

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
			from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			,				_a			)       ->       str:
												for model_result in results.values():
																	for batch_size, sequence_length in zip(model_result['''bs''']			,				model_result['''ss''']			):
																						_a      :       Optional[Any]   =						model_result['''result'''][batch_size][sequence_length]
																						self.assertIsNotNone(_a			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       str   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       str   =						'''sgugger/tiny-distilbert-classification'''
												_a      :       str   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				only_pretrain_model=_a			,				)
												_a      :       Any   =						PyTorchBenchmark(_a			)
												_a      :       Dict   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				torchscript=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       Any   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							@unittest.skipIf(torch_device == '''cpu'''			,				'''Cant do half precision'''			)
							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Any   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				fpaa=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			)
												_a      :       Any   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						'''sshleifer/tiny-gpt2'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												# set architectures equal to `None`
												_a      :       List[Any]   =						None
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[Any]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       int   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Tuple   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Dict   =						PyTorchBenchmark(_a			)
												_a      :       str   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result			)
												self.check_results_dict_not_empty(results.memory_train_result			)


							@unittest.skipIf(torch_device == '''cpu'''			,				'''Can\'t do half precision'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						'''sshleifer/tiny-gpt2'''
												_a      :       Tuple   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				fpaa=_a			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			)
												_a      :       List[str]   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result			)
												self.check_results_dict_not_empty(results.memory_train_result			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       int   =						'''sshleifer/tiny-gpt2'''
												_a      :       List[str]   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[str]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       List[str]   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						'''sshleifer/tinier_bart'''
												_a      :       Optional[int]   =						AutoConfig.from_pretrained(_a			)
												_a      :       Optional[int]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       List[str]   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       Any   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result			)
												self.check_results_dict_not_empty(results.memory_inference_result			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''
												_a      :       int   =						AutoConfig.from_pretrained(_a			)
												_a      :       Union[str, Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       Tuple   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       str   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result			)
												self.check_results_dict_not_empty(results.memory_train_result			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Dict   =						'''sshleifer/tinier_bart'''
												_a      :       Dict   =						AutoConfig.from_pretrained(_a			)
												_a      :       List[Any]   =						PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				multi_process=_a			,				)
												_a      :       int   =						PyTorchBenchmark(_a			,				configs=[config]			)
												_a      :       Any   =						benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result			)
												self.check_results_dict_not_empty(results.memory_train_result			)


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       List[str]   =						'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       Tuple   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				save_to_csv=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				inference_time_csv_file=os.path.join(_a			,				'''inf_time.csv'''			)			,				train_memory_csv_file=os.path.join(_a			,				'''train_mem.csv'''			)			,				inference_memory_csv_file=os.path.join(_a			,				'''inf_mem.csv'''			)			,				train_time_csv_file=os.path.join(_a			,				'''train_time.csv'''			)			,				env_info_csv_file=os.path.join(_a			,				'''env.csv'''			)			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	benchmark.run()
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_time.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''inf_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''train_mem.csv'''			)			).exists()			)
																	self.assertTrue(Path(os.path.join(_a			,				'''env.csv'''			)			).exists()			)




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[int]   =						'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(_a			):
																	self.assertTrue(hasattr(_a			,				'''sequential'''			)			)
																	self.assertTrue(hasattr(_a			,				'''cumulative'''			)			)
																	self.assertTrue(hasattr(_a			,				'''current'''			)			)
																	self.assertTrue(hasattr(_a			,				'''total'''			)			)

												with tempfile.TemporaryDirectory() as tmp_dir:
																	_a      :       str   =						PyTorchBenchmarkArguments(
																	    models=[MODEL_ID]			,				training=_a			,				inference=_a			,				sequence_lengths=[8]			,				batch_sizes=[1]			,				log_filename=os.path.join(_a			,				'''log.txt'''			)			,				log_print=_a			,				trace_memory_line_by_line=_a			,				multi_process=_a			,				)
																	_a      :       List[str]   =						PyTorchBenchmark(_a			)
																	_a      :       Any   =						benchmark.run()
																	_check_summary_is_not_empty(result.inference_summary			)
																	_check_summary_is_not_empty(result.train_summary			)
																	self.assertTrue(Path(os.path.join(_a			,				'''log.txt'''			)			).exists()			)







'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							make_list_of_images(lowerCamelCase		)

          if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

          if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

          if do_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

