



"""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


A:     Union[str, Any]										=	logging.get_logger(__name__)

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

def    _snake_case		(		UpperCamelCase		:	Any    ,	UpperCamelCase		:	List[str]		):
 UpperCAmelCase			:      Tuple			=			x.reshape(-1    ,	3		)
 UpperCAmelCase			:      List[Any]			=			squared_euclidean_distance(UpperCamelCase    ,	UpperCamelCase		)
 return np.argmin(UpperCamelCase    ,	axis=1		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[Any]		=		['pixel_values']

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   None:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   np.ndarray:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			get_size_dict(_SCREAMING_SNAKE_CASE		)
    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(
        _SCREAMING_SNAKE_CASE   ,  size=(size["""height"""], size["""width"""])   ,  resample=_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  )						->   np.ndarray:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			rescale(image=_SCREAMING_SNAKE_CASE   ,  scale=1 / 127.5   ,  data_format=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			image - 1
    return image




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   PIL.Image.Image:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase			:      Optional[Any]			=			size if size is not None else self.size
    UpperCAmelCase			:      List[str]			=			get_size_dict(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			resample if resample is not None else self.resample
    UpperCAmelCase			:      Optional[Any]			=			do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase			:      Optional[int]			=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    UpperCAmelCase			:      Optional[Any]			=			clusters if clusters is not None else self.clusters
    UpperCAmelCase			:      Tuple			=			np.array(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			make_list_of_images(_SCREAMING_SNAKE_CASE		)

    if not valid_images(_SCREAMING_SNAKE_CASE		):
     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.
    UpperCAmelCase			:      str			=			[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

    if do_resize:
     UpperCAmelCase			:      Any			=			[self.resize(image=_SCREAMING_SNAKE_CASE   ,  size=_SCREAMING_SNAKE_CASE   ,  resample=_SCREAMING_SNAKE_CASE		) for image in images]

    if do_normalize:
     UpperCAmelCase			:      Dict			=			[self.normalize(image=_SCREAMING_SNAKE_CASE		) for image in images]

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

     # flatten to (batch_size, height*width)
     UpperCAmelCase			:      Union[str, Any]			=			images.shape[0]
     UpperCAmelCase			:      Dict			=			images.reshape(_SCREAMING_SNAKE_CASE   ,  -1		)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     UpperCAmelCase			:      Dict			=			list(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) for image in images]

    UpperCAmelCase			:      List[str]			=			{"""input_ids""": images}
    return BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)





"""simple docstring"""
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    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["""bs"""]   ,  model_result["""ss"""]		):
      UpperCAmelCase			:      List[str]			=			model_result["""result"""][batch_size][sequence_length]
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sgugger/tiny-distilbert-classification"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  only_pretrain_model=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  torchscript=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[int]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      List[str]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      Optional[int]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Optional[Any]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      int			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




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




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE		):
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """sequential"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """cumulative"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """current"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """total"""		)		)

    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  log_filename=os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)   ,  log_print=_SCREAMING_SNAKE_CASE   ,  trace_memory_line_by_line=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[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(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)		).exists()		)
