
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__    )


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__    )
