelCase : str = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(a_ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : str = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
_UpperCAmelCase : str = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ ,controlnet=a_ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase : int = """evil space-punk bird"""
_UpperCAmelCase : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
_UpperCAmelCase : Tuple = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
_UpperCAmelCase : Any = pipe(
a_ ,a_ ,control_image=a_ ,generator=a_ ,output_type="""np""" ,num_inference_steps=50 ,strength=0.6 ,)
_UpperCAmelCase : Union[str, Any] = output.images[0]
assert image.shape == (512, 512, 3)
_UpperCAmelCase : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 349 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
A_ : List[str] = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""
A_ : Any = """\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""
A_ : List[Any] = """
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> wer = datasets.load_metric(\"wer\")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ,id="""sequence""" ),
"""references""": datasets.Value("""string""" ,id="""sequence""" ),
} ) ,codebase_urls=["""https://github.com/jitsi/jiwer/"""] ,reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
] ,)
def _snake_case ( self ,a_=None ,a_=None ,a_=False ) -> List[Any]:
if concatenate_texts:
return compute_measures(a_ ,a_ )["wer"]
else:
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Any = 0
for prediction, reference in zip(a_ ,a_ ):
_UpperCAmelCase : Optional[int] = compute_measures(a_ ,a_ )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_=13 ,a_=7 ,a_=True ,a_=True ,a_=True ,a_=True ,a_=True ,a_=False ,a_=False ,a_=False ,a_=2 ,a_=99 ,a_=0 ,a_=32 ,a_=5 ,a_=4 ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=2 ,a_=0.02 ,a_=2 ,a_=4 ,a_="last" ,a_=True ,a_=None ,a_=0 ,) -> str:
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : List[Any] = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : List[Any] = use_input_lengths
_UpperCAmelCase : str = use_token_type_ids
_UpperCAmelCase : Tuple = use_labels
_UpperCAmelCase : Optional[int] = gelu_activation
_UpperCAmelCase : str = sinusoidal_embeddings
_UpperCAmelCase : Dict = causal
_UpperCAmelCase : Union[str, Any] = asm
_UpperCAmelCase : str = n_langs
_UpperCAmelCase : List[str] = vocab_size
_UpperCAmelCase : List[Any] = n_special
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : List[Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : List[str] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = type_sequence_label_size
_UpperCAmelCase : Any = initializer_range
_UpperCAmelCase : int = num_labels
_UpperCAmelCase : Dict = num_choices
_UpperCAmelCase : Dict = summary_type
_UpperCAmelCase : Dict = use_proj
_UpperCAmelCase : str = scope
_UpperCAmelCase : str = bos_token_id
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCAmelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : List[str] = None
if self.use_input_lengths:
_UpperCAmelCase : Optional[int] = (
ids_tensor([self.batch_size] ,vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_UpperCAmelCase : List[Any] = None
if self.use_token_type_ids:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.n_langs )
_UpperCAmelCase : str = None
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : Union[str, Any] = None
if self.use_labels:
_UpperCAmelCase : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_UpperCAmelCase : str = ids_tensor([self.batch_size] ,2 ).float()
_UpperCAmelCase : Any = ids_tensor([self.batch_size] ,self.num_choices )
_UpperCAmelCase : Any = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _snake_case ( self ) -> str:
return XLMConfig(
vocab_size=self.vocab_size ,n_special=self.n_special ,emb_dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,gelu_activation=self.gelu_activation ,sinusoidal_embeddings=self.sinusoidal_embeddings ,asm=self.asm ,causal=self.causal ,n_langs=self.n_langs ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,summary_type=self.summary_type ,use_proj=self.use_proj ,num_labels=self.num_labels ,bos_token_id=self.bos_token_id ,)
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> Dict:
_UpperCAmelCase : int = XLMModel(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : List[str] = model(a_ ,lengths=a_ ,langs=a_ )
_UpperCAmelCase : int = model(a_ ,langs=a_ )
_UpperCAmelCase : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> Optional[int]:
_UpperCAmelCase : Any = XLMWithLMHeadModel(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : List[str] = model(a_ ,token_type_ids=a_ ,labels=a_ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> Dict:
_UpperCAmelCase : str = XLMForQuestionAnsweringSimple(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : List[str] = model(a_ )
_UpperCAmelCase : List[str] = model(a_ ,start_positions=a_ ,end_positions=a_ )
_UpperCAmelCase : Any = outputs
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 _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> int:
_UpperCAmelCase : List[Any] = XLMForQuestionAnswering(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Optional[Any] = model(a_ )
_UpperCAmelCase : Tuple = model(
a_ ,start_positions=a_ ,end_positions=a_ ,cls_index=a_ ,is_impossible=a_ ,p_mask=a_ ,)
_UpperCAmelCase : Optional[int] = model(
a_ ,start_positions=a_ ,end_positions=a_ ,cls_index=a_ ,is_impossible=a_ ,)
((_UpperCAmelCase) ,) : Tuple = result_with_labels.to_tuple()
_UpperCAmelCase : Optional[Any] = model(a_ ,start_positions=a_ ,end_positions=a_ )
((_UpperCAmelCase) ,) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape ,() )
self.parent.assertEqual(result.start_top_log_probs.shape ,(self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape ,(self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape ,(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape ,(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape ,(self.batch_size,) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> List[Any]:
_UpperCAmelCase : Optional[Any] = XLMForSequenceClassification(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : List[str] = model(a_ )
_UpperCAmelCase : Dict = model(a_ ,labels=a_ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = self.num_labels
_UpperCAmelCase : Dict = XLMForTokenClassification(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Dict = model(a_ ,attention_mask=a_ ,labels=a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,) -> str:
_UpperCAmelCase : str = self.num_choices
_UpperCAmelCase : Dict = XLMForMultipleChoice(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase : Any = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_UpperCAmelCase : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_UpperCAmelCase : List[Any] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
_UpperCAmelCase : Dict = model(
a_ ,attention_mask=a_ ,token_type_ids=a_ ,labels=a_ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _snake_case ( self ) -> int:
_UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowercase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
UpperCAmelCase = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Tuple:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _snake_case ( self ,a_ ,a_ ,a_=False ) -> int:
_UpperCAmelCase : Any = super()._prepare_for_class(a_ ,a_ ,return_labels=a_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_UpperCAmelCase : Optional[int] = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=a_ )
_UpperCAmelCase : str = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=a_ )
return inputs_dict
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[Any] = XLMModelTester(self )
_UpperCAmelCase : str = ConfigTester(self ,config_class=a_ ,emb_dim=37 )
def _snake_case ( self ) -> Any:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*a_ )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*a_ )
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*a_ )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*a_ )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*a_ )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*a_ )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*a_ )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_=False ,a_=1 ) -> Optional[int]:
self.assertIsInstance(a_ ,a_ )
self.assertListEqual(
[isinstance(a_ ,a_ ) for iter_attentions in attentions] ,[True] * len(a_ ) )
self.assertEqual(len(a_ ) ,(max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(a_ ):
# adds PAD dummy token
_UpperCAmelCase : Dict = min_length + idx + 1
_UpperCAmelCase : List[str] = min_length + idx + 1
_UpperCAmelCase : Optional[Any] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] ,[expected_shape] * len(a_ ) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_=False ,a_=1 ) -> List[str]:
self.assertIsInstance(a_ ,a_ )
self.assertListEqual(
[isinstance(a_ ,a_ ) for iter_hidden_states in hidden_states] ,[True] * len(a_ ) ,)
self.assertEqual(len(a_ ) ,(max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(a_ ):
# adds PAD dummy token
_UpperCAmelCase : Tuple = min_length + idx + 1
_UpperCAmelCase : List[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] ,[expected_shape] * len(a_ ) ,)
pass
@slow
def _snake_case ( self ) -> int:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = XLMModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[Any] = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(a_ )
_UpperCAmelCase : Union[str, Any] = torch.tensor([[14, 447]] ,dtype=torch.long ,device=a_ ) # the president
_UpperCAmelCase : Optional[int] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_UpperCAmelCase : str = model.generate(a_ ,do_sample=a_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() ,a_ )
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,) -> Dict:
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Tuple = 13
_UpperCAmelCase : Union[str, Any] = 7
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : int = False
_UpperCAmelCase : List[str] = True
_UpperCAmelCase : Optional[int] = 99
_UpperCAmelCase : int = 32
_UpperCAmelCase : Any = 2
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Optional[int] = 37
_UpperCAmelCase : List[str] = """gelu"""
_UpperCAmelCase : List[Any] = 0.1
_UpperCAmelCase : List[Any] = 0.1
_UpperCAmelCase : int = 512
_UpperCAmelCase : Tuple = 16
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Optional[int] = 0.02
_UpperCAmelCase : int = 3
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Dict = None
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCAmelCase : List[Any] = None
if self.use_input_mask:
_UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : str = None
_UpperCAmelCase : Union[str, Any] = None
if self.use_labels:
_UpperCAmelCase : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_UpperCAmelCase : str = ids_tensor([self.batch_size] ,self.num_choices )
_UpperCAmelCase : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = TFDistilBertModel(config=a_ )
_UpperCAmelCase : Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Tuple = model(a_ )
_UpperCAmelCase : List[str] = [input_ids, input_mask]
_UpperCAmelCase : str = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> str:
_UpperCAmelCase : Tuple = TFDistilBertForMaskedLM(config=a_ )
_UpperCAmelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = TFDistilBertForQuestionAnswering(config=a_ )
_UpperCAmelCase : Union[str, Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
_UpperCAmelCase : str = 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 _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> int:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : Union[str, Any] = TFDistilBertForSequenceClassification(a_ )
_UpperCAmelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = self.num_choices
_UpperCAmelCase : Optional[int] = TFDistilBertForMultipleChoice(a_ )
_UpperCAmelCase : List[str] = tf.tile(tf.expand_dims(a_ ,1 ) ,(1, self.num_choices, 1) )
_UpperCAmelCase : Optional[int] = tf.tile(tf.expand_dims(a_ ,1 ) ,(1, self.num_choices, 1) )
_UpperCAmelCase : Dict = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
_UpperCAmelCase : Tuple = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> int:
_UpperCAmelCase : Optional[Any] = self.num_labels
_UpperCAmelCase : List[Any] = TFDistilBertForTokenClassification(a_ )
_UpperCAmelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_UpperCAmelCase : Optional[int] = model(a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = self.prepare_config_and_inputs()
((_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase)) : str = config_and_inputs
_UpperCAmelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
UpperCAmelCase = (
{
"""feature-extraction""": TFDistilBertModel,
"""fill-mask""": TFDistilBertForMaskedLM,
"""question-answering""": TFDistilBertForQuestionAnswering,
"""text-classification""": TFDistilBertForSequenceClassification,
"""token-classification""": TFDistilBertForTokenClassification,
"""zero-shot""": TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = TFDistilBertModelTester(self )
_UpperCAmelCase : Any = ConfigTester(self ,config_class=a_ ,dim=37 )
def _snake_case ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a_ )
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a_ )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a_ )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a_ )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a_ )
def _snake_case ( self ) -> str:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a_ )
@slow
def _snake_case ( self ) -> List[Any]:
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
_UpperCAmelCase : Optional[int] = TFDistilBertModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_tf
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Tuple = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
_UpperCAmelCase : Tuple = tf.constant([[0, 1, 2, 3, 4, 5]] )
_UpperCAmelCase : List[str] = model(a_ )[0]
_UpperCAmelCase : int = [1, 6, 768]
self.assertEqual(output.shape ,a_ )
_UpperCAmelCase : Tuple = tf.constant(
[
[
[0.1926_1885, -0.1373_2955, 0.411_9799],
[0.2215_0156, -0.0742_2661, 0.3903_7204],
[0.2275_6018, -0.089_6414, 0.370_1467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,a_ ,atol=1E-4 )
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import requests
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : List[Any] = {"""Content-Type""": """application/json"""}
_UpperCAmelCase : Optional[Any] = requests.post(lowerCAmelCase_ , json={"""text""": message_body} , headers=lowerCAmelCase_ )
if response.status_code != 200:
_UpperCAmelCase : str = (
"""Request to slack returned an error """
F'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(lowerCAmelCase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")
| 349 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A_ : Dict = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """sequence-classification"""
def __init__( self ,a_ ) -> Dict:
if type(a_ ) == dict:
_UpperCAmelCase : Tuple = Namespace(**a_ )
_UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task]
_UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(a_ ,a_ ,self.mode )
def _snake_case ( self ,**a_ ) -> Optional[Any]:
return self.model(**a_ )
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : Any = self(**a_ )
_UpperCAmelCase : int = outputs[0]
_UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""]
_UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = self.hparams
_UpperCAmelCase : int = processors[args.task]()
_UpperCAmelCase : str = processor.get_labels()
for mode in ["train", "dev"]:
_UpperCAmelCase : Tuple = self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,a_ )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
_UpperCAmelCase : List[Any] = (
processor.get_dev_examples(args.data_dir )
if mode == """dev"""
else processor.get_train_examples(args.data_dir )
)
_UpperCAmelCase : Union[str, Any] = convert_examples_to_features(
a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,)
logger.info("""Saving features into cached file %s""" ,a_ )
torch.save(a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader:
_UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode
_UpperCAmelCase : Tuple = self._feature_file(a_ )
logger.info("""Loading features from cached file %s""" ,a_ )
_UpperCAmelCase : Union[str, Any] = torch.load(a_ )
_UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
_UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
_UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float )
return DataLoader(
TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,)
def _snake_case ( self ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : List[str] = self(**a_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2]
_UpperCAmelCase : List[str] = logits.detach().cpu().numpy()
_UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self ,a_ ) -> tuple:
_UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
_UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : int = np.argmax(a_ ,axis=1 )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : Union[str, Any] = np.squeeze(a_ )
_UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
_UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )}
_UpperCAmelCase : Dict = dict(results.items() )
_UpperCAmelCase : Any = results
return ret, preds_list, out_label_list
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( a_ ,a_ ) -> Any:
BaseTransformer.add_model_specific_args(a_ ,a_ )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=a_ ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
add_generic_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_UpperCAmelCase : Optional[int] = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ )
_UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) )
_UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """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-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 | 1 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_ ,a_ ) -> List[str]:
_UpperCAmelCase : List[Any] = name
_UpperCAmelCase : Dict = value
_UpperCAmelCase : Any = weight
def __repr__( self ) -> Dict:
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _snake_case ( self ) -> Any:
return self.value
def _snake_case ( self ) -> Tuple:
return self.name
def _snake_case ( self ) -> Optional[Any]:
return self.weight
def _snake_case ( self ) -> List[Any]:
return self.value / self.weight
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
_UpperCAmelCase : Any = []
for i in range(len(lowerCAmelCase_ ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = sorted(lowerCAmelCase_ , key=lowerCAmelCase_ , reverse=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = 0.0, 0.0
for i in range(len(lowerCAmelCase_ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def snake_case_ ( )-> List[str]:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@slow
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,"""prajjwal1/bert-tiny""" )
_UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_UpperCAmelCase : List[Any] = bertabert.config.encoder.vocab_size
_UpperCAmelCase : Optional[int] = tokenizer.sep_token_id
_UpperCAmelCase : Union[str, Any] = tokenizer.cls_token_id
_UpperCAmelCase : str = 128
_UpperCAmelCase : List[str] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""train[:1%]""" )
_UpperCAmelCase : Union[str, Any] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""validation[:1%]""" )
_UpperCAmelCase : Any = train_dataset.select(range(32 ) )
_UpperCAmelCase : Any = val_dataset.select(range(16 ) )
_UpperCAmelCase : List[Any] = 4
def _map_to_encoder_decoder_inputs(a_ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_UpperCAmelCase : int = tokenizer(batch["""article"""] ,padding="""max_length""" ,truncation=a_ ,max_length=512 )
_UpperCAmelCase : Tuple = tokenizer(batch["""highlights"""] ,padding="""max_length""" ,truncation=a_ ,max_length=128 )
_UpperCAmelCase : int = inputs.input_ids
_UpperCAmelCase : Union[str, Any] = inputs.attention_mask
_UpperCAmelCase : Union[str, Any] = outputs.input_ids
_UpperCAmelCase : Dict = outputs.input_ids.copy()
_UpperCAmelCase : Dict = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
_UpperCAmelCase : Optional[int] = outputs.attention_mask
assert all(len(a_ ) == 512 for x in inputs.input_ids )
assert all(len(a_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(a_ ):
_UpperCAmelCase : Optional[int] = pred.label_ids
_UpperCAmelCase : Optional[int] = pred.predictions
# all unnecessary tokens are removed
_UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : str = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : Tuple = sum([int(pred_str[i] == label_str[i] ) for i in range(len(a_ ) )] ) / len(a_ )
return {"accuracy": accuracy}
# map train dataset
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
train_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
# same for validation dataset
_UpperCAmelCase : List[str] = val_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
val_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
_UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : List[str] = SeqaSeqTrainingArguments(
output_dir=a_ ,per_device_train_batch_size=a_ ,per_device_eval_batch_size=a_ ,predict_with_generate=a_ ,evaluation_strategy="""steps""" ,do_train=a_ ,do_eval=a_ ,warmup_steps=0 ,eval_steps=2 ,logging_steps=2 ,)
# instantiate trainer
_UpperCAmelCase : int = SeqaSeqTrainer(
model=a_ ,args=a_ ,compute_metrics=_compute_metrics ,train_dataset=a_ ,eval_dataset=a_ ,tokenizer=a_ ,)
# start training
trainer.train()
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> bool:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : Any = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCAmelCase_ )
if number < 0:
return False
_UpperCAmelCase : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A_ : List[Any] = 637_8137.0
A_ : Dict = 635_6752.31_4245
A_ : int = 6_3_7_8_1_3_7
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> float:
'''simple docstring'''
_UpperCAmelCase : Tuple = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_UpperCAmelCase : Any = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
_UpperCAmelCase : Optional[Any] = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_UpperCAmelCase : Union[str, Any] = haversine_distance(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_UpperCAmelCase : Optional[int] = (b_lata + b_lata) / 2
_UpperCAmelCase : Any = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_UpperCAmelCase : List[str] = (sin(lowerCAmelCase_ ) ** 2) * (cos(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = cos(sigma / 2 ) ** 2
_UpperCAmelCase : Dict = (sigma - sin(lowerCAmelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_UpperCAmelCase : Union[str, Any] = (cos(lowerCAmelCase_ ) ** 2) * (sin(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = sin(sigma / 2 ) ** 2
_UpperCAmelCase : Optional[Any] = (sigma + sin(lowerCAmelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 | 1 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_ = 13 ,a_ = 64 ,a_ = 2 ,a_ = 3 ,a_ = 3 ,a_ = True ,a_ = True ,a_ = 128 ,a_=[16, 32, 64, 128] ,a_ = 7 ,a_ = 4 ,a_ = 37 ,a_ = "gelu" ,a_ = 0.1 ,a_ = 0.1 ,a_ = 10 ,a_ = 0.02 ,a_ = 2 ,a_ = 1 ,a_ = 128 ,a_ = [2, 2, 2, 2] ,a_ = 2 ,a_ = 2 ,) -> Tuple:
_UpperCAmelCase : int = parent
_UpperCAmelCase : Optional[Any] = batch_size
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Optional[int] = patch_size
_UpperCAmelCase : int = num_channels
_UpperCAmelCase : List[str] = is_training
_UpperCAmelCase : Optional[int] = use_labels
_UpperCAmelCase : Union[str, Any] = hidden_size
_UpperCAmelCase : Tuple = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : Any = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : Any = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : int = type_sequence_label_size
_UpperCAmelCase : Dict = initializer_range
_UpperCAmelCase : List[str] = encoder_stride
_UpperCAmelCase : Optional[Any] = num_attention_outputs
_UpperCAmelCase : Any = embed_dim
_UpperCAmelCase : Union[str, Any] = embed_dim + 1
_UpperCAmelCase : List[Any] = resolution
_UpperCAmelCase : Dict = depths
_UpperCAmelCase : int = hidden_sizes
_UpperCAmelCase : List[str] = dim
_UpperCAmelCase : str = mlp_expansion_ratio
def _snake_case ( self ) -> int:
_UpperCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : str = None
if self.use_labels:
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self ) -> str:
return EfficientFormerConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=a_ ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,resolution=self.resolution ,depths=self.depths ,hidden_sizes=self.hidden_sizes ,dim=self.dim ,mlp_expansion_ratio=self.mlp_expansion_ratio ,)
def _snake_case ( self ,a_ ,a_ ,a_ ) -> int:
_UpperCAmelCase : str = TFEfficientFormerModel(config=a_ )
_UpperCAmelCase : Union[str, Any] = model(a_ ,training=a_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Any:
_UpperCAmelCase : int = self.type_sequence_label_size
_UpperCAmelCase : Union[str, Any] = TFEfficientFormerForImageClassification(a_ )
_UpperCAmelCase : Optional[Any] = model(a_ ,labels=a_ ,training=a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_UpperCAmelCase : str = 1
_UpperCAmelCase : Any = TFEfficientFormerForImageClassification(a_ )
_UpperCAmelCase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCAmelCase : List[Any] = model(a_ ,labels=a_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : List[str] = config_and_inputs
_UpperCAmelCase : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
UpperCAmelCase = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Optional[Any] = TFEfficientFormerModelTester(self )
_UpperCAmelCase : Tuple = ConfigTester(
self ,config_class=a_ ,has_text_modality=a_ ,hidden_size=37 )
def _snake_case ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def _snake_case ( self ) -> Optional[int]:
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def _snake_case ( self ) -> Optional[Any]:
pass
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(a_ )
_UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Dict = [*signature.parameters.keys()]
_UpperCAmelCase : Union[str, Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,a_ )
def _snake_case ( self ) -> int:
def check_hidden_states_output(a_ ,a_ ,a_ ):
_UpperCAmelCase : Any = model_class(a_ )
_UpperCAmelCase : Optional[int] = model(**self._prepare_for_class(a_ ,a_ ) ,training=a_ )
_UpperCAmelCase : List[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : Optional[int] = getattr(
self.model_tester ,"""expected_num_hidden_layers""" ,self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(a_ ) ,a_ )
if hasattr(self.model_tester ,"""encoder_seq_length""" ):
_UpperCAmelCase : Any = self.model_tester.encoder_seq_length
if hasattr(self.model_tester ,"""chunk_length""" ) and self.model_tester.chunk_length > 1:
_UpperCAmelCase : Optional[int] = seq_length * self.model_tester.chunk_length
else:
_UpperCAmelCase : Optional[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) ,[seq_length, self.model_tester.hidden_size] ,)
if config.is_encoder_decoder:
_UpperCAmelCase : Tuple = outputs.decoder_hidden_states
self.asseretIsInstance(a_ ,(list, tuple) )
self.assertEqual(len(a_ ) ,a_ )
_UpperCAmelCase : Optional[Any] = getattr(self.model_tester ,"""seq_length""" ,a_ )
_UpperCAmelCase : Optional[Any] = getattr(self.model_tester ,"""decoder_seq_length""" ,a_ )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) ,[decoder_seq_length, self.model_tester.hidden_size] ,)
_UpperCAmelCase ,_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : str = True
check_hidden_states_output(a_ ,a_ ,a_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : List[str] = True
check_hidden_states_output(a_ ,a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_=False ) -> List[Any]:
_UpperCAmelCase : Optional[Any] = super()._prepare_for_class(a_ ,a_ ,return_labels=a_ )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
@slow
def _snake_case ( self ) -> Tuple:
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : int = TFEfficientFormerModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase ,_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = True
_UpperCAmelCase : str = getattr(self.model_tester ,"""seq_length""" ,a_ )
_UpperCAmelCase : Any = getattr(self.model_tester ,"""encoder_seq_length""" ,a_ )
_UpperCAmelCase : str = getattr(self.model_tester ,"""key_length""" ,a_ )
_UpperCAmelCase : List[Any] = getattr(self.model_tester ,"""chunk_length""" ,a_ )
if chunk_length is not None and hasattr(self.model_tester ,"""num_hashes""" ):
_UpperCAmelCase : int = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
_UpperCAmelCase : Any = True
_UpperCAmelCase : str = False
_UpperCAmelCase : int = True
_UpperCAmelCase : Any = model_class(a_ )
_UpperCAmelCase : str = model(**self._prepare_for_class(a_ ,a_ ) ,training=a_ )
_UpperCAmelCase : Optional[int] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(a_ ) ,self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase : Dict = True
_UpperCAmelCase : List[str] = model_class(a_ )
_UpperCAmelCase : str = model(**self._prepare_for_class(a_ ,a_ ) ,training=a_ )
_UpperCAmelCase : Optional[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(a_ ) ,self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] ,)
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] ,)
def _snake_case ( self ) -> str:
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
_UpperCAmelCase : int = model_class(a_ )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
_UpperCAmelCase : Optional[int] = {
key: tf.keras.Input(shape=val.shape[1:] ,dtype=val.dtype ,name=a_ )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
_UpperCAmelCase : Any = model(a_ )
self.assertTrue(outputs_dict is not None )
def snake_case_ ( )-> str:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self ) -> List[Any]:
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Union[str, Any] = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
_UpperCAmelCase : int = self.default_image_processor
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : Any = image_processor(images=a_ ,return_tensors="""tf""" )
# forward pass
_UpperCAmelCase : str = model(**a_ ,training=a_ )
# verify the logits
_UpperCAmelCase : Tuple = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape ,a_ )
_UpperCAmelCase : List[str] = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] ,a_ ,atol=1E-4 ) )
@slow
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Dict = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
_UpperCAmelCase : Any = self.default_image_processor
_UpperCAmelCase : List[Any] = prepare_img()
_UpperCAmelCase : List[str] = image_processor(images=a_ ,return_tensors="""tf""" )
# forward pass
_UpperCAmelCase : List[str] = model(**a_ ,training=a_ )
# verify the logits
_UpperCAmelCase : Optional[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape ,a_ )
_UpperCAmelCase : Any = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] ,a_ ,atol=1E-4 ) )
| 349 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowerCAmelCase_ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowerCAmelCase_ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowerCAmelCase_ )
return parser.parse_args()
def snake_case_ ( )-> str:
'''simple docstring'''
_UpperCAmelCase : List[str] = parse_args()
# Import training_script as a module.
_UpperCAmelCase : List[Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase : Optional[Any] = script_fpath.stem
_UpperCAmelCase : List[str] = importlib.import_module(lowerCAmelCase_ )
# Patch sys.argv
_UpperCAmelCase : Dict = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ = 2000000 )-> int:
'''simple docstring'''
_UpperCAmelCase : int = [0 for i in range(n + 1 )]
_UpperCAmelCase : int = 1
_UpperCAmelCase : Any = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCAmelCase_ ):
_UpperCAmelCase : Any = 1
_UpperCAmelCase : List[str] = 0
for i in range(lowerCAmelCase_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f"""{solution() = }""")
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict = str(abs(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = [list(lowerCAmelCase_ ) for char in range(len(lowerCAmelCase_ ) )]
for index in range(len(lowerCAmelCase_ ) ):
num_transpositions[index].pop(lowerCAmelCase_ )
return max(
int("""""".join(list(lowerCAmelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A_ : Tuple = logging.get_logger(__name__)
A_ : List[str] = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """dpt"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=384 ,a_=16 ,a_=3 ,a_=False ,a_=True ,a_=[2, 5, 8, 11] ,a_="project" ,a_=[4, 2, 1, 0.5] ,a_=[96, 192, 384, 768] ,a_=256 ,a_=-1 ,a_=False ,a_=True ,a_=0.4 ,a_=255 ,a_=0.1 ,a_=[1, 1_024, 24, 24] ,a_=[0, 1] ,a_=None ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : Any = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("""Initializing the config with a `BiT` backbone.""" )
_UpperCAmelCase : Dict = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
}
_UpperCAmelCase : Optional[Any] = BitConfig(**a_ )
elif isinstance(a_ ,a_ ):
logger.info("""Initializing the config with a `BiT` backbone.""" )
_UpperCAmelCase : Optional[int] = BitConfig(**a_ )
elif isinstance(a_ ,a_ ):
_UpperCAmelCase : Any = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
_UpperCAmelCase : List[str] = backbone_featmap_shape
_UpperCAmelCase : Union[str, Any] = neck_ignore_stages
if readout_type != "project":
raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
else:
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Dict = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : str = layer_norm_eps
_UpperCAmelCase : str = image_size
_UpperCAmelCase : Any = patch_size
_UpperCAmelCase : Any = num_channels
_UpperCAmelCase : Optional[int] = qkv_bias
_UpperCAmelCase : Tuple = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
_UpperCAmelCase : Tuple = readout_type
_UpperCAmelCase : Any = reassemble_factors
_UpperCAmelCase : Optional[int] = neck_hidden_sizes
_UpperCAmelCase : str = fusion_hidden_size
_UpperCAmelCase : str = head_in_index
_UpperCAmelCase : Dict = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase : Dict = use_auxiliary_head
_UpperCAmelCase : Union[str, Any] = auxiliary_loss_weight
_UpperCAmelCase : Dict = semantic_loss_ignore_index
_UpperCAmelCase : List[Any] = semantic_classifier_dropout
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
_UpperCAmelCase : Dict = self.backbone_config.to_dict()
_UpperCAmelCase : List[str] = self.__class__.model_type
return output
| 349 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Dict = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), F'''{len(lowerCAmelCase_ )} != {len(lowerCAmelCase_ )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A_ : Union[str, Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A_ : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
try:
_UpperCAmelCase : Any = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[int]:
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(lowerCAmelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = "student" , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ , )-> Tuple[PreTrainedModel, List[int], List[int]]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
AutoTokenizer.from_pretrained(lowerCAmelCase_ ).save_pretrained(lowerCAmelCase_ ) # purely for convenience
_UpperCAmelCase : Any = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_ ).eval()
else:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), F'''teacher must be a model or string got type {type(lowerCAmelCase_ )}'''
_UpperCAmelCase : str = teacher.config.to_diff_dict()
try:
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
_UpperCAmelCase : Tuple = teacher_e
if d is None:
_UpperCAmelCase : Dict = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
_UpperCAmelCase : List[str] = teacher_e
if d is None:
_UpperCAmelCase : str = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase_ )
# Copy weights
_UpperCAmelCase : Any = teacher.config_class(**lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
_UpperCAmelCase : Optional[Any] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = list(range(lowerCAmelCase_ ) ), list(range(lowerCAmelCase_ ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(lowerCAmelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
if d_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
try:
if hasattr(
lowerCAmelCase_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase_ )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
_UpperCAmelCase : Dict = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 349 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ ( lowerCAmelCase_ )-> Dict[str, torch.Tensor]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Tuple = []
for rt in rc.restypes:
_UpperCAmelCase : Optional[int] = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
_UpperCAmelCase : Any = {name: i for i, name in enumerate(lowerCAmelCase_ )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
_UpperCAmelCase : Optional[Any] = torch.tensor(
lowerCAmelCase_ , dtype=torch.intaa , device=protein["""aatype"""].device , )
_UpperCAmelCase : Any = torch.tensor(
lowerCAmelCase_ , dtype=torch.intaa , device=protein["""aatype"""].device , )
_UpperCAmelCase : Dict = torch.tensor(
lowerCAmelCase_ , dtype=torch.floataa , device=protein["""aatype"""].device , )
_UpperCAmelCase : Optional[Any] = protein["""aatype"""].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
_UpperCAmelCase : List[str] = restype_atomaa_to_atomaa[protein_aatype]
_UpperCAmelCase : Optional[int] = restype_atomaa_mask[protein_aatype]
_UpperCAmelCase : str = residx_atomaa_mask
_UpperCAmelCase : Tuple = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
_UpperCAmelCase : Optional[Any] = restype_atomaa_to_atomaa[protein_aatype]
_UpperCAmelCase : List[Any] = residx_atomaa_to_atomaa.long()
# create the corresponding mask
_UpperCAmelCase : Union[str, Any] = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["""aatype"""].device )
for restype, restype_letter in enumerate(rc.restypes ):
_UpperCAmelCase : Tuple = rc.restype_atoa[restype_letter]
_UpperCAmelCase : str = rc.residue_atoms[restype_name]
for atom_name in atom_names:
_UpperCAmelCase : Optional[int] = rc.atom_order[atom_name]
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : int = restype_atomaa_mask[protein_aatype]
_UpperCAmelCase : Dict = residx_atomaa_mask
return protein
def snake_case_ ( lowerCAmelCase_ )-> Dict[str, np.ndarray]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = tree_map(lambda lowerCAmelCase_ : torch.tensor(lowerCAmelCase_ , device=batch["""aatype"""].device ) , lowerCAmelCase_ , np.ndarray )
_UpperCAmelCase : Optional[int] = tensor_tree_map(lambda lowerCAmelCase_ : np.array(lowerCAmelCase_ ) , make_atomaa_masks(lowerCAmelCase_ ) )
return out
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase_ , lowerCAmelCase_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """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-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_UpperCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter Video/IGTV url: """).strip()
A_ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 349 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A_ : int = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
"""processing_speech_to_text""": ["""Speech2TextProcessor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = ["""Speech2TextTokenizer"""]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = ["""Speech2TextFeatureExtractor"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFSpeech2TextForConditionalGeneration""",
"""TFSpeech2TextModel""",
"""TFSpeech2TextPreTrainedModel""",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = [
"""SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Speech2TextForConditionalGeneration""",
"""Speech2TextModel""",
"""Speech2TextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
A_ : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Union[str, Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(a_ )
return image
@property
def _snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def _snake_case ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def _snake_case ( self ) -> Dict:
torch.manual_seed(0 )
_UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
return CLIPTextModel(a_ )
@property
def _snake_case ( self ) -> Union[str, Any]:
def extract(*a_ ,**a_ ):
class lowercase :
"""simple docstring"""
def __init__( self ) -> Any:
_UpperCAmelCase : str = torch.ones([0] )
def _snake_case ( self ,a_ ) -> Any:
self.pixel_values.to(a_ )
return self
return Out()
return extract
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Union[str, Any] = self.dummy_cond_unet
_UpperCAmelCase : int = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
_UpperCAmelCase : Optional[int] = self.dummy_vae
_UpperCAmelCase : Optional[int] = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : int = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Tuple = self.dummy_cond_unet
_UpperCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : int = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : str = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : List[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=a_ )
assert isinstance(a_ ,a_ )
assert isinstance(pipe.scheduler ,a_ )
assert pipe.safety_checker is None
_UpperCAmelCase : Dict = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained(a_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase : Union[str, Any] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.dummy_cond_unet
_UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : List[str] = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_UpperCAmelCase : str = unet.half()
_UpperCAmelCase : List[str] = vae.half()
_UpperCAmelCase : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : Dict = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : int = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : List[Any] = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCAmelCase : Any = 4_003_660_346
_UpperCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCAmelCase : Optional[Any] = 2_734_971_755
_UpperCAmelCase : Optional[int] = 7
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[int] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCAmelCase : Optional[int] = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Union[str, Any] = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCAmelCase : Dict = 1_044_355_234
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : List[str] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCAmelCase : Tuple = torch.manual_seed(a_ )
_UpperCAmelCase : Dict = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 | 1 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ ( lowerCAmelCase_ )-> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCAmelCase_ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCAmelCase_ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def snake_case_ ( )-> None:
'''simple docstring'''
assert gamma(0.5 ) == sqrt(lowerCAmelCase_ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
A_ : Union[str, Any] = 1.0
while num:
A_ : Optional[Any] = float(input("""Gamma of: """))
print(f"""gamma({num}) = {gamma(num)}""")
print("""\nEnter 0 to exit...""")
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[int] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
A_ : Tuple = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : int = {}
with open(lowerCAmelCase_ , """r""" ) as file:
for line_number, line in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase : int = line.strip()
if line:
_UpperCAmelCase : Tuple = line.split()
_UpperCAmelCase : Optional[int] = line_number
_UpperCAmelCase : str = words[0]
_UpperCAmelCase : Optional[Any] = value
return result
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
for attribute in key.split(""".""" ):
_UpperCAmelCase : Dict = getattr(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : str = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowerCAmelCase_ ):
_UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_UpperCAmelCase : Union[str, Any] = """param"""
if weight_type is not None and weight_type != "param":
_UpperCAmelCase : Optional[int] = getattr(lowerCAmelCase_ , lowerCAmelCase_ ).shape
elif weight_type is not None and weight_type == "param":
_UpperCAmelCase : Optional[Any] = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_UpperCAmelCase : Any = getattr(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : List[str] = shape_pointer.shape
# let's reduce dimension
_UpperCAmelCase : str = value[0]
else:
_UpperCAmelCase : List[str] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_UpperCAmelCase : List[Any] = value
elif weight_type == "weight_g":
_UpperCAmelCase : Optional[int] = value
elif weight_type == "weight_v":
_UpperCAmelCase : Dict = value
elif weight_type == "bias":
_UpperCAmelCase : int = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_UpperCAmelCase : List[Any] = getattr(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = value
else:
_UpperCAmelCase : Tuple = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : Dict = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(lowerCAmelCase_ ):
_UpperCAmelCase : Any = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_UpperCAmelCase : Optional[Any] = """param"""
if weight_type is not None and weight_type != "param":
_UpperCAmelCase : List[str] = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_UpperCAmelCase : Dict = """.""".join([key, hf_param_name] )
else:
_UpperCAmelCase : int = key
_UpperCAmelCase : int = value if """lm_head""" in full_key else value[0]
A_ : Union[str, Any] = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None )-> Any:
'''simple docstring'''
_UpperCAmelCase : int = False
for key, mapped_key in MAPPING.items():
_UpperCAmelCase : Tuple = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_UpperCAmelCase : Tuple = True
if "*" in mapped_key:
_UpperCAmelCase : Dict = name.split(lowerCAmelCase_ )[0].split(""".""" )[-2]
_UpperCAmelCase : Dict = mapped_key.replace("""*""" , lowerCAmelCase_ )
if "weight_g" in name:
_UpperCAmelCase : Optional[Any] = """weight_g"""
elif "weight_v" in name:
_UpperCAmelCase : Optional[Any] = """weight_v"""
elif "bias" in name:
_UpperCAmelCase : Dict = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCAmelCase : List[str] = """weight"""
else:
_UpperCAmelCase : Optional[Any] = None
if hf_dict is not None:
rename_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
else:
set_recursively(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return is_used
return is_used
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Any = []
_UpperCAmelCase : Dict = fairseq_model.state_dict()
_UpperCAmelCase : Optional[Any] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase : int = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , hf_model.config.feat_extract_norm == """group""" , )
_UpperCAmelCase : int = True
else:
_UpperCAmelCase : Dict = load_wavaveca_layer(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if not is_used:
unused_weights.append(lowerCAmelCase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Dict = full_name.split("""conv_layers.""" )[-1]
_UpperCAmelCase : Any = name.split(""".""" )
_UpperCAmelCase : Any = int(items[0] )
_UpperCAmelCase : int = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
_UpperCAmelCase : int = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
_UpperCAmelCase : Any = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
_UpperCAmelCase : Tuple = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
_UpperCAmelCase : int = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowerCAmelCase_ )
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_=False )-> Optional[int]:
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase : List[Any] = WavaVecaConfig.from_pretrained(lowerCAmelCase_ )
else:
_UpperCAmelCase : Optional[Any] = WavaVecaConfig()
if is_seq_class:
_UpperCAmelCase : List[str] = read_txt_into_dict(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = idalabel
_UpperCAmelCase : List[str] = WavaVecaForSequenceClassification(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
feature_extractor.save_pretrained(lowerCAmelCase_ )
elif is_finetuned:
if dict_path:
_UpperCAmelCase : str = Dictionary.load(lowerCAmelCase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCAmelCase : str = target_dict.pad_index
_UpperCAmelCase : List[str] = target_dict.bos_index
_UpperCAmelCase : int = target_dict.eos_index
_UpperCAmelCase : Optional[Any] = len(target_dict.symbols )
_UpperCAmelCase : Union[str, Any] = os.path.join(lowerCAmelCase_ , """vocab.json""" )
if not os.path.isdir(lowerCAmelCase_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase_ ) )
return
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
_UpperCAmelCase : int = target_dict.indices
# fairseq has the <pad> and <s> switched
_UpperCAmelCase : Any = 0
_UpperCAmelCase : int = 1
with open(lowerCAmelCase_ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer(
lowerCAmelCase_ , 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=lowerCAmelCase_ , )
_UpperCAmelCase : int = True if config.feat_extract_norm == """layer""" else False
_UpperCAmelCase : Tuple = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , )
_UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
_UpperCAmelCase : str = WavaVecaForCTC(lowerCAmelCase_ )
else:
_UpperCAmelCase : int = WavaVecaForPreTraining(lowerCAmelCase_ )
if is_finetuned or is_seq_class:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_UpperCAmelCase : Optional[Any] = argparse.Namespace(task="""audio_pretraining""" )
_UpperCAmelCase : List[str] = fairseq.tasks.setup_task(lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowerCAmelCase_ )
_UpperCAmelCase : str = model[0].eval()
recursively_load_weights(lowerCAmelCase_ , lowerCAmelCase_ , not is_finetuned )
hf_wavavec.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
A_ : List[str] = 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(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
A_ : Optional[int] = parser.parse_args()
A_ : List[str] = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A_ : Dict = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LayoutLMv3Config""",
"""LayoutLMv3OnnxConfig""",
],
"""processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
"""tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = ["""LayoutLMv3TokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv3ForQuestionAnswering""",
"""LayoutLMv3ForSequenceClassification""",
"""LayoutLMv3ForTokenClassification""",
"""LayoutLMv3Model""",
"""LayoutLMv3PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
"""TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLayoutLMv3ForQuestionAnswering""",
"""TFLayoutLMv3ForSequenceClassification""",
"""TFLayoutLMv3ForTokenClassification""",
"""TFLayoutLMv3Model""",
"""TFLayoutLMv3PreTrainedModel""",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Any = ["""LayoutLMv3FeatureExtractor"""]
A_ : Optional[int] = ["""LayoutLMv3ImageProcessor"""]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
A_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Dict = [2, 4, 6, 8, 10, 12]
_UpperCAmelCase : Optional[int] = 100
self.assertEqual(kp.calc_profit(a_ ,a_ ,a_ ) ,210 )
def _snake_case ( self ) -> Union[str, Any]:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Any:
self.assertRaisesRegex(a_ ,"""Weight can not be negative.""" )
def _snake_case ( self ) -> Optional[Any]:
self.assertRaisesRegex(a_ ,"""Profit can not be negative.""" )
def _snake_case ( self ) -> Dict:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Tuple:
self.assertRaisesRegex(
a_ ,"""The length of profit and weight must be same.""" )
if __name__ == "__main__":
unittest.main()
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )-> float:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("""Relative densities cannot be greater than one""" )
else:
_UpperCAmelCase : Optional[int] = 1 - (matter_density + radiation_density + dark_energy)
_UpperCAmelCase : int = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
_UpperCAmelCase : Union[str, Any] = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
A_ : Dict = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 349 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : int = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
"""SqueezeBertOnnxConfig""",
],
"""tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = ["""SqueezeBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
"""SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SqueezeBertForMaskedLM""",
"""SqueezeBertForMultipleChoice""",
"""SqueezeBertForQuestionAnswering""",
"""SqueezeBertForSequenceClassification""",
"""SqueezeBertForTokenClassification""",
"""SqueezeBertModel""",
"""SqueezeBertModule""",
"""SqueezeBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
A_ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 | 1 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1e-1_2 )-> List[str]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(lowerCAmelCase_ , axis=1 ) , a_min=lowerCAmelCase_ ) ).T
_UpperCAmelCase : Optional[Any] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(lowerCAmelCase_ , axis=1 ) , a_min=lowerCAmelCase_ ) ).T
return jnp.matmul(lowerCAmelCase_ , norm_emb_a.T )
class lowercase ( nn.Module ):
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = jnp.floataa
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : List[str] = FlaxCLIPVisionModule(self.config.vision_config )
_UpperCAmelCase : Optional[Any] = nn.Dense(self.config.projection_dim ,use_bias=a_ ,dtype=self.dtype )
_UpperCAmelCase : Optional[int] = self.param("""concept_embeds""" ,jax.nn.initializers.ones ,(17, self.config.projection_dim) )
_UpperCAmelCase : List[str] = self.param(
"""special_care_embeds""" ,jax.nn.initializers.ones ,(3, self.config.projection_dim) )
_UpperCAmelCase : List[Any] = self.param("""concept_embeds_weights""" ,jax.nn.initializers.ones ,(17,) )
_UpperCAmelCase : str = self.param("""special_care_embeds_weights""" ,jax.nn.initializers.ones ,(3,) )
def __call__( self ,a_ ) -> List[Any]:
_UpperCAmelCase : str = self.vision_model(a_ )[1]
_UpperCAmelCase : Tuple = self.visual_projection(a_ )
_UpperCAmelCase : str = jax_cosine_distance(a_ ,self.special_care_embeds )
_UpperCAmelCase : Optional[int] = jax_cosine_distance(a_ ,self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
_UpperCAmelCase : str = 0.0
_UpperCAmelCase : List[str] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
_UpperCAmelCase : Any = jnp.round(a_ ,3 )
_UpperCAmelCase : Dict = jnp.any(special_scores > 0 ,axis=1 ,keepdims=a_ )
# Use a lower threshold if an image has any special care concept
_UpperCAmelCase : Union[str, Any] = is_special_care * 0.01
_UpperCAmelCase : List[str] = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
_UpperCAmelCase : str = jnp.round(a_ ,3 )
_UpperCAmelCase : Optional[Any] = jnp.any(concept_scores > 0 ,axis=1 )
return has_nsfw_concepts
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = CLIPConfig
UpperCAmelCase = """clip_input"""
UpperCAmelCase = FlaxStableDiffusionSafetyCheckerModule
def __init__( self ,a_ ,a_ = None ,a_ = 0 ,a_ = jnp.floataa ,a_ = True ,**a_ ,) -> str:
if input_shape is None:
_UpperCAmelCase : List[str] = (1, 224, 224, 3)
_UpperCAmelCase : Union[str, Any] = self.module_class(config=a_ ,dtype=a_ ,**a_ )
super().__init__(a_ ,a_ ,input_shape=a_ ,seed=a_ ,dtype=a_ ,_do_init=_do_init )
def _snake_case ( self ,a_ ,a_ ,a_ = None ) -> FrozenDict:
# init input tensor
_UpperCAmelCase : Optional[Any] = jax.random.normal(a_ ,a_ )
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = jax.random.split(a_ )
_UpperCAmelCase : Tuple = {"""params""": params_rng, """dropout""": dropout_rng}
_UpperCAmelCase : Optional[Any] = self.module.init(a_ ,a_ )["""params"""]
return random_params
def __call__( self ,a_ ,a_ = None ,) -> List[str]:
_UpperCAmelCase : Optional[int] = jnp.transpose(a_ ,(0, 2, 3, 1) )
return self.module.apply(
{"""params""": params or self.params} ,jnp.array(a_ ,dtype=jnp.floataa ) ,rngs={} ,)
| 349 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A_ : Dict = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """sequence-classification"""
def __init__( self ,a_ ) -> Dict:
if type(a_ ) == dict:
_UpperCAmelCase : Tuple = Namespace(**a_ )
_UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task]
_UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(a_ ,a_ ,self.mode )
def _snake_case ( self ,**a_ ) -> Optional[Any]:
return self.model(**a_ )
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : Any = self(**a_ )
_UpperCAmelCase : int = outputs[0]
_UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""]
_UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = self.hparams
_UpperCAmelCase : int = processors[args.task]()
_UpperCAmelCase : str = processor.get_labels()
for mode in ["train", "dev"]:
_UpperCAmelCase : Tuple = self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,a_ )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
_UpperCAmelCase : List[Any] = (
processor.get_dev_examples(args.data_dir )
if mode == """dev"""
else processor.get_train_examples(args.data_dir )
)
_UpperCAmelCase : Union[str, Any] = convert_examples_to_features(
a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,)
logger.info("""Saving features into cached file %s""" ,a_ )
torch.save(a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader:
_UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode
_UpperCAmelCase : Tuple = self._feature_file(a_ )
logger.info("""Loading features from cached file %s""" ,a_ )
_UpperCAmelCase : Union[str, Any] = torch.load(a_ )
_UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
_UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
_UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float )
return DataLoader(
TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,)
def _snake_case ( self ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : List[str] = self(**a_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2]
_UpperCAmelCase : List[str] = logits.detach().cpu().numpy()
_UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self ,a_ ) -> tuple:
_UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
_UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : int = np.argmax(a_ ,axis=1 )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : Union[str, Any] = np.squeeze(a_ )
_UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
_UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )}
_UpperCAmelCase : Dict = dict(results.items() )
_UpperCAmelCase : Any = results
return ret, preds_list, out_label_list
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( a_ ,a_ ) -> Any:
BaseTransformer.add_model_specific_args(a_ ,a_ )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=a_ ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
add_generic_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_UpperCAmelCase : Optional[int] = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ )
_UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) )
_UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
A_ : List[str] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
A_ : List[Any] = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A_ : int = [2, 4, 1, 5]
A_ : Optional[Any] = len(train_data)
A_ : Optional[Any] = 0.009
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_="train" )-> List[str]:
'''simple docstring'''
return calculate_hypothesis_value(lowerCAmelCase_ , lowerCAmelCase_ ) - output(
lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Dict = 0
for i in range(len(lowerCAmelCase_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=m )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = 0
for i in range(lowerCAmelCase_ ):
if index == -1:
summation_value += _error(lowerCAmelCase_ )
else:
summation_value += _error(lowerCAmelCase_ ) * train_data[i][0][index]
return summation_value
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : int = summation_of_cost_derivative(lowerCAmelCase_ , lowerCAmelCase_ ) / m
return cost_derivative_value
def snake_case_ ( )-> Any:
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_UpperCAmelCase : Optional[int] = 0.0_0_0_0_0_2
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = 0
while True:
j += 1
_UpperCAmelCase : List[str] = [0, 0, 0, 0]
for i in range(0 , len(lowerCAmelCase_ ) ):
_UpperCAmelCase : Tuple = get_cost_derivative(i - 1 )
_UpperCAmelCase : List[Any] = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCAmelCase_ , lowerCAmelCase_ , atol=lowerCAmelCase_ , rtol=lowerCAmelCase_ , ):
break
_UpperCAmelCase : str = temp_parameter_vector
print(("""Number of iterations:""", j) )
def snake_case_ ( )-> List[str]:
'''simple docstring'''
for i in range(len(lowerCAmelCase_ ) ):
print(("""Actual output value:""", output(lowerCAmelCase_ , """test""" )) )
print(("""Hypothesis output:""", calculate_hypothesis_value(lowerCAmelCase_ , """test""" )) )
if __name__ == "__main__":
run_gradient_descent()
print("""\nTesting gradient descent for a linear hypothesis function.\n""")
test_gradient_descent()
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
import baseaa
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
return baseaa.baadecode(lowerCAmelCase_ ).decode("""utf-8""" )
if __name__ == "__main__":
A_ : Dict = """Hello World!"""
A_ : Any = baseaa_encode(test)
print(encoded)
A_ : Optional[int] = baseaa_decode(encoded)
print(decoded)
| 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
A_ : Any = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """dpr"""
def __init__( self ,a_=30_522 ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=2 ,a_=0.02 ,a_=1E-1_2 ,a_=0 ,a_="absolute" ,a_ = 0 ,**a_ ,) -> Dict:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : Any = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : Optional[int] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : Dict = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : Optional[int] = projection_dim
_UpperCAmelCase : Tuple = position_embedding_type
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
A_ : Optional[Any] = threading.Lock()
A_ : Optional[logging.Handler] = None
A_ : Dict = {
"""debug""": logging.DEBUG,
"""info""": logging.INFO,
"""warning""": logging.WARNING,
"""error""": logging.ERROR,
"""critical""": logging.CRITICAL,
}
A_ : List[str] = logging.WARNING
A_ : Dict = True
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : List[str] = os.getenv("""TRANSFORMERS_VERBOSITY""" , lowerCAmelCase_ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'''Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '''
F'''has to be one of: { ', '.join(log_levels.keys() ) }''' )
return _default_log_level
def snake_case_ ( )-> str:
'''simple docstring'''
return __name__.split(""".""" )[0]
def snake_case_ ( )-> logging.Logger:
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def snake_case_ ( )-> None:
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_UpperCAmelCase : List[str] = logging.StreamHandler() # Set sys.stderr as stream.
_UpperCAmelCase : int = sys.stderr.flush
# Apply our default configuration to the library root logger.
_UpperCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
_UpperCAmelCase : Dict = False
def snake_case_ ( )-> None:
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
_UpperCAmelCase : Union[str, Any] = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
_UpperCAmelCase : List[Any] = None
def snake_case_ ( )-> List[Any]:
'''simple docstring'''
return log_levels
def snake_case_ ( lowerCAmelCase_ = None )-> logging.Logger:
'''simple docstring'''
if name is None:
_UpperCAmelCase : str = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(lowerCAmelCase_ )
def snake_case_ ( )-> int:
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(lowerCAmelCase_ )
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
return set_verbosity(lowerCAmelCase_ )
def snake_case_ ( )-> int:
'''simple docstring'''
return set_verbosity(lowerCAmelCase_ )
def snake_case_ ( )-> Tuple:
'''simple docstring'''
return set_verbosity(lowerCAmelCase_ )
def snake_case_ ( )-> str:
'''simple docstring'''
return set_verbosity(lowerCAmelCase_ )
def snake_case_ ( )-> None:
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def snake_case_ ( )-> None:
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(lowerCAmelCase_ )
def snake_case_ ( )-> None:
'''simple docstring'''
_configure_library_root_logger()
_UpperCAmelCase : Dict = False
def snake_case_ ( )-> None:
'''simple docstring'''
_configure_library_root_logger()
_UpperCAmelCase : List[Any] = True
def snake_case_ ( )-> None:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = _get_library_root_logger().handlers
for handler in handlers:
_UpperCAmelCase : Optional[Any] = logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s""" )
handler.setFormatter(lowerCAmelCase_ )
def snake_case_ ( )-> None:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(lowerCAmelCase_ )
def snake_case_ ( self , *lowerCAmelCase_ , **lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS""" , lowerCAmelCase_ )
if no_advisory_warnings:
return
self.warning(*lowerCAmelCase_ , **lowerCAmelCase_ )
A_ : Any = warning_advice
@functools.lru_cache(lowerCAmelCase_ )
def snake_case_ ( self , *lowerCAmelCase_ , **lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
self.warning(*lowerCAmelCase_ , **lowerCAmelCase_ )
A_ : Optional[Any] = warning_once
class lowercase :
"""simple docstring"""
def __init__( self ,*a_ ,**a_ ) -> List[str]: # pylint: disable=unused-argument
_UpperCAmelCase : Optional[Any] = args[0] if args else None
def __iter__( self ) -> Dict:
return iter(self._iterator )
def __getattr__( self ,a_ ) -> Any:
def empty_fn(*a_ ,**a_ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> List[str]:
return self
def __exit__( self ,a_ ,a_ ,a_ ) -> Any:
return
class lowercase :
"""simple docstring"""
def __call__( self ,*a_ ,**a_ ) -> Optional[int]:
if _tqdm_active:
return tqdm_lib.tqdm(*a_ ,**a_ )
else:
return EmptyTqdm(*a_ ,**a_ )
def _snake_case ( self ,*a_ ,**a_ ) -> Optional[Any]:
_UpperCAmelCase : str = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*a_ ,**a_ )
def _snake_case ( self ) -> str:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A_ : str = _tqdm_cls()
def snake_case_ ( )-> bool:
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def snake_case_ ( )-> Optional[Any]:
'''simple docstring'''
global _tqdm_active
_UpperCAmelCase : Optional[Any] = True
hf_hub_utils.enable_progress_bars()
def snake_case_ ( )-> List[str]:
'''simple docstring'''
global _tqdm_active
_UpperCAmelCase : int = False
hf_hub_utils.disable_progress_bars()
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A_ : Dict = logging.get_logger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """AutoTokenizer"""
UpperCAmelCase = ["""tokenizer"""]
UpperCAmelCase = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self ,a_ ,a_=None ) -> Any:
super().__init__(a_ )
_UpperCAmelCase : Optional[int] = speaker_embeddings
@classmethod
def _snake_case ( cls ,a_ ,a_="speaker_embeddings_path.json" ,**a_ ) -> Any:
if speaker_embeddings_dict_path is not None:
_UpperCAmelCase : Optional[Any] = get_file_from_repo(
a_ ,a_ ,subfolder=kwargs.pop("""subfolder""" ,a_ ) ,cache_dir=kwargs.pop("""cache_dir""" ,a_ ) ,force_download=kwargs.pop("""force_download""" ,a_ ) ,proxies=kwargs.pop("""proxies""" ,a_ ) ,resume_download=kwargs.pop("""resume_download""" ,a_ ) ,local_files_only=kwargs.pop("""local_files_only""" ,a_ ) ,use_auth_token=kwargs.pop("""use_auth_token""" ,a_ ) ,revision=kwargs.pop("""revision""" ,a_ ) ,)
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(a_ ,a_ )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
_UpperCAmelCase : Optional[int] = None
else:
with open(a_ ) as speaker_embeddings_json:
_UpperCAmelCase : Optional[int] = json.load(a_ )
else:
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Dict = AutoTokenizer.from_pretrained(a_ ,**a_ )
return cls(tokenizer=a_ ,speaker_embeddings=a_ )
def _snake_case ( self ,a_ ,a_="speaker_embeddings_path.json" ,a_="speaker_embeddings" ,a_ = False ,**a_ ,) -> Optional[Any]:
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(a_ ,a_ ,"""v2""" ) ,exist_ok=a_ )
_UpperCAmelCase : int = {}
_UpperCAmelCase : Union[str, Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
_UpperCAmelCase : List[Any] = self._load_voice_preset(a_ )
_UpperCAmelCase : List[Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["""repo_or_path"""] ,a_ ,f'''{prompt_key}_{key}''' ) ,voice_preset[key] ,allow_pickle=a_ ,)
_UpperCAmelCase : List[Any] = os.path.join(a_ ,f'''{prompt_key}_{key}.npy''' )
_UpperCAmelCase : int = tmp_dict
with open(os.path.join(a_ ,a_ ) ,"""w""" ) as fp:
json.dump(a_ ,a_ )
super().save_pretrained(a_ ,a_ ,**a_ )
def _snake_case ( self ,a_ = None ,**a_ ) -> Tuple:
_UpperCAmelCase : int = self.speaker_embeddings[voice_preset]
_UpperCAmelCase : Any = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
_UpperCAmelCase : int = get_file_from_repo(
self.speaker_embeddings.get("""repo_or_path""" ,"""/""" ) ,voice_preset_paths[key] ,subfolder=kwargs.pop("""subfolder""" ,a_ ) ,cache_dir=kwargs.pop("""cache_dir""" ,a_ ) ,force_download=kwargs.pop("""force_download""" ,a_ ) ,proxies=kwargs.pop("""proxies""" ,a_ ) ,resume_download=kwargs.pop("""resume_download""" ,a_ ) ,local_files_only=kwargs.pop("""local_files_only""" ,a_ ) ,use_auth_token=kwargs.pop("""use_auth_token""" ,a_ ) ,revision=kwargs.pop("""revision""" ,a_ ) ,)
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' ,'/' ) ,voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
_UpperCAmelCase : Tuple = np.load(a_ )
return voice_preset_dict
def _snake_case ( self ,a_ = None ) -> Optional[int]:
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] ,np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self ,a_=None ,a_=None ,a_="pt" ,a_=256 ,a_=False ,a_=True ,a_=False ,**a_ ,) -> Tuple:
if voice_preset is not None and not isinstance(a_ ,a_ ):
if (
isinstance(a_ ,a_ )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
_UpperCAmelCase : int = self._load_voice_preset(a_ )
else:
if isinstance(a_ ,a_ ) and not voice_preset.endswith(""".npz""" ):
_UpperCAmelCase : Optional[Any] = voice_preset + """.npz"""
_UpperCAmelCase : Optional[Any] = np.load(a_ )
if voice_preset is not None:
self._validate_voice_preset_dict(a_ ,**a_ )
_UpperCAmelCase : int = BatchFeature(data=a_ ,tensor_type=a_ )
_UpperCAmelCase : Tuple = self.tokenizer(
a_ ,return_tensors=a_ ,padding="""max_length""" ,max_length=a_ ,return_attention_mask=a_ ,return_token_type_ids=a_ ,add_special_tokens=a_ ,**a_ ,)
if voice_preset is not None:
_UpperCAmelCase : Dict = voice_preset
return encoded_text
| 349 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
A_ : Dict = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """sequence-classification"""
def __init__( self ,a_ ) -> Dict:
if type(a_ ) == dict:
_UpperCAmelCase : Tuple = Namespace(**a_ )
_UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task]
_UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task]
super().__init__(a_ ,a_ ,self.mode )
def _snake_case ( self ,**a_ ) -> Optional[Any]:
return self.model(**a_ )
def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : Any = self(**a_ )
_UpperCAmelCase : int = outputs[0]
_UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""]
_UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = self.hparams
_UpperCAmelCase : int = processors[args.task]()
_UpperCAmelCase : str = processor.get_labels()
for mode in ["train", "dev"]:
_UpperCAmelCase : Tuple = self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,a_ )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
_UpperCAmelCase : List[Any] = (
processor.get_dev_examples(args.data_dir )
if mode == """dev"""
else processor.get_train_examples(args.data_dir )
)
_UpperCAmelCase : Union[str, Any] = convert_examples_to_features(
a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,)
logger.info("""Saving features into cached file %s""" ,a_ )
torch.save(a_ ,a_ )
def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader:
_UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode
_UpperCAmelCase : Tuple = self._feature_file(a_ )
logger.info("""Loading features from cached file %s""" ,a_ )
_UpperCAmelCase : Union[str, Any] = torch.load(a_ )
_UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
_UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
_UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float )
return DataLoader(
TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,)
def _snake_case ( self ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None
_UpperCAmelCase : List[str] = self(**a_ )
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2]
_UpperCAmelCase : List[str] = logits.detach().cpu().numpy()
_UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _snake_case ( self ,a_ ) -> tuple:
_UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item()
_UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
if self.hparams.glue_output_mode == "classification":
_UpperCAmelCase : int = np.argmax(a_ ,axis=1 )
elif self.hparams.glue_output_mode == "regression":
_UpperCAmelCase : Union[str, Any] = np.squeeze(a_ )
_UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
_UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
_UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )}
_UpperCAmelCase : Dict = dict(results.items() )
_UpperCAmelCase : Any = results
return ret, preds_list, out_label_list
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _snake_case ( self ,a_ ) -> dict:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ )
_UpperCAmelCase : List[Any] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _snake_case ( a_ ,a_ ) -> Any:
BaseTransformer.add_model_specific_args(a_ ,a_ )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=a_ ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
def snake_case_ ( )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
add_generic_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() )
_UpperCAmelCase : Optional[int] = parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_UpperCAmelCase : Optional[int] = os.path.join(
"""./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ )
_UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) )
_UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
A_ : Dict = """docs/source/en/_toctree.yml"""
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] = defaultdict(lowerCAmelCase_ )
for doc in model_doc:
counts[doc["local"]] += 1
_UpperCAmelCase : Any = [key for key, value in counts.items() if value > 1]
_UpperCAmelCase : Tuple = []
for duplicate_key in duplicates:
_UpperCAmelCase : List[Any] = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(lowerCAmelCase_ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(lowerCAmelCase_ , key=lambda lowerCAmelCase_ : s["title"].lower() )
def snake_case_ ( lowerCAmelCase_=False )-> List[str]:
'''simple docstring'''
with open(lowerCAmelCase_ , encoding="""utf-8""" ) as f:
_UpperCAmelCase : Union[str, Any] = yaml.safe_load(f.read() )
# Get to the API doc
_UpperCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_UpperCAmelCase : str = content[api_idx]["""sections"""]
# Then to the model doc
_UpperCAmelCase : Optional[Any] = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_UpperCAmelCase : Tuple = api_doc[model_idx]["""sections"""]
_UpperCAmelCase : int = [(idx, section) for idx, section in enumerate(lowerCAmelCase_ ) if """sections""" in section]
_UpperCAmelCase : Union[str, Any] = False
for idx, modality_doc in modalities_docs:
_UpperCAmelCase : Union[str, Any] = modality_doc["""sections"""]
_UpperCAmelCase : int = clean_model_doc_toc(lowerCAmelCase_ )
if old_modality_doc != new_modality_doc:
_UpperCAmelCase : Any = True
if overwrite:
_UpperCAmelCase : Union[str, Any] = new_modality_doc
if diff:
if overwrite:
_UpperCAmelCase : List[Any] = model_doc
_UpperCAmelCase : int = api_doc
with open(lowerCAmelCase_ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(lowerCAmelCase_ , allow_unicode=lowerCAmelCase_ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
A_ : List[str] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """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-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
A_ : List[Any] = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
try:
with open(lowerCAmelCase_ , """rb""" ) as flax_state_f:
_UpperCAmelCase : str = from_bytes(lowerCAmelCase_ , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(lowerCAmelCase_ ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. ''' )
return load_flax_weights_in_pytorch_model(lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
_UpperCAmelCase : Union[str, Any] = flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_ : x.dtype == jnp.bfloataa , lowerCAmelCase_ ) ).values()
if any(lowerCAmelCase_ ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
_UpperCAmelCase : Union[str, Any] = jax.tree_util.tree_map(
lambda lowerCAmelCase_ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , lowerCAmelCase_ )
_UpperCAmelCase : int = """"""
_UpperCAmelCase : Any = flatten_dict(lowerCAmelCase_ , sep=""".""" )
_UpperCAmelCase : Optional[int] = pt_model.state_dict()
# keep track of unexpected & missing keys
_UpperCAmelCase : str = []
_UpperCAmelCase : Optional[int] = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_UpperCAmelCase : int = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
_UpperCAmelCase : Dict = flax_key_tuple_array[:-1] + ["""weight"""]
_UpperCAmelCase : List[Any] = jnp.transpose(lowerCAmelCase_ , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
_UpperCAmelCase : int = flax_key_tuple_array[:-1] + ["""weight"""]
_UpperCAmelCase : Union[str, Any] = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
_UpperCAmelCase : Any = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase : Dict = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
_UpperCAmelCase : Any = """.""".join(lowerCAmelCase_ )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
else:
# add weight to pytorch dict
_UpperCAmelCase : int = np.asarray(lowerCAmelCase_ ) if not isinstance(lowerCAmelCase_ , np.ndarray ) else flax_tensor
_UpperCAmelCase : Union[str, Any] = torch.from_numpy(lowerCAmelCase_ )
# remove from missing keys
missing_keys.remove(lowerCAmelCase_ )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(lowerCAmelCase_ )
pt_model.load_state_dict(lowerCAmelCase_ )
# re-transform missing_keys to list
_UpperCAmelCase : Tuple = list(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
if len(lowerCAmelCase_ ) > 0:
logger.warning(
F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
""" use it for predictions and inference.""" )
return pt_model
| 349 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@slow
@require_torch
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,"""prajjwal1/bert-tiny""" )
_UpperCAmelCase : List[Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
_UpperCAmelCase : List[Any] = bertabert.config.encoder.vocab_size
_UpperCAmelCase : Optional[int] = tokenizer.sep_token_id
_UpperCAmelCase : Union[str, Any] = tokenizer.cls_token_id
_UpperCAmelCase : str = 128
_UpperCAmelCase : List[str] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""train[:1%]""" )
_UpperCAmelCase : Union[str, Any] = datasets.load_dataset("""cnn_dailymail""" ,"""3.0.0""" ,split="""validation[:1%]""" )
_UpperCAmelCase : Any = train_dataset.select(range(32 ) )
_UpperCAmelCase : Any = val_dataset.select(range(16 ) )
_UpperCAmelCase : List[Any] = 4
def _map_to_encoder_decoder_inputs(a_ ):
# Tokenizer will automatically set [BOS] <text> [EOS]
_UpperCAmelCase : int = tokenizer(batch["""article"""] ,padding="""max_length""" ,truncation=a_ ,max_length=512 )
_UpperCAmelCase : Tuple = tokenizer(batch["""highlights"""] ,padding="""max_length""" ,truncation=a_ ,max_length=128 )
_UpperCAmelCase : int = inputs.input_ids
_UpperCAmelCase : Union[str, Any] = inputs.attention_mask
_UpperCAmelCase : Union[str, Any] = outputs.input_ids
_UpperCAmelCase : Dict = outputs.input_ids.copy()
_UpperCAmelCase : Dict = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
_UpperCAmelCase : Optional[int] = outputs.attention_mask
assert all(len(a_ ) == 512 for x in inputs.input_ids )
assert all(len(a_ ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(a_ ):
_UpperCAmelCase : Optional[int] = pred.label_ids
_UpperCAmelCase : Optional[int] = pred.predictions
# all unnecessary tokens are removed
_UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : str = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : Tuple = sum([int(pred_str[i] == label_str[i] ) for i in range(len(a_ ) )] ) / len(a_ )
return {"accuracy": accuracy}
# map train dataset
_UpperCAmelCase : Union[str, Any] = train_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
train_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
# same for validation dataset
_UpperCAmelCase : List[str] = val_dataset.map(
_map_to_encoder_decoder_inputs ,batched=a_ ,batch_size=a_ ,remove_columns=["""article""", """highlights"""] ,)
val_dataset.set_format(
type="""torch""" ,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,)
_UpperCAmelCase : Optional[int] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : List[str] = SeqaSeqTrainingArguments(
output_dir=a_ ,per_device_train_batch_size=a_ ,per_device_eval_batch_size=a_ ,predict_with_generate=a_ ,evaluation_strategy="""steps""" ,do_train=a_ ,do_eval=a_ ,warmup_steps=0 ,eval_steps=2 ,logging_steps=2 ,)
# instantiate trainer
_UpperCAmelCase : int = SeqaSeqTrainer(
model=a_ ,args=a_ ,compute_metrics=_compute_metrics ,train_dataset=a_ ,eval_dataset=a_ ,tokenizer=a_ ,)
# start training
trainer.train()
| 349 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def snake_case_ ( lowerCAmelCase_ = "AAPL" )-> str:
'''simple docstring'''
_UpperCAmelCase : Any = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_UpperCAmelCase : Tuple = BeautifulSoup(requests.get(lowerCAmelCase_ ).text , """html.parser""" )
_UpperCAmelCase : List[Any] = """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}""")
| 349 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A_ : List[Any] = 637_8137.0
A_ : Dict = 635_6752.31_4245
A_ : int = 6_3_7_8_1_3_7
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> float:
'''simple docstring'''
_UpperCAmelCase : Tuple = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_UpperCAmelCase : Any = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
_UpperCAmelCase : Optional[Any] = atan((1 - flattening) * tan(radians(lowerCAmelCase_ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_UpperCAmelCase : Union[str, Any] = haversine_distance(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_UpperCAmelCase : Optional[int] = (b_lata + b_lata) / 2
_UpperCAmelCase : Any = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_UpperCAmelCase : List[str] = (sin(lowerCAmelCase_ ) ** 2) * (cos(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = cos(sigma / 2 ) ** 2
_UpperCAmelCase : Dict = (sigma - sin(lowerCAmelCase_ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_UpperCAmelCase : Union[str, Any] = (cos(lowerCAmelCase_ ) ** 2) * (sin(lowerCAmelCase_ ) ** 2)
_UpperCAmelCase : Union[str, Any] = sin(sigma / 2 ) ** 2
_UpperCAmelCase : Optional[Any] = (sigma + sin(lowerCAmelCase_ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_=None ,a_=True ,a_=None ,**a_ ) -> Optional[Any]:
_UpperCAmelCase : List[str] = parent
_UpperCAmelCase : Optional[Any] = config_class
_UpperCAmelCase : Optional[Any] = has_text_modality
_UpperCAmelCase : Any = kwargs
_UpperCAmelCase : Tuple = common_properties
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Any = self.config_class(**self.inputs_dict )
_UpperCAmelCase : Union[str, Any] = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(a_ ,a_ ) ,msg=f'''`{prop}` does not exist''' )
# Test that config has the common properties as setter
for idx, name in enumerate(a_ ):
try:
setattr(a_ ,a_ ,a_ )
self.parent.assertEqual(
getattr(a_ ,a_ ) ,a_ ,msg=f'''`{name} value {idx} expected, but was {getattr(a_ ,a_ )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(a_ ):
try:
_UpperCAmelCase : Dict = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(a_ ,a_ ) ,a_ ,msg=f'''`{name} value {idx} expected, but was {getattr(a_ ,a_ )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : str = self.config_class(**self.inputs_dict )
_UpperCAmelCase : Dict = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] ,a_ )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : int = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : List[str] = os.path.join(a_ ,"""config.json""" )
config_first.to_json_file(a_ )
_UpperCAmelCase : Any = self.config_class.from_json_file(a_ )
self.parent.assertEqual(config_second.to_dict() ,config_first.to_dict() )
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[str] = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(a_ )
_UpperCAmelCase : Dict = self.config_class.from_pretrained(a_ )
self.parent.assertEqual(config_second.to_dict() ,config_first.to_dict() )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.config_class(**self.inputs_dict )
_UpperCAmelCase : Optional[int] = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[int] = os.path.join(a_ ,a_ )
config_first.save_pretrained(a_ )
_UpperCAmelCase : Union[str, Any] = self.config_class.from_pretrained(a_ ,subfolder=a_ )
self.parent.assertEqual(config_second.to_dict() ,config_first.to_dict() )
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Union[str, Any] = self.config_class(**self.inputs_dict ,num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) ,5 )
self.parent.assertEqual(len(config.labelaid ) ,5 )
_UpperCAmelCase : List[Any] = 3
self.parent.assertEqual(len(config.idalabel ) ,3 )
self.parent.assertEqual(len(config.labelaid ) ,3 )
def _snake_case ( self ) -> Optional[int]:
if self.config_class.is_composition:
return
_UpperCAmelCase : List[Any] = self.config_class()
self.parent.assertIsNotNone(a_ )
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Tuple = copy.deepcopy(a_ )
_UpperCAmelCase : Optional[Any] = self.config_class(**a_ )
_UpperCAmelCase : Optional[int] = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(a_ ,a_ ) != value:
wrong_values.append((key, getattr(a_ ,a_ ), value) )
if len(a_ ) > 0:
_UpperCAmelCase : Tuple = """\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
raise ValueError(f'''The following keys were not properly set in the config:\n{errors}''' )
def _snake_case ( self ) -> List[Any]:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , )-> float:
'''simple docstring'''
_UpperCAmelCase : str = x_start
_UpperCAmelCase : Union[str, Any] = fnc(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = 0.0
for _ in range(lowerCAmelCase_ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
_UpperCAmelCase : Any = (x_end - x_start) / steps + xa
_UpperCAmelCase : List[Any] = fnc(lowerCAmelCase_ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
_UpperCAmelCase : Any = xa
_UpperCAmelCase : str = fxa
return area
if __name__ == "__main__":
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
A_ : List[str] = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = (boundary[1] - boundary[0]) / steps
_UpperCAmelCase : Tuple = boundary[0]
_UpperCAmelCase : List[str] = boundary[1]
_UpperCAmelCase : List[Any] = make_points(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = 0.0
y += (h / 2.0) * f(lowerCAmelCase_ )
for i in x_i:
# print(i)
y += h * f(lowerCAmelCase_ )
y += (h / 2.0) * f(lowerCAmelCase_ )
return y
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
'''simple docstring'''
_UpperCAmelCase : int = a + h
while x < (b - h):
yield x
_UpperCAmelCase : List[str] = x + h
def snake_case_ ( lowerCAmelCase_ )-> Tuple: # enter your function here
'''simple docstring'''
_UpperCAmelCase : int = (x - 0) * (x - 0)
return y
def snake_case_ ( )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : int = 0.0 # Lower bound of integration
_UpperCAmelCase : Optional[Any] = 1.0 # Upper bound of integration
_UpperCAmelCase : Union[str, Any] = 1_0.0 # define number of steps or resolution
_UpperCAmelCase : List[Any] = [a, b] # define boundary of integration
_UpperCAmelCase : Any = method_a(lowerCAmelCase_ , lowerCAmelCase_ )
print(F'''y = {y}''' )
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowerCAmelCase_ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowerCAmelCase_ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowerCAmelCase_ )
return parser.parse_args()
def snake_case_ ( )-> str:
'''simple docstring'''
_UpperCAmelCase : List[str] = parse_args()
# Import training_script as a module.
_UpperCAmelCase : List[Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_UpperCAmelCase : Optional[Any] = script_fpath.stem
_UpperCAmelCase : List[str] = importlib.import_module(lowerCAmelCase_ )
# Patch sys.argv
_UpperCAmelCase : Dict = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : Union[str, Any] = StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
_UpperCAmelCase : Optional[int] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler("""sample_euler""" )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Tuple = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase : int = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : str = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
_UpperCAmelCase : Tuple = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler("""sample_euler""" )
_UpperCAmelCase : Optional[Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type="""np""" )
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase : Optional[int] = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
_UpperCAmelCase : List[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler("""sample_dpmpp_2m""" )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Union[str, Any] = torch.manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=7.5 ,num_inference_steps=15 ,output_type="""np""" ,use_karras_sigmas=a_ ,)
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase : Dict = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""only integers accepted as input""" )
else:
_UpperCAmelCase : Dict = str(abs(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = [list(lowerCAmelCase_ ) for char in range(len(lowerCAmelCase_ ) )]
for index in range(len(lowerCAmelCase_ ) ):
num_transpositions[index].pop(lowerCAmelCase_ )
return max(
int("""""".join(list(lowerCAmelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
_UpperCAmelCase : Union[str, Any] = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
_UpperCAmelCase : Union[str, Any] = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
_UpperCAmelCase : Tuple = subset[i - 1][j]
if arr[i - 1] <= j:
_UpperCAmelCase : Optional[int] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Dict = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), F'''{len(lowerCAmelCase_ )} != {len(lowerCAmelCase_ )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A_ : Union[str, Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A_ : int = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
try:
_UpperCAmelCase : Any = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(lowerCAmelCase_ ) )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[int]:
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(lowerCAmelCase_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = "student" , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ , )-> Tuple[PreTrainedModel, List[int], List[int]]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
AutoTokenizer.from_pretrained(lowerCAmelCase_ ).save_pretrained(lowerCAmelCase_ ) # purely for convenience
_UpperCAmelCase : Any = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_ ).eval()
else:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), F'''teacher must be a model or string got type {type(lowerCAmelCase_ )}'''
_UpperCAmelCase : str = teacher.config.to_diff_dict()
try:
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
_UpperCAmelCase : Tuple = teacher_e
if d is None:
_UpperCAmelCase : Dict = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
_UpperCAmelCase ,_UpperCAmelCase : int = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
_UpperCAmelCase : List[str] = teacher_e
if d is None:
_UpperCAmelCase : str = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase_ )
# Copy weights
_UpperCAmelCase : Any = teacher.config_class(**lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
_UpperCAmelCase : Optional[Any] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = list(range(lowerCAmelCase_ ) ), list(range(lowerCAmelCase_ ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(lowerCAmelCase_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
if d_layers_to_copy is None:
_UpperCAmelCase : List[int] = pick_layers_to_copy(lowerCAmelCase_ , lowerCAmelCase_ )
try:
if hasattr(
lowerCAmelCase_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase_ )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase_ )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
_UpperCAmelCase : Dict = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 349 | 1 |
'''simple docstring'''
import requests
A_ : str = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
_UpperCAmelCase : Any = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["""articles"""] , 1 ):
print(F'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 349 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase_ , lowerCAmelCase_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=False )-> Dict:
'''simple docstring'''
try:
_UpperCAmelCase : Any = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase : str = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase : int = strtobool(lowerCAmelCase_ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
A_ : str = parse_flag_from_env("""RUN_SLOW""", default=False)
A_ : List[str] = parse_flag_from_env("""RUN_REMOTE""", default=False)
A_ : str = parse_flag_from_env("""RUN_LOCAL""", default=True)
A_ : Union[str, Any] = parse_flag_from_env("""RUN_PACKAGED""", default=True)
# Compression
A_ : int = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="""test requires lz4""")
A_ : Any = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="""test requires py7zr""")
A_ : Optional[Any] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="""test requires zstandard""")
# Audio
A_ : str = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("""soundfile""") is None or version.parse(importlib_metadata.version("""soundfile""")) < version.parse("""0.12.0"""),
reason="""test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; """,
)
# Beam
A_ : Optional[int] = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("""0.3.2"""),
reason="""test requires apache-beam and a compatible dill version""",
)
# Dill-cloudpickle compatibility
A_ : Optional[int] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("""0.3.2"""),
reason="""test requires dill>0.3.2 for cloudpickle compatibility""",
)
# Windows
A_ : Dict = pytest.mark.skipif(
sys.platform == """win32""",
reason="""test should not be run on Windows""",
)
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
try:
import faiss # noqa
except ImportError:
_UpperCAmelCase : Any = unittest.skip("""test requires faiss""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
try:
import regex # noqa
except ImportError:
_UpperCAmelCase : Optional[int] = unittest.skip("""test requires regex""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
try:
import elasticsearch # noqa
except ImportError:
_UpperCAmelCase : str = unittest.skip("""test requires elasticsearch""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
try:
import sqlalchemy # noqa
except ImportError:
_UpperCAmelCase : Optional[Any] = unittest.skip("""test requires sqlalchemy""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
if not config.TORCH_AVAILABLE:
_UpperCAmelCase : str = unittest.skip("""test requires PyTorch""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
if not config.TF_AVAILABLE:
_UpperCAmelCase : List[str] = unittest.skip("""test requires TensorFlow""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
if not config.JAX_AVAILABLE:
_UpperCAmelCase : Any = unittest.skip("""test requires JAX""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
if not config.PIL_AVAILABLE:
_UpperCAmelCase : str = unittest.skip("""test requires Pillow""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("""test requires transformers""" )(lowerCAmelCase_ )
else:
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("""test requires tiktoken""" )(lowerCAmelCase_ )
else:
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("""test requires spacy""" )(lowerCAmelCase_ )
else:
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
def _require_spacy_model(lowerCAmelCase_ ):
try:
import spacy # noqa F401
spacy.load(lowerCAmelCase_ )
except ImportError:
return unittest.skip("""test requires spacy""" )(lowerCAmelCase_ )
except OSError:
return unittest.skip("""test requires spacy model '{}'""".format(lowerCAmelCase_ ) )(lowerCAmelCase_ )
else:
return test_case
return _require_spacy_model
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("""test requires pyspark""" )(lowerCAmelCase_ )
else:
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("""test requires joblibspark""" )(lowerCAmelCase_ )
else:
return test_case
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
if not _run_slow_tests or _run_slow_tests == 0:
_UpperCAmelCase : List[Any] = unittest.skip("""test is slow""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
if not _run_local_tests or _run_local_tests == 0:
_UpperCAmelCase : Union[str, Any] = unittest.skip("""test is local""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
if not _run_packaged_tests or _run_packaged_tests == 0:
_UpperCAmelCase : List[Any] = unittest.skip("""test is packaged""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not _run_remote_tests or _run_remote_tests == 0:
_UpperCAmelCase : Tuple = unittest.skip("""test requires remote""" )(lowerCAmelCase_ )
return test_case
def snake_case_ ( *lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(lowerCAmelCase_ ) and name.startswith("""test""" ):
for decorator in decorators:
_UpperCAmelCase : List[Any] = decorator(lowerCAmelCase_ )
setattr(cls , lowerCAmelCase_ , lowerCAmelCase_ )
return cls
return decorate
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
pass
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = 1
UpperCAmelCase = 2
@contextmanager
def snake_case_ ( lowerCAmelCase_=OfflineSimulationMode.CONNECTION_FAILS , lowerCAmelCase_=1e-1_6 )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Any = requests.Session().request
def timeout_request(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ):
# Change the url to an invalid url so that the connection hangs
_UpperCAmelCase : List[Any] = """https://10.255.255.1"""
if kwargs.get("""timeout""" ) is None:
raise RequestWouldHangIndefinitelyError(
F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
_UpperCAmelCase : List[str] = timeout
try:
return online_request(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
_UpperCAmelCase : Tuple = url
_UpperCAmelCase : Optional[int] = e.args[0]
_UpperCAmelCase : Dict = (max_retry_error.args[0].replace("""10.255.255.1""" , F'''OfflineMock[{url}]''' ),)
_UpperCAmelCase : Dict = (max_retry_error,)
raise
def raise_connection_error(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ ):
raise requests.ConnectionError("""Offline mode is enabled.""" , request=lowerCAmelCase_ )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("""requests.Session.send""" , lowerCAmelCase_ ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("""requests.Session.request""" , lowerCAmelCase_ ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("""datasets.config.HF_DATASETS_OFFLINE""" , lowerCAmelCase_ ):
yield
else:
raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" )
@contextmanager
def snake_case_ ( *lowerCAmelCase_ , **lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : int = str(Path().resolve() )
with tempfile.TemporaryDirectory(*lowerCAmelCase_ , **lowerCAmelCase_ ) as tmp_dir:
try:
os.chdir(lowerCAmelCase_ )
yield
finally:
os.chdir(lowerCAmelCase_ )
@contextmanager
def snake_case_ ( )-> Any:
'''simple docstring'''
import gc
gc.collect()
_UpperCAmelCase : List[Any] = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def snake_case_ ( )-> Any:
'''simple docstring'''
import gc
gc.collect()
_UpperCAmelCase : Optional[Any] = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
return deepcopy(lowerCAmelCase_ ).integers(0 , 100 , 10 ).tolist() == deepcopy(lowerCAmelCase_ ).integers(0 , 100 , 10 ).tolist()
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
import decorator
from requests.exceptions import HTTPError
def _wrapper(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ ):
try:
return func(*lowerCAmelCase_ , **lowerCAmelCase_ )
except HTTPError as err:
if str(lowerCAmelCase_ ).startswith("""500""" ) or str(lowerCAmelCase_ ).startswith("""502""" ):
pytest.xfail(str(lowerCAmelCase_ ) )
raise err
return decorator.decorator(_wrapper , lowerCAmelCase_ )
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_ ,a_ ) -> Dict:
_UpperCAmelCase : Union[str, Any] = returncode
_UpperCAmelCase : Optional[Any] = stdout
_UpperCAmelCase : Any = stderr
async def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase : Optional[int] = await stream.readline()
if line:
callback(lowerCAmelCase_ )
else:
break
async def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=False , lowerCAmelCase_=False )-> _RunOutput:
'''simple docstring'''
if echo:
print("""\nRunning: """ , """ """.join(lowerCAmelCase_ ) )
_UpperCAmelCase : str = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=lowerCAmelCase_ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=lowerCAmelCase_ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Optional[int] = []
def tee(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="" ):
_UpperCAmelCase : Union[str, Any] = line.decode("""utf-8""" ).rstrip()
sink.append(lowerCAmelCase_ )
if not quiet:
print(lowerCAmelCase_ , lowerCAmelCase_ , file=lowerCAmelCase_ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda lowerCAmelCase_ : tee(lowerCAmelCase_ , lowerCAmelCase_ , sys.stdout , label="""stdout:""" ) ),
_read_stream(p.stderr , lambda lowerCAmelCase_ : tee(lowerCAmelCase_ , lowerCAmelCase_ , sys.stderr , label="""stderr:""" ) ),
] , timeout=lowerCAmelCase_ , )
return _RunOutput(await p.wait() , lowerCAmelCase_ , lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=180 , lowerCAmelCase_=False , lowerCAmelCase_=True )-> _RunOutput:
'''simple docstring'''
_UpperCAmelCase : Dict = asyncio.get_event_loop()
_UpperCAmelCase : Any = loop.run_until_complete(
_stream_subprocess(lowerCAmelCase_ , env=lowerCAmelCase_ , stdin=lowerCAmelCase_ , timeout=lowerCAmelCase_ , quiet=lowerCAmelCase_ , echo=lowerCAmelCase_ ) )
_UpperCAmelCase : Tuple = """ """.join(lowerCAmelCase_ )
if result.returncode > 0:
_UpperCAmelCase : Union[str, Any] = """\n""".join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F'''\'{cmd_str}\' produced no output.''' )
return result
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : Tuple = os.environ.get("""PYTEST_XDIST_WORKER""" , """gw0""" )
_UpperCAmelCase : Dict = re.sub(R"""^gw""" , """""" , lowerCAmelCase_ , 0 , re.M )
return int(lowerCAmelCase_ )
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Any = 29500
_UpperCAmelCase : str = pytest_xdist_worker_id()
return port + uniq_delta
| 349 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_UpperCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter Video/IGTV url: """).strip()
A_ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 349 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : str = 0
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Optional[int] = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(a_ ,a_ )
def _snake_case ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[int] = Path(a_ ) / """preprocessor_config.json"""
_UpperCAmelCase : int = Path(a_ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} ,open(a_ ,"""w""" ) ,)
json.dump({"""model_type""": """clip"""} ,open(a_ ,"""w""" ) )
_UpperCAmelCase : Dict = AutoImageProcessor.from_pretrained(a_ )
self.assertIsInstance(a_ ,a_ )
def _snake_case ( self ) -> Union[str, Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : List[str] = Path(a_ ) / """preprocessor_config.json"""
_UpperCAmelCase : List[Any] = Path(a_ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} ,open(a_ ,"""w""" ) ,)
json.dump({"""model_type""": """clip"""} ,open(a_ ,"""w""" ) )
_UpperCAmelCase : int = AutoImageProcessor.from_pretrained(a_ )
self.assertIsInstance(a_ ,a_ )
def _snake_case ( self ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Any = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_UpperCAmelCase : str = Path(a_ ) / """preprocessor_config.json"""
_UpperCAmelCase : Optional[int] = Path(a_ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} ,open(a_ ,"""w""" ) ,)
json.dump({"""model_type""": """clip"""} ,open(a_ ,"""w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_UpperCAmelCase : List[str] = AutoImageProcessor.from_pretrained(a_ ).to_dict()
config_dict.pop("""image_processor_type""" )
_UpperCAmelCase : List[str] = CLIPImageProcessor(**a_ )
# save in new folder
model_config.save_pretrained(a_ )
config.save_pretrained(a_ )
_UpperCAmelCase : Dict = AutoImageProcessor.from_pretrained(a_ )
# make sure private variable is not incorrectly saved
_UpperCAmelCase : Dict = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(a_ ,a_ )
def _snake_case ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : str = Path(a_ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} ,open(a_ ,"""w""" ) ,)
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(a_ )
self.assertIsInstance(a_ ,a_ )
def _snake_case ( self ) -> List[Any]:
with self.assertRaisesRegex(
a_ ,"""clip-base is not a local folder and is not a valid model identifier""" ):
_UpperCAmelCase : Optional[int] = AutoImageProcessor.from_pretrained("""clip-base""" )
def _snake_case ( self ) -> List[str]:
with self.assertRaisesRegex(
a_ ,r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
_UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained(a_ ,revision="""aaaaaa""" )
def _snake_case ( self ) -> Optional[Any]:
with self.assertRaisesRegex(
a_ ,"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" ,):
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _snake_case ( self ) -> Optional[Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(a_ ):
_UpperCAmelCase : Dict = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a_ ):
_UpperCAmelCase : List[str] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" ,trust_remote_code=a_ )
_UpperCAmelCase : Optional[int] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" ,trust_remote_code=a_ )
self.assertEqual(image_processor.__class__.__name__ ,"""NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a_ )
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(a_ ,trust_remote_code=a_ )
self.assertEqual(reloaded_image_processor.__class__.__name__ ,"""NewImageProcessor""" )
def _snake_case ( self ) -> Any:
try:
AutoConfig.register("""custom""" ,a_ )
AutoImageProcessor.register(a_ ,a_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a_ ):
AutoImageProcessor.register(a_ ,a_ )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[int] = Path(a_ ) / """preprocessor_config.json"""
_UpperCAmelCase : Dict = Path(a_ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} ,open(a_ ,"""w""" ) ,)
json.dump({"""model_type""": """clip"""} ,open(a_ ,"""w""" ) )
_UpperCAmelCase : Union[str, Any] = CustomImageProcessor.from_pretrained(a_ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a_ )
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(a_ )
self.assertIsInstance(a_ ,a_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _snake_case ( self ) -> str:
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = True
try:
AutoConfig.register("""custom""" ,a_ )
AutoImageProcessor.register(a_ ,a_ )
# If remote code is not set, the default is to use local
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ ,"""NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
_UpperCAmelCase : Optional[int] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" ,trust_remote_code=a_ )
self.assertEqual(image_processor.__class__.__name__ ,"""NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" ,trust_remote_code=a_ )
self.assertEqual(image_processor.__class__.__name__ ,"""NewImageProcessor""" )
self.assertTrue(not hasattr(a_ ,"""is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 349 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : Union[str, Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(a_ )
return image
@property
def _snake_case ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def _snake_case ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def _snake_case ( self ) -> Dict:
torch.manual_seed(0 )
_UpperCAmelCase : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1_000 ,)
return CLIPTextModel(a_ )
@property
def _snake_case ( self ) -> Union[str, Any]:
def extract(*a_ ,**a_ ):
class lowercase :
"""simple docstring"""
def __init__( self ) -> Any:
_UpperCAmelCase : str = torch.ones([0] )
def _snake_case ( self ,a_ ) -> Any:
self.pixel_values.to(a_ )
return self
return Out()
return extract
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : List[str] = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Union[str, Any] = self.dummy_cond_unet
_UpperCAmelCase : int = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=a_ ,set_alpha_to_one=a_ ,)
_UpperCAmelCase : Optional[int] = self.dummy_vae
_UpperCAmelCase : Optional[int] = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : int = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : int = output.images
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : str = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase : Tuple = self.dummy_cond_unet
_UpperCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : int = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : str = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : int = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : Any = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : List[Any] = sd_pipe([prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : List[Any] = torch.Generator(device=a_ ).manual_seed(0 )
_UpperCAmelCase : Any = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=a_ ,)[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Union[str, Any] = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=a_ )
assert isinstance(a_ ,a_ )
assert isinstance(pipe.scheduler ,a_ )
assert pipe.safety_checker is None
_UpperCAmelCase : Dict = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(a_ )
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained(a_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase : Union[str, Any] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.dummy_cond_unet
_UpperCAmelCase : str = PNDMScheduler(skip_prk_steps=a_ )
_UpperCAmelCase : List[str] = self.dummy_vae
_UpperCAmelCase : int = self.dummy_text_encoder
_UpperCAmelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
_UpperCAmelCase : str = unet.half()
_UpperCAmelCase : List[str] = vae.half()
_UpperCAmelCase : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_UpperCAmelCase : Dict = StableDiffusionPipeline(
unet=a_ ,scheduler=a_ ,vae=a_ ,text_encoder=a_ ,tokenizer=a_ ,safety_checker=a_ ,feature_extractor=self.dummy_extractor ,)
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = """A painting of a squirrel eating a burger"""
_UpperCAmelCase : int = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : List[Any] = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
_UpperCAmelCase : Any = 4_003_660_346
_UpperCAmelCase : List[Any] = 7
# without safety guidance (sld_guidance_scale = 0)
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : str = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : str = output.images
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_UpperCAmelCase : List[str] = torch.manual_seed(a_ )
_UpperCAmelCase : Optional[Any] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : List[str] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> int:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=a_ )
_UpperCAmelCase : Union[str, Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_UpperCAmelCase : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Any = """padme amidala taking a bath artwork, safe for work, no nudity"""
_UpperCAmelCase : Optional[Any] = 2_734_971_755
_UpperCAmelCase : Optional[int] = 7
_UpperCAmelCase : int = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : Optional[int] = output.images
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[int] = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_UpperCAmelCase : Optional[int] = torch.manual_seed(a_ )
_UpperCAmelCase : int = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Union[str, Any] = output.images
_UpperCAmelCase : Any = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _snake_case ( self ) -> Any:
_UpperCAmelCase : Any = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
_UpperCAmelCase : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Optional[int] = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
_UpperCAmelCase : Dict = 1_044_355_234
_UpperCAmelCase : int = 12
_UpperCAmelCase : Optional[Any] = torch.manual_seed(a_ )
_UpperCAmelCase : List[str] = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=0 ,)
_UpperCAmelCase : List[str] = output.images
_UpperCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_UpperCAmelCase : Tuple = torch.manual_seed(a_ )
_UpperCAmelCase : Dict = sd_pipe(
[prompt] ,generator=a_ ,guidance_scale=a_ ,num_inference_steps=50 ,output_type="""np""" ,width=512 ,height=512 ,sld_guidance_scale=2_000 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
_UpperCAmelCase : Optional[Any] = output.images
_UpperCAmelCase : Dict = image[0, -3:, -3:, -1]
_UpperCAmelCase : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 349 | 1 |
'''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_ : List[str] = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
A_ : Dict = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
A_ : str = """|""".join(sys.argv[1:])
A_ : Union[str, Any] = re.compile(rf"""^({joined_dirs}).*?\.py$""")
A_ : int = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : List[Any] = {"""vocab_file""": """sentencepiece.model"""}
A_ : Any = {
"""vocab_file""": {
"""google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
},
}
A_ : Any = {
"""google/rembert""": 2_5_6,
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = VOCAB_FILES_NAMES
UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self ,a_ ,a_=False ,a_=True ,a_=True ,a_="[CLS]" ,a_="[SEP]" ,a_="[UNK]" ,a_="[SEP]" ,a_="[PAD]" ,a_="[CLS]" ,a_="[MASK]" ,**a_ ,) -> Dict:
super().__init__(
do_lower_case=a_ ,remove_space=a_ ,keep_accents=a_ ,bos_token=a_ ,eos_token=a_ ,unk_token=a_ ,sep_token=a_ ,pad_token=a_ ,cls_token=a_ ,mask_token=a_ ,**a_ ,)
_UpperCAmelCase : Tuple = do_lower_case
_UpperCAmelCase : str = remove_space
_UpperCAmelCase : int = keep_accents
_UpperCAmelCase : int = vocab_file
_UpperCAmelCase : str = spm.SentencePieceProcessor()
self.sp_model.Load(a_ )
@property
def _snake_case ( self ) -> Tuple:
return len(self.sp_model )
def _snake_case ( self ) -> str:
_UpperCAmelCase : Optional[int] = {self.convert_ids_to_tokens(a_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Any:
_UpperCAmelCase : Tuple = self.__dict__.copy()
_UpperCAmelCase : Dict = None
return state
def __setstate__( self ,a_ ) -> Optional[int]:
_UpperCAmelCase : Dict = d
_UpperCAmelCase : int = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def _snake_case ( self ,a_ ,a_=False ) -> str:
_UpperCAmelCase : Union[str, Any] = self.sp_model.EncodeAsPieces(a_ )
return pieces
def _snake_case ( self ,a_ ) -> Optional[int]:
return self.sp_model.PieceToId(a_ )
def _snake_case ( self ,a_ ) -> str:
return self.sp_model.IdToPiece(a_ )
def _snake_case ( self ,a_ ) -> List[str]:
_UpperCAmelCase : str = self.sp_model.decode_pieces(a_ )
return out_string
def _snake_case ( self ,a_ ,a_ = None ) -> List[int]:
_UpperCAmelCase : Union[str, Any] = [self.sep_token_id]
_UpperCAmelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _snake_case ( self ,a_ ,a_ = None ,a_ = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1]
return [1] + ([0] * len(a_ )) + [1]
def _snake_case ( self ,a_ ,a_ = None ) -> List[int]:
_UpperCAmelCase : Tuple = [self.sep_token_id]
_UpperCAmelCase : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _snake_case ( self ,a_ ,a_ = None ) -> Tuple[str]:
if not os.path.isdir(a_ ):
logger.error("""Vocabulary path ({}) should be a directory""".format(a_ ) )
return
_UpperCAmelCase : List[Any] = os.path.join(
a_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ):
copyfile(self.vocab_file ,a_ )
return (out_vocab_file,)
| 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """yolos"""
def __init__( self ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.0 ,a_=0.0 ,a_=0.02 ,a_=1E-1_2 ,a_=[512, 864] ,a_=16 ,a_=3 ,a_=True ,a_=100 ,a_=True ,a_=False ,a_=1 ,a_=5 ,a_=2 ,a_=5 ,a_=2 ,a_=0.1 ,**a_ ,) -> List[str]:
super().__init__(**a_ )
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[Any] = num_hidden_layers
_UpperCAmelCase : Tuple = num_attention_heads
_UpperCAmelCase : Optional[Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[str] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Union[str, Any] = layer_norm_eps
_UpperCAmelCase : int = image_size
_UpperCAmelCase : Dict = patch_size
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : Optional[Any] = qkv_bias
_UpperCAmelCase : List[Any] = num_detection_tokens
_UpperCAmelCase : Tuple = use_mid_position_embeddings
_UpperCAmelCase : int = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : Dict = class_cost
_UpperCAmelCase : Dict = bbox_cost
_UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : Union[str, Any] = eos_coefficient
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = version.parse("""1.11""" )
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _snake_case ( self ) -> float:
return 1E-4
@property
def _snake_case ( self ) -> int:
return 12
| 349 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Any = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Dict = [2, 4, 6, 8, 10, 12]
_UpperCAmelCase : Optional[int] = 100
self.assertEqual(kp.calc_profit(a_ ,a_ ,a_ ) ,210 )
def _snake_case ( self ) -> Union[str, Any]:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Any:
self.assertRaisesRegex(a_ ,"""Weight can not be negative.""" )
def _snake_case ( self ) -> Optional[Any]:
self.assertRaisesRegex(a_ ,"""Profit can not be negative.""" )
def _snake_case ( self ) -> Dict:
self.assertRaisesRegex(a_ ,"""max_weight must greater than zero.""" )
def _snake_case ( self ) -> Tuple:
self.assertRaisesRegex(
a_ ,"""The length of profit and weight must be same.""" )
if __name__ == "__main__":
unittest.main()
| 349 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
A_ : List[Any] = logging.get_logger(__name__)
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
if "resnet-50" in model_name:
_UpperCAmelCase : str = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
_UpperCAmelCase : List[Any] = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
_UpperCAmelCase : Optional[Any] = DetrConfig(use_timm_backbone=lowerCAmelCase_ , backbone_config=lowerCAmelCase_ )
# set label attributes
_UpperCAmelCase : List[Any] = """panoptic""" in model_name
if is_panoptic:
_UpperCAmelCase : Dict = 250
else:
_UpperCAmelCase : Dict = 91
_UpperCAmelCase : List[Any] = """huggingface/label-files"""
_UpperCAmelCase : str = """coco-detection-id2label.json"""
_UpperCAmelCase : Any = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase : str = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
_UpperCAmelCase : Dict = idalabel
_UpperCAmelCase : List[str] = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Tuple = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = state_dict.pop(lowerCAmelCase_ )
_UpperCAmelCase : int = val
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=False )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = """"""
if is_panoptic:
_UpperCAmelCase : Optional[int] = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_UpperCAmelCase : Optional[int] = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
_UpperCAmelCase : Any = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Optional[Any] = in_proj_weight[:256, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[:256]
_UpperCAmelCase : List[str] = in_proj_weight[256:512, :]
_UpperCAmelCase : Dict = in_proj_bias[256:512]
_UpperCAmelCase : List[Any] = in_proj_weight[-256:, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_UpperCAmelCase : int = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
_UpperCAmelCase : List[str] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Dict = in_proj_weight[:256, :]
_UpperCAmelCase : Optional[int] = in_proj_bias[:256]
_UpperCAmelCase : Dict = in_proj_weight[256:512, :]
_UpperCAmelCase : List[Any] = in_proj_bias[256:512]
_UpperCAmelCase : List[str] = in_proj_weight[-256:, :]
_UpperCAmelCase : int = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_UpperCAmelCase : List[str] = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
_UpperCAmelCase : Dict = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_UpperCAmelCase : Dict = in_proj_weight_cross_attn[:256, :]
_UpperCAmelCase : int = in_proj_bias_cross_attn[:256]
_UpperCAmelCase : List[Any] = in_proj_weight_cross_attn[256:512, :]
_UpperCAmelCase : Dict = in_proj_bias_cross_attn[256:512]
_UpperCAmelCase : List[Any] = in_proj_weight_cross_attn[-256:, :]
_UpperCAmelCase : int = in_proj_bias_cross_attn[-256:]
def snake_case_ ( )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase : Optional[int] = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=False )-> Any:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = get_detr_config(lowerCAmelCase_ )
# load original model from torch hub
_UpperCAmelCase : Optional[int] = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
_UpperCAmelCase : str = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=lowerCAmelCase_ ).eval()
_UpperCAmelCase : Optional[int] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(lowerCAmelCase_ ):
if is_panoptic:
_UpperCAmelCase : Dict = """detr.""" + src
rename_key(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCAmelCase_ , is_panoptic=lowerCAmelCase_ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_UpperCAmelCase : Optional[int] = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
_UpperCAmelCase : List[Any] = state_dict.pop(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_UpperCAmelCase : Optional[Any] = state_dict.pop(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
_UpperCAmelCase : Union[str, Any] = state_dict.pop(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
_UpperCAmelCase : Union[str, Any] = state_dict.pop(lowerCAmelCase_ )
_UpperCAmelCase : str = val
# finally, create HuggingFace model and load state dict
_UpperCAmelCase : Optional[int] = DetrForSegmentation(lowerCAmelCase_ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
# verify our conversion on an image
_UpperCAmelCase : Optional[Any] = """coco_panoptic""" if is_panoptic else """coco_detection"""
_UpperCAmelCase : Dict = DetrImageProcessor(format=lowerCAmelCase_ )
_UpperCAmelCase : Optional[Any] = processor(images=prepare_img() , return_tensors="""pt""" )
_UpperCAmelCase : List[Any] = encoding["""pixel_values"""]
_UpperCAmelCase : Optional[int] = detr(lowerCAmelCase_ )
_UpperCAmelCase : int = model(lowerCAmelCase_ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
A_ : Any = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
A_ : int = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 349 |
'''simple docstring'''
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ )-> list[int]:
'''simple docstring'''
if num <= 0:
_UpperCAmelCase : List[Any] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = [True] * (num + 1)
_UpperCAmelCase : int = []
_UpperCAmelCase : int = 2
_UpperCAmelCase : int = int(math.sqrt(lowerCAmelCase_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCAmelCase_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCAmelCase_ ):
if sieve[i] is True:
_UpperCAmelCase : Tuple = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(lowerCAmelCase_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 349 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase ( _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = KandinskyImgaImgPipeline
UpperCAmelCase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
UpperCAmelCase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
UpperCAmelCase = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCAmelCase = False
@property
def _snake_case ( self ) -> List[str]:
return 32
@property
def _snake_case ( self ) -> Optional[Any]:
return 32
@property
def _snake_case ( self ) -> Any:
return self.time_input_dim
@property
def _snake_case ( self ) -> List[Any]:
return self.time_input_dim * 4
@property
def _snake_case ( self ) -> Optional[int]:
return 100
@property
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : List[Any] = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def _snake_case ( self ) -> int:
torch.manual_seed(0 )
_UpperCAmelCase : Any = MCLIPConfig(
numDims=self.cross_attention_dim ,transformerDimensions=self.text_embedder_hidden_size ,hidden_size=self.text_embedder_hidden_size ,intermediate_size=37 ,num_attention_heads=4 ,num_hidden_layers=5 ,vocab_size=1_005 ,)
_UpperCAmelCase : List[Any] = MultilingualCLIP(a_ )
_UpperCAmelCase : List[str] = text_encoder.eval()
return text_encoder
@property
def _snake_case ( self ) -> int:
torch.manual_seed(0 )
_UpperCAmelCase : str = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
_UpperCAmelCase : Optional[Any] = UNetaDConditionModel(**a_ )
return model
@property
def _snake_case ( self ) -> Optional[int]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def _snake_case ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
_UpperCAmelCase : int = VQModel(**self.dummy_movq_kwargs )
return model
def _snake_case ( self ) -> Union[str, Any]:
_UpperCAmelCase : Union[str, Any] = self.dummy_text_encoder
_UpperCAmelCase : Dict = self.dummy_tokenizer
_UpperCAmelCase : List[str] = self.dummy_unet
_UpperCAmelCase : Union[str, Any] = self.dummy_movq
_UpperCAmelCase : Union[str, Any] = {
"""num_train_timesteps""": 1_000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_0085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
_UpperCAmelCase : Optional[Any] = DDIMScheduler(**a_ )
_UpperCAmelCase : Union[str, Any] = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _snake_case ( self ,a_ ,a_=0 ) -> int:
_UpperCAmelCase : Dict = floats_tensor((1, self.cross_attention_dim) ,rng=random.Random(a_ ) ).to(a_ )
_UpperCAmelCase : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) ,rng=random.Random(seed + 1 ) ).to(a_ )
# create init_image
_UpperCAmelCase : Dict = floats_tensor((1, 3, 64, 64) ,rng=random.Random(a_ ) ).to(a_ )
_UpperCAmelCase : Union[str, Any] = image.cpu().permute(0 ,2 ,3 ,1 )[0]
_UpperCAmelCase : List[str] = Image.fromarray(np.uinta(a_ ) ).convert("""RGB""" ).resize((256, 256) )
if str(a_ ).startswith("""mps""" ):
_UpperCAmelCase : int = torch.manual_seed(a_ )
else:
_UpperCAmelCase : Optional[int] = torch.Generator(device=a_ ).manual_seed(a_ )
_UpperCAmelCase : int = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Tuple = """cpu"""
_UpperCAmelCase : str = self.get_dummy_components()
_UpperCAmelCase : Tuple = self.pipeline_class(**a_ )
_UpperCAmelCase : Tuple = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : str = pipe(**self.get_dummy_inputs(a_ ) )
_UpperCAmelCase : Dict = output.images
_UpperCAmelCase : Optional[int] = pipe(
**self.get_dummy_inputs(a_ ) ,return_dict=a_ ,)[0]
_UpperCAmelCase : List[Any] = image[0, -3:, -3:, -1]
_UpperCAmelCase : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase : Dict = np.array(
[0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> str:
_UpperCAmelCase : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
_UpperCAmelCase : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
_UpperCAmelCase : List[Any] = """A red cartoon frog, 4k"""
_UpperCAmelCase : Optional[int] = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(a_ )
_UpperCAmelCase : int = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" ,torch_dtype=torch.floataa )
_UpperCAmelCase : str = pipeline.to(a_ )
pipeline.set_progress_bar_config(disable=a_ )
_UpperCAmelCase : Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = pipe_prior(
a_ ,generator=a_ ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
_UpperCAmelCase : int = pipeline(
a_ ,image=a_ ,image_embeds=a_ ,negative_image_embeds=a_ ,generator=a_ ,num_inference_steps=100 ,height=768 ,width=768 ,strength=0.2 ,output_type="""np""" ,)
_UpperCAmelCase : List[str] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(a_ ,a_ )
| 349 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ ,a_ = None ,a_ = None ,a_ = True ,a_ = None ,a_ = False ,a_ = None ,a_ = True ,a_ = "arrow" ,**a_ ,) -> str:
super().__init__(
split=a_ ,features=a_ ,cache_dir=a_ ,keep_in_memory=a_ ,streaming=a_ ,**a_ ,)
_UpperCAmelCase : Any = load_from_cache_file
_UpperCAmelCase : Optional[int] = file_format
_UpperCAmelCase : int = Spark(
df=a_ ,features=a_ ,cache_dir=a_ ,working_dir=a_ ,**a_ ,)
def _snake_case ( self ) -> int:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=a_ ,file_format=self._file_format ,)
return self.builder.as_dataset(split=self.split )
| 349 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> list:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = len(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = [[0] * n for i in range(lowerCAmelCase_ )]
for i in range(lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = y_points[i]
for i in range(2 , lowerCAmelCase_ ):
for j in range(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : str = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 |
'''simple docstring'''
A_ : Optional[Any] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 349 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self ,a_ = 101 ) -> List[str]:
_UpperCAmelCase : Dict = length
def __len__( self ) -> Any:
return self.length
def __getitem__( self ,a_ ) -> int:
return i
class lowercase :
"""simple docstring"""
def __call__( self ,a_ ) -> Any:
return {"input_ids": torch.tensor(a_ ), "labels": torch.tensor(a_ )}
class lowercase ( nn.Module ):
"""simple docstring"""
def __init__( self ) -> Dict:
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_UpperCAmelCase : Any = nn.Linear(120 ,80 )
def _snake_case ( self ,a_ ,a_=None ) -> Any:
if labels is not None:
return torch.tensor(0.0 ,device=input_ids.device ), input_ids
else:
return input_ids
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@require_torch_neuroncore
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase : int = f'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
_UpperCAmelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : Tuple = f'''--output_dir {output_dir}'''.split()
_UpperCAmelCase : Any = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(a_ ,env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@require_torch_multi_gpu
def _snake_case ( self ) -> str:
_UpperCAmelCase : Union[str, Any] = f'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
_UpperCAmelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : str = f'''--output_dir {output_dir}'''.split()
_UpperCAmelCase : List[str] = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(a_ ,env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
A_ : str = HfArgumentParser((TrainingArguments,))
A_ : Any = parser.parse_args_into_dataclasses()[0]
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [1_0_1, 4_0, 7]:
A_ : List[Any] = DummyDataset(dataset_length)
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : List[Any] = list(range(len(lowerCAmelCase_ ) ) )
_UpperCAmelCase : Optional[int] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"""Predictions and/or labels do not match expected results:\n - predictions: """
F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
A_ : Dict = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
A_ : Optional[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
A_ : Dict = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
A_ : Any = 2
A_ : int = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
A_ : Union[str, Any] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
A_ : Any = None
| 349 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def snake_case_ ( )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
_UpperCAmelCase : str = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowerCAmelCase_ )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(lowerCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : Optional[int] = args.func(lowerCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 349 | 1 |
'''simple docstring'''
import re
def snake_case_ ( lowerCAmelCase_ )-> list:
'''simple docstring'''
return [char.split() for char in re.split(R"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = split_input(str_ )
return "".join(
["""""".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
try:
_UpperCAmelCase : Tuple = split_input(lowerCAmelCase_ )
if upper:
_UpperCAmelCase : str = """""".join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
_UpperCAmelCase : Tuple = """""".join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
return to_simple_case(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
try:
_UpperCAmelCase : Any = to_simple_case(lowerCAmelCase_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
return to_complex_case(lowerCAmelCase_ , lowerCAmelCase_ , """_""" )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
return to_complex_case(lowerCAmelCase_ , lowerCAmelCase_ , """-""" )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 349 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
_UpperCAmelCase : int = 0
while arr[min(lowerCAmelCase_ , lowerCAmelCase_ ) - 1] < x:
_UpperCAmelCase : Optional[int] = step
step += int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase : List[Any] = prev + 1
if prev == min(lowerCAmelCase_ , lowerCAmelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A_ : str = input("""Enter numbers separated by a comma:\n""").strip()
A_ : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
A_ : int = int(input("""Enter the number to be searched:\n"""))
A_ : Any = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(f"""Number {x} is at index {res}""")
| 349 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=5 )-> Optional[int]:
'''simple docstring'''
assert masked_input.count("""<mask>""" ) == 1
_UpperCAmelCase : Dict = torch.tensor(tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase : Any = model(lowerCAmelCase_ )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase : Optional[int] = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase : Tuple = logits[0, masked_index, :]
_UpperCAmelCase : List[str] = logits.softmax(dim=0 )
_UpperCAmelCase ,_UpperCAmelCase : int = prob.topk(k=lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : List[str] = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(lowerCAmelCase_ ) )] )
_UpperCAmelCase : Tuple = tokenizer.mask_token
_UpperCAmelCase : List[Any] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
_UpperCAmelCase : str = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(lowerCAmelCase_ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(lowerCAmelCase_ ) , lowerCAmelCase_ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(lowerCAmelCase_ , lowerCAmelCase_ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
A_ : Union[str, Any] = CamembertTokenizer.from_pretrained("""camembert-base""")
A_ : List[Any] = CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()
A_ : Any = """Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_UpperCAmelCase : Optional[int] = []
_list.append([line.split()[1], line.split()[2]] )
_UpperCAmelCase : List[str] = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_UpperCAmelCase : List[str] = []
_list.append([line.split()[0], line.split()[2]] )
_UpperCAmelCase : Optional[int] = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
with open(lowerCAmelCase_ ) as f:
_UpperCAmelCase : List[Any] = f.read(1 )
_UpperCAmelCase : int = start_node
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Dict = start_node
_UpperCAmelCase : Any = 0
while visiting not in first_solution:
_UpperCAmelCase : Optional[int] = 10000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(lowerCAmelCase_ ) and k[0] not in first_solution:
_UpperCAmelCase : Optional[int] = k[1]
_UpperCAmelCase : List[str] = k[0]
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = distance_of_first_solution + int(lowerCAmelCase_ )
_UpperCAmelCase : Dict = best_node
first_solution.append(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_UpperCAmelCase : int = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10000
)
return first_solution, distance_of_first_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : int = []
for n in solution[1:-1]:
_UpperCAmelCase : Tuple = solution.index(lowerCAmelCase_ )
for kn in solution[1:-1]:
_UpperCAmelCase : int = solution.index(lowerCAmelCase_ )
if n == kn:
continue
_UpperCAmelCase : Tuple = copy.deepcopy(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = kn
_UpperCAmelCase : List[str] = n
_UpperCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_UpperCAmelCase : List[str] = _tmp[_tmp.index(lowerCAmelCase_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_UpperCAmelCase : Dict = distance + int(i[1] )
_tmp.append(lowerCAmelCase_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_UpperCAmelCase : Dict = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda lowerCAmelCase_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : Optional[Any] = first_solution
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : List[Any] = distance_of_first_solution
_UpperCAmelCase : Dict = solution
while count <= iters:
_UpperCAmelCase : Any = find_neighborhood(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
_UpperCAmelCase : Optional[Any] = len(lowerCAmelCase_ ) - 1
_UpperCAmelCase : Optional[Any] = False
while not found:
_UpperCAmelCase : Tuple = 0
while i < len(lowerCAmelCase_ ):
if best_solution[i] != solution[i]:
_UpperCAmelCase : Any = best_solution[i]
_UpperCAmelCase : str = solution[i]
break
_UpperCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : List[Any] = best_solution[:-1]
_UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_UpperCAmelCase : Tuple = cost
_UpperCAmelCase : List[Any] = solution
else:
_UpperCAmelCase : Any = index_of_best_solution + 1
_UpperCAmelCase : Dict = neighborhood[index_of_best_solution]
if len(lowerCAmelCase_ ) >= size:
tabu_list.pop(0 )
_UpperCAmelCase : Optional[Any] = count + 1
return best_solution_ever, best_cost
def snake_case_ ( lowerCAmelCase_=None )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Tuple = generate_neighbours(args.File )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = generate_first_solution(
args.File , lowerCAmelCase_ )
_UpperCAmelCase ,_UpperCAmelCase : str = tabu_search(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , args.Iterations , args.Size , )
print(F'''Best solution: {best_sol}, with total distance: {best_cost}.''' )
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser(description="""Tabu Search""")
parser.add_argument(
"""-f""",
"""--File""",
type=str,
help="""Path to the file containing the data""",
required=True,
)
parser.add_argument(
"""-i""",
"""--Iterations""",
type=int,
help="""How many iterations the algorithm should perform""",
required=True,
)
parser.add_argument(
"""-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 349 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : int = {}
if train_file is not None:
_UpperCAmelCase : int = [train_file]
if eval_file is not None:
_UpperCAmelCase : List[Any] = [eval_file]
if test_file is not None:
_UpperCAmelCase : Optional[int] = [test_file]
_UpperCAmelCase : str = datasets.load_dataset("""csv""" , data_files=lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = list(ds[list(files.keys() )[0]].features.keys() )
_UpperCAmelCase : Any = features_name.pop(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = list(set(ds[list(files.keys() )[0]][label_name] ) )
_UpperCAmelCase : List[str] = {label: i for i, label in enumerate(lowerCAmelCase_ )}
_UpperCAmelCase : Dict = tokenizer.model_input_names
_UpperCAmelCase : List[Any] = {}
if len(lowerCAmelCase_ ) == 1:
for k in files.keys():
_UpperCAmelCase : List[str] = ds[k].map(
lambda lowerCAmelCase_ : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" ) , batched=lowerCAmelCase_ , )
elif len(lowerCAmelCase_ ) == 2:
for k in files.keys():
_UpperCAmelCase : str = ds[k].map(
lambda lowerCAmelCase_ : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ , padding="""max_length""" , ) , batched=lowerCAmelCase_ , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
_UpperCAmelCase : Union[str, Any] = {k: v for k, v in ex.items() if k in input_names}
_UpperCAmelCase : Optional[Any] = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
_UpperCAmelCase : Any = {k: v for k, v in ex.items() if k in input_names}
_UpperCAmelCase : str = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
_UpperCAmelCase : List[Any] = {k: v for k, v in ex.items() if k in input_names}
_UpperCAmelCase : List[Any] = labelaid[ex[label_name]]
yield (d, label)
_UpperCAmelCase : Union[str, Any] = (
tf.data.Dataset.from_generator(
lowerCAmelCase_ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
_UpperCAmelCase : List[str] = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
_UpperCAmelCase : Dict = (
tf.data.Dataset.from_generator(
lowerCAmelCase_ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
_UpperCAmelCase : Optional[Any] = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
_UpperCAmelCase : Optional[int] = (
tf.data.Dataset.from_generator(
lowerCAmelCase_ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
_UpperCAmelCase : Dict = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
A_ : str = logging.getLogger(__name__)
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = field(metadata={"""help""": """Which column contains the label"""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """The path of the training file"""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """The path of the development file"""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """The path of the test file"""} )
UpperCAmelCase = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
UpperCAmelCase = field(default=_lowerCamelCase , metadata={"""help""": """Set this flag to use fast tokenization."""} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
UpperCAmelCase = field(
default=_lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
def snake_case_ ( )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(
F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
F'''16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase : str = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=lowerCAmelCase_ , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
_UpperCAmelCase : str = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(lowerCAmelCase_ ) , labelaid=lowerCAmelCase_ , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="""text-classification""" , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
_UpperCAmelCase : int = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(""".bin""" in model_args.model_name_or_path ) , config=lowerCAmelCase_ , cache_dir=model_args.cache_dir , )
def compute_metrics(lowerCAmelCase_ ) -> Dict:
_UpperCAmelCase : int = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
_UpperCAmelCase : List[str] = TFTrainer(
model=lowerCAmelCase_ , args=lowerCAmelCase_ , train_dataset=lowerCAmelCase_ , eval_dataset=lowerCAmelCase_ , compute_metrics=lowerCAmelCase_ , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCAmelCase : Tuple = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_UpperCAmelCase : Tuple = trainer.evaluate()
_UpperCAmelCase : int = os.path.join(training_args.output_dir , """eval_results.txt""" )
with open(lowerCAmelCase_ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(F''' {key} = {value}''' )
writer.write(F'''{key} = {value}\n''' )
results.update(lowerCAmelCase_ )
return results
if __name__ == "__main__":
main()
| 349 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> List[str]:
_UpperCAmelCase : list[list[Edge]] = [[] for _ in range(a_ )]
_UpperCAmelCase : int = size
def __getitem__( self ,a_ ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def _snake_case ( self ) -> List[Any]:
return self._size
def _snake_case ( self ,a_ ,a_ ,a_ ) -> Tuple:
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(a_ ,a_ ) )
def _snake_case ( self ,a_ ,a_ ) -> int | None:
_UpperCAmelCase : Union[str, Any] = deque([start_vertex] )
_UpperCAmelCase : list[int | None] = [None] * self.size
_UpperCAmelCase : Union[str, Any] = 0
while queue:
_UpperCAmelCase : Union[str, Any] = queue.popleft()
_UpperCAmelCase : Union[str, Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_UpperCAmelCase : List[Any] = current_distance + edge.weight
_UpperCAmelCase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(a_ ,a_ )
and new_distance >= dest_vertex_distance
):
continue
_UpperCAmelCase : Tuple = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_=14 ,a_=7 ,a_=True ,a_=True ,a_=False ,a_=True ,a_=99 ,a_=32 ,a_=4 ,a_=4 ,a_=4 ,a_=37 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=0.02 ,) -> Dict:
_UpperCAmelCase : int = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : Dict = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : List[Any] = use_input_mask
_UpperCAmelCase : Dict = use_token_type_ids
_UpperCAmelCase : List[str] = use_labels
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Optional[int] = rotary_dim
_UpperCAmelCase : List[Any] = num_hidden_layers
_UpperCAmelCase : Union[str, Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : List[str] = attention_probs_dropout_prob
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : Optional[Any] = None
_UpperCAmelCase : Any = vocab_size - 1
_UpperCAmelCase : List[Any] = vocab_size - 1
_UpperCAmelCase : Union[str, Any] = vocab_size - 1
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_UpperCAmelCase : Optional[int] = None
if self.use_input_mask:
_UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Optional[Any] = GPTJConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,use_cache=a_ ,bos_token_id=self.bos_token_id ,eos_token_id=self.eos_token_id ,pad_token_id=self.pad_token_id ,rotary_dim=self.rotary_dim ,)
return (config, input_ids, input_mask)
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = config_and_inputs
_UpperCAmelCase : Dict = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ) -> Dict:
_UpperCAmelCase : int = 20
_UpperCAmelCase : Optional[int] = model_class_name(a_ )
_UpperCAmelCase : str = model.init_cache(input_ids.shape[0] ,a_ )
_UpperCAmelCase : Optional[int] = jnp.ones((input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
_UpperCAmelCase : Union[str, Any] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] ,(input_ids.shape[0], input_ids.shape[-1] - 1) )
_UpperCAmelCase : Union[str, Any] = model(
input_ids[:, :-1] ,attention_mask=a_ ,past_key_values=a_ ,position_ids=a_ ,)
_UpperCAmelCase : Tuple = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,dtype="""i4""" )
_UpperCAmelCase : int = model(
input_ids[:, -1:] ,attention_mask=a_ ,past_key_values=outputs_cache.past_key_values ,position_ids=a_ ,)
_UpperCAmelCase : List[Any] = model(a_ )
_UpperCAmelCase : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 ,msg=f'''Max diff is {diff}''' )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Dict = 20
_UpperCAmelCase : Dict = model_class_name(a_ )
_UpperCAmelCase : Union[str, Any] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] ,axis=-1 ,)
_UpperCAmelCase : Tuple = model.init_cache(input_ids.shape[0] ,a_ )
_UpperCAmelCase : str = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] ,(input_ids.shape[0], input_ids.shape[-1] - 1) )
_UpperCAmelCase : int = model(
input_ids[:, :-1] ,attention_mask=a_ ,past_key_values=a_ ,position_ids=a_ ,)
_UpperCAmelCase : Optional[int] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,dtype="""i4""" )
_UpperCAmelCase : Dict = model(
input_ids[:, -1:] ,past_key_values=outputs_cache.past_key_values ,attention_mask=a_ ,position_ids=a_ ,)
_UpperCAmelCase : List[str] = model(a_ ,attention_mask=a_ )
_UpperCAmelCase : List[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 ,msg=f'''Max diff is {diff}''' )
@require_flax
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
UpperCAmelCase = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def _snake_case ( self ) -> int:
_UpperCAmelCase : Optional[int] = FlaxGPTJModelTester(self )
def _snake_case ( self ) -> str:
for model_class_name in self.all_model_classes:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(a_ ,a_ ,a_ ,a_ )
def _snake_case ( self ) -> Any:
for model_class_name in self.all_model_classes:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
a_ ,a_ ,a_ ,a_ )
@tooslow
def _snake_case ( self ) -> str:
_UpperCAmelCase : Dict = GPTaTokenizer.from_pretrained("""gpt2""" ,pad_token="""<|endoftext|>""" ,padding_side="""left""" )
_UpperCAmelCase : str = tokenizer(["""Hello this is a long string""", """Hey"""] ,return_tensors="""np""" ,padding=a_ ,truncation=a_ )
_UpperCAmelCase : int = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : Union[str, Any] = model.config.eos_token_id
_UpperCAmelCase : Tuple = jax.jit(model.generate )
_UpperCAmelCase : Optional[Any] = jit_generate(
inputs["""input_ids"""] ,attention_mask=inputs["""attention_mask"""] ,pad_token_id=tokenizer.pad_token_id ).sequences
_UpperCAmelCase : List[str] = tokenizer.batch_decode(a_ ,skip_special_tokens=a_ )
_UpperCAmelCase : int = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(a_ ,a_ )
@is_pt_flax_cross_test
def _snake_case ( self ) -> List[str]:
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(a_ ,a_ )
_UpperCAmelCase : int = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_UpperCAmelCase : List[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
_UpperCAmelCase : Union[str, Any] = getattr(a_ ,a_ )
_UpperCAmelCase ,_UpperCAmelCase : int = pt_inputs["""input_ids"""].shape
_UpperCAmelCase : Tuple = np.random.randint(0 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(a_ ):
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Optional[int] = 1
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = 1
_UpperCAmelCase : Optional[int] = pt_model_class(a_ ).eval()
_UpperCAmelCase : int = model_class(a_ ,dtype=jnp.floataa )
_UpperCAmelCase : List[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,a_ )
_UpperCAmelCase : Dict = fx_state
with torch.no_grad():
_UpperCAmelCase : Optional[Any] = pt_model(**a_ ).to_tuple()
_UpperCAmelCase : str = fx_model(**a_ ).to_tuple()
self.assertEqual(len(a_ ) ,len(a_ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(a_ ,a_ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(a_ )
_UpperCAmelCase : Optional[int] = model_class.from_pretrained(a_ ,from_pt=a_ )
_UpperCAmelCase : int = fx_model_loaded(**a_ ).to_tuple()
self.assertEqual(
len(a_ ) ,len(a_ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(a_ ,a_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] ,pt_output[:, -1].numpy() ,4E-2 )
@is_pt_flax_cross_test
def _snake_case ( self ) -> str:
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_UpperCAmelCase : Union[str, Any] = self._prepare_for_class(a_ ,a_ )
_UpperCAmelCase : str = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_UpperCAmelCase : Union[str, Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
_UpperCAmelCase : Any = getattr(a_ ,a_ )
_UpperCAmelCase : Any = pt_model_class(a_ ).eval()
_UpperCAmelCase : Optional[int] = model_class(a_ ,dtype=jnp.floataa )
_UpperCAmelCase : Union[str, Any] = load_flax_weights_in_pytorch_model(a_ ,fx_model.params )
_UpperCAmelCase ,_UpperCAmelCase : Tuple = pt_inputs["""input_ids"""].shape
_UpperCAmelCase : Any = np.random.randint(0 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(a_ ):
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Optional[Any] = 1
_UpperCAmelCase : Any = 0
_UpperCAmelCase : Optional[int] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_UpperCAmelCase : List[Any] = pt_model(**a_ ).to_tuple()
_UpperCAmelCase : Optional[int] = fx_model(**a_ ).to_tuple()
self.assertEqual(len(a_ ) ,len(a_ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(a_ ,a_ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(a_ )
_UpperCAmelCase : str = pt_model_class.from_pretrained(a_ ,from_flax=a_ )
with torch.no_grad():
_UpperCAmelCase : str = pt_model_loaded(**a_ ).to_tuple()
self.assertEqual(
len(a_ ) ,len(a_ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(a_ ,a_ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4E-2 )
@tooslow
def _snake_case ( self ) -> int:
for model_class_name in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
_UpperCAmelCase : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(a_ )
| 349 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Any = 1_6
A_ : Union[str, Any] = 3_2
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_UpperCAmelCase : str = DatasetDict(
{
"""train""": dataset["""train"""].select(lowerCAmelCase_ ),
"""validation""": dataset["""train"""].select(lowerCAmelCase_ ),
"""test""": dataset["""validation"""],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : List[str] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Optional[int] = datasets.map(
lowerCAmelCase_ , batched=lowerCAmelCase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : List[str] = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : Dict = None
return tokenizer.pad(
lowerCAmelCase_ , padding="""longest""" , max_length=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
_UpperCAmelCase : Dict = DataLoader(
tokenized_datasets["""test"""] , shuffle=lowerCAmelCase_ , collate_fn=lowerCAmelCase_ , batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader, test_dataloader
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = []
# Download the dataset
_UpperCAmelCase : Dict = load_dataset("""glue""" , """mrpc""" )
# Create our splits
_UpperCAmelCase : Optional[Any] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_UpperCAmelCase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : Dict = config["""lr"""]
_UpperCAmelCase : List[Any] = int(config["""num_epochs"""] )
_UpperCAmelCase : str = int(config["""seed"""] )
_UpperCAmelCase : List[Any] = int(config["""batch_size"""] )
_UpperCAmelCase : int = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase : Dict = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase : Tuple = MAX_GPU_BATCH_SIZE
set_seed(lowerCAmelCase_ )
# New Code #
# Create our folds:
_UpperCAmelCase : Any = kfold.split(np.zeros(datasets["""train"""].num_rows ) , datasets["""train"""]["""label"""] )
_UpperCAmelCase : Tuple = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_ ):
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = get_fold_dataloaders(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : int = AdamW(params=model.parameters() , lr=lowerCAmelCase_ )
# Instantiate scheduler
_UpperCAmelCase : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Any = accelerator.prepare(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase : Union[str, Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Dict = outputs.loss
_UpperCAmelCase : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**lowerCAmelCase_ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowerCAmelCase_ , references=lowerCAmelCase_ , )
_UpperCAmelCase : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCAmelCase_ )
# New Code #
# We also run predictions on the test set at the very end
_UpperCAmelCase : Tuple = []
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : List[Any] = model(**lowerCAmelCase_ )
_UpperCAmelCase : Any = outputs.logits
_UpperCAmelCase ,_UpperCAmelCase : List[Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(lowerCAmelCase_ , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_UpperCAmelCase : List[Any] = torch.cat(lowerCAmelCase_ , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.stack(lowerCAmelCase_ , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_UpperCAmelCase : List[str] = metric.compute(predictions=lowerCAmelCase_ , references=lowerCAmelCase_ )
accelerator.print("""Average test metrics from all folds:""" , lowerCAmelCase_ )
def snake_case_ ( )-> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
# New Code #
parser.add_argument("""--num_folds""" , type=lowerCAmelCase_ , default=3 , help="""The number of splits to perform across the dataset""" )
_UpperCAmelCase : Optional[int] = parser.parse_args()
_UpperCAmelCase : Tuple = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCAmelCase_ , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 349 | 1 |