code: 
  from __future__ import annotations
  
  import unittest
  
  from transformers import OpenAIGPTConfig, 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.openai.modeling_tf_openai import (
  						    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFOpenAIGPTDoubleHeadsModel,
  						    TFOpenAIGPTForSequenceClassification,
  						    TFOpenAIGPTLMHeadModel,
  						    TFOpenAIGPTModel,
  						)
  
  
  
  
  
  class      UpperCamelCase_			:
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :List[str]		,						lowerCAmelCase__       :Dict		,						)     ->int:
  										lowercase						    =			parent
  										lowercase						    =			13
  										lowercase						    =			7
  										lowercase						    =			True
  										lowercase						    =			True
  										lowercase						    =			True
  										lowercase						    =			True
  										lowercase						    =			True
  										lowercase						    =			99
  										lowercase						    =			32
  										lowercase						    =			2
  										lowercase						    =			4
  										lowercase						    =			37
  										lowercase						    =			"gelu"
  										lowercase						    =			0.1
  										lowercase						    =			0.1
  										lowercase						    =			512
  										lowercase						    =			16
  										lowercase						    =			2
  										lowercase						    =			0.02
  										lowercase						    =			3
  										lowercase						    =			4
  										lowercase						    =			None
  										lowercase						    =			self.vocab_size - 1
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->List[Any]:
  										lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size    )
  
  										lowercase						    =			None
  										if self.use_input_mask:
  														lowercase						    =			random_attention_mask([self.batch_size, self.seq_length]    )
  
  										lowercase						    =			None
  										if self.use_token_type_ids:
  														lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size    )
  
  										lowercase						    =			None
  										if self.use_mc_token_ids:
  														lowercase						    =			ids_tensor([self.batch_size, self.num_choices]		,						self.seq_length    )
  
  										lowercase						    =			None
  										lowercase						    =			None
  										lowercase						    =			None
  										if self.use_labels:
  														lowercase						    =			ids_tensor([self.batch_size]		,						self.type_sequence_label_size    )
  														lowercase						    =			ids_tensor([self.batch_size, self.seq_length]		,						self.num_labels    )
  														lowercase						    =			ids_tensor([self.batch_size]		,						self.num_choices    )
  
  										lowercase						    =			OpenAIGPTConfig(
  										    vocab_size=self.vocab_size		,						n_embd=self.hidden_size		,						n_layer=self.num_hidden_layers		,						n_head=self.num_attention_heads		,						n_positions=self.max_position_embeddings		,						pad_token_id=self.pad_token_id		,						)
  
  										lowercase						    =			ids_tensor([self.num_hidden_layers, self.num_attention_heads]		,						2    )
  
  										return (
  										    config,
  										    input_ids,
  										    input_mask,
  										    head_mask,
  										    token_type_ids,
  										    mc_token_ids,
  										    sequence_labels,
  										    token_labels,
  										    choice_labels,
  										)
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :Any		,						*lowerCAmelCase__       :Optional[int]    )     ->Optional[Any]:
  										lowercase						    =			TFOpenAIGPTModel(config=lowerCAmelCase__    )
  										lowercase						    =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  										lowercase						    =			model(lowerCAmelCase__    )
  
  										lowercase						    =			[input_ids, input_mask]
  										lowercase						    =			model(lowerCAmelCase__    )
  
  										lowercase						    =			model(lowerCAmelCase__    )
  
  										self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :List[str]		,						*lowerCAmelCase__       :int    )     ->Union[str, Any]:
  										lowercase						    =			TFOpenAIGPTLMHeadModel(config=lowerCAmelCase__    )
  										lowercase						    =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  										lowercase						    =			model(lowerCAmelCase__    )
  										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Dict		,						*lowerCAmelCase__       :str    )     ->Optional[int]:
  										lowercase						    =			TFOpenAIGPTDoubleHeadsModel(config=lowerCAmelCase__    )
  
  										lowercase						    =			tf.tile(tf.expand_dims(lowerCAmelCase__		,						1    )		,						(1, self.num_choices, 1)    )
  										lowercase						    =			tf.tile(tf.expand_dims(lowerCAmelCase__		,						1    )		,						(1, self.num_choices, 1)    )
  										lowercase						    =			tf.tile(tf.expand_dims(lowerCAmelCase__		,						1    )		,						(1, self.num_choices, 1)    )
  
  										lowercase						    =			{
  										    "input_ids": multiple_choice_inputs_ids,
  										    "mc_token_ids": mc_token_ids,
  										    "attention_mask": multiple_choice_input_mask,
  										    "token_type_ids": multiple_choice_token_type_ids,
  										}
  										lowercase						    =			model(lowerCAmelCase__    )
  										self.parent.assertEqual(
  										    result.logits.shape		,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)    )
  										self.parent.assertEqual(result.mc_logits.shape		,						(self.batch_size, self.num_choices)    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Any		,						*lowerCAmelCase__       :List[Any]    )     ->str:
  										lowercase						    =			self.num_labels
  										lowercase						    =			ids_tensor([self.batch_size]		,						self.type_sequence_label_size    )
  										lowercase						    =			{
  										    "input_ids": input_ids,
  										    "attention_mask": input_mask,
  										    "token_type_ids": token_type_ids,
  										    "labels": sequence_labels,
  										}
  										lowercase						    =			TFOpenAIGPTForSequenceClassification(lowerCAmelCase__    )
  										lowercase						    =			model(lowerCAmelCase__    )
  										self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Dict:
  										lowercase						    =			self.prepare_config_and_inputs()
  
  										(
  										    (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  (
  										    lowercase
  										)    ,  
  										)						    =			config_and_inputs
  
  										lowercase						    =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  										return config, inputs_dict
  
  
  
  
  
  @require_tf
  class      UpperCamelCase_			(			__a		,						__a		,						unittest.TestCase		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     int							      =				(
  						    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
  						    if is_tf_available()
  						    else ()
  						)
  						UpperCamelCase			:     Dict							      =				(
  						    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
  						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  						UpperCamelCase			:     List[Any]							      =				(
  						    {
  						        '''feature-extraction''': TFOpenAIGPTModel,
  						        '''text-classification''': TFOpenAIGPTForSequenceClassification,
  						        '''text-generation''': TFOpenAIGPTLMHeadModel,
  						        '''zero-shot''': TFOpenAIGPTForSequenceClassification,
  						    }
  						    if is_tf_available()
  						    else {}
  						)
  						UpperCamelCase			:     Tuple							      =				False
  						UpperCamelCase			:     Dict							      =				False
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :List[Any]    )     ->Tuple:
  										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
  														# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
  														# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
  														# tiny config could not be created.
  														return True
  
  										return False
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->List[Any]:
  										lowercase						    =			TFOpenAIGPTModelTester(self    )
  										lowercase						    =			ConfigTester(self		,						config_class=lowerCAmelCase__		,						n_embd=37    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->Optional[Any]:
  										self.config_tester.run_common_tests()
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->Optional[Any]:
  										lowercase						    =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]    )     ->Any:
  										lowercase						    =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_openai_gpt_lm_head(*lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any    )     ->List[Any]:
  										lowercase						    =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_openai_gpt_double_head(*lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->Optional[Any]:
  										lowercase						    =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase__    )
  
  
  
  
  
  						@slow
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]    )     ->Tuple:
  										for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  														lowercase						    =			TFOpenAIGPTModel.from_pretrained(lowerCAmelCase__    )
  														self.assertIsNotNone(lowerCAmelCase__    )
  
  
  
  
  
  @require_tf
  class      UpperCamelCase_			(			unittest.TestCase		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						@slow
  						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->str:
  										lowercase						    =			TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"    )
  										lowercase						    =			tf.convert_to_tensor([[481, 4735, 544]]		,						dtype=tf.intaa    )  # the president is
  										lowercase						    =			[
  										    481,
  										    4735,
  										    544,
  										    246,
  										    963,
  										    870,
  										    762,
  										    239,
  										    244,
  										    40477,
  										    244,
  										    249,
  										    719,
  										    881,
  										    487,
  										    544,
  										    240,
  										    244,
  										    603,
  										    481,
  										]  # the president is a very good man. " \n " i\'m sure he is, " said the
  
  										lowercase						    =			model.generate(lowerCAmelCase__		,						do_sample=lowerCAmelCase__    )
  										self.assertListEqual(output_ids[0].numpy().tolist()		,						lowerCAmelCase__    )
  
style_context: 
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  _snake_case			:       Tuple      =						logging.get_logger(__name__)
  
  
  if is_vision_available():
  						import PIL
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']
  
  
  
  
  
  						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
  										super().__init__(**lowerCAmelCase__    )
  
  										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# The previous feature extractor size parameter was in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  
  										lowercase						    =			do_resize
  										lowercase						    =			size
  										lowercase						    =			resample
  										lowercase						    =			do_thumbnail
  										lowercase						    =			do_align_long_axis
  										lowercase						    =			do_pad
  										lowercase						    =			do_rescale
  										lowercase						    =			rescale_factor
  										lowercase						    =			do_normalize
  										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										if (output_width < output_height and input_width > input_height) or (
  										    output_width > output_height and input_width < input_height
  										):
  														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )
  
  										if data_format is not None:
  														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										return image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  
  										lowercase						    =			output_width - input_width
  										lowercase						    =			output_height - input_height
  
  										if random_padding:
  														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
  														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
  										else:
  														lowercase						    =			delta_height // 2
  														lowercase						    =			delta_width // 2
  
  										lowercase						    =			delta_height - pad_top
  										lowercase						    =			delta_width - pad_left
  
  										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
  										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
  										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
  										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										# We always resize to the smallest of either the input or output size.
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										if height == input_height and width == input_width:
  														return image
  
  										if input_height > input_width:
  														lowercase						    =			int(input_width * height / input_height    )
  										elif input_width > input_height:
  														lowercase						    =			int(input_height * width / input_width    )
  
  										return resize(
  										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			min(size["height"]		,						size["width"]    )
  										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
  										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  										return resized_image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
  										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
  										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
  										lowercase						    =			do_resize if do_resize is not None else self.do_resize
  										lowercase						    =			size if size is not None else self.size
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# Previous feature extractor had size in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			resample if resample is not None else self.resample
  										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  										lowercase						    =			do_pad if do_pad is not None else self.do_pad
  										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
  										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
  										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
  										lowercase						    =			image_mean if image_mean is not None else self.image_mean
  										lowercase						    =			image_std if image_std is not None else self.image_std
  
  										lowercase						    =			make_list_of_images(lowerCAmelCase__    )
  
  										if not valid_images(lowerCAmelCase__    ):
  														raise ValueError(
  														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
  														    "torch.Tensor, tf.Tensor or jax.ndarray."    )
  
  										if do_resize and size is None:
  														raise ValueError("Size must be specified if do_resize is True."    )
  
  										if do_rescale and rescale_factor is None:
  														raise ValueError("Rescale factor must be specified if do_rescale is True."    )
  
  										if do_pad and size is None:
  														raise ValueError("Size must be specified if do_pad is True."    )
  
  										if do_normalize and (image_mean is None or image_std is None):
  														raise ValueError("Image mean and std must be specified if do_normalize is True."    )
  
  										# All transformations expect numpy arrays.
  										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]
  
  										if do_align_long_axis:
  														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_resize:
  														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]
  
  										if do_thumbnail:
  														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_pad:
  														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]
  
  										if do_rescale:
  														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]
  
  										if do_normalize:
  														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			{"pixel_values": images}
  										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
  