




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch


class 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase ,       __lowercase=14 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=False ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=32 ,       __lowercase=4 ,       __lowercase=4 ,       __lowercase=[[["global", "local"], 2]] ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=7 ,       __lowercase=0.02 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Tuple							   =							seq_length
						lowerCamelCase_							:		Optional[Any]							   =							is_training
						lowerCamelCase_							:		List[Any]							   =							use_input_mask
						lowerCamelCase_							:		Dict							   =							use_token_type_ids
						lowerCamelCase_							:		int							   =							use_labels
						lowerCamelCase_							:		Dict							   =							vocab_size
						lowerCamelCase_							:		List[Any]							   =							hidden_size
						lowerCamelCase_							:		Union[str, Any]							   =							num_hidden_layers
						lowerCamelCase_							:		List[str]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							attention_types
						lowerCamelCase_							:		Tuple							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		List[str]							   =							hidden_dropout_prob
						lowerCamelCase_							:		Tuple							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		List[str]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							window_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		Tuple							   =							None
						lowerCamelCase_							:		Optional[Any]							   =							vocab_size - 1
						lowerCamelCase_							:		Any							   =							vocab_size - 1
						lowerCamelCase_							:		str							   =							vocab_size - 1

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		Optional[int]							   =							None
						if self.use_input_mask:
							lowerCamelCase_							:		Any							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							GPTNeoConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       num_layers=self.num_hidden_layers ,       num_heads=self.num_attention_heads ,       max_position_embeddings=self.max_position_embeddings ,       use_cache=__lowercase ,       bos_token_id=self.bos_token_id ,       eos_token_id=self.eos_token_id ,       pad_token_id=self.pad_token_id ,       window_size=self.window_size ,       attention_types=self.attention_types ,       )

						return (config, input_ids, input_mask)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							config_and_inputs
						lowerCamelCase_							:		Dict							   =							{'input_ids': input_ids, 'attention_mask': attention_mask}
						return config, inputs_dict

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							20
						lowerCamelCase_							:		Optional[Any]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		List[Any]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Any							   =							jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype='i4'					)

						lowerCamelCase_							:		Union[str, Any]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)
						lowerCamelCase_							:		List[Any]							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Any							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		int							   =							model(
						    input_ids[:, -1:] ,       attention_mask=__lowercase ,       past_key_values=outputs_cache.past_key_values ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[int]							   =							model(__lowercase					)

						lowerCamelCase_							:		Union[str, 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 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							20
						lowerCamelCase_							:		List[str]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)] ,       axis=-1 ,       )

						lowerCamelCase_							:		List[str]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)

						lowerCamelCase_							:		Tuple							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )
						lowerCamelCase_							:		Optional[int]							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		Optional[Any]							   =							model(
						    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[Any]							   =							model(__lowercase ,       attention_mask=__lowercase					)

						lowerCamelCase_							:		List[str]							   =							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 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					_lowerCamelCase		  = (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							FlaxGPTNeoModelTester(self					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		List[str]							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							GPTaTokenizer.from_pretrained('gpt2' ,       pad_token='<|endoftext|>' ,       padding_side='left'					)
						lowerCamelCase_							:		Dict							   =							tokenizer(['Hello this is a long string', 'Hey'] ,       return_tensors='np' ,       padding=__lowercase ,       truncation=__lowercase					)

						lowerCamelCase_							:		Optional[Any]							   =							FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'					)
						lowerCamelCase_							:		Union[str, Any]							   =							False
						lowerCamelCase_							:		Tuple							   =							model.config.eos_token_id

						lowerCamelCase_							:		str							   =							jax.jit(model.generate					)

						lowerCamelCase_							:		Union[str, Any]							   =							jit_generate(
						    inputs['input_ids'] ,       attention_mask=inputs['attention_mask'] ,       pad_token_id=tokenizer.pad_token_id					).sequences

						lowerCamelCase_							:		Tuple							   =							tokenizer.batch_decode(__lowercase ,       skip_special_tokens=__lowercase					)

						lowerCamelCase_							:		Dict							   =							[
						    '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(__lowercase ,       __lowercase					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		List[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
								lowerCamelCase_							:		List[str]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		List[Any]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Optional[Any]							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		Dict							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		int							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[int]							   =							0
									lowerCamelCase_							:		List[Any]							   =							1
									lowerCamelCase_							:		Any							   =							0
									lowerCamelCase_							:		List[str]							   =							1
								lowerCamelCase_							:		Tuple							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Optional[int]							   =							convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       __lowercase					)
								lowerCamelCase_							:		List[str]							   =							fx_state

								with torch.no_grad():
									lowerCamelCase_							:		Dict							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		Union[str, Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		Dict							   =							model_class.from_pretrained(__lowercase ,       from_pt=__lowercase					)

								lowerCamelCase_							:		Optional[int]							   =							fx_model_loaded(**__lowercase					).to_tuple()
								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output_loaded, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output_loaded[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		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
								lowerCamelCase_							:		Optional[int]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[int]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Any							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		List[str]							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_							:		int							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Any							   =							load_flax_weights_in_pytorch_model(__lowercase ,       fx_model.params					)
								lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		Optional[Any]							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[Any]							   =							0
									lowerCamelCase_							:		List[str]							   =							1
									lowerCamelCase_							:		Dict							   =							0
									lowerCamelCase_							:		str							   =							1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		List[Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		str							   =							pt_model_class.from_pretrained(__lowercase ,       from_flax=__lowercase					)

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model_loaded(**__lowercase					).to_tuple()

								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_							:		int							   =							model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'					)
							lowerCamelCase_							:		Union[str, Any]							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)







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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												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."""							)

								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."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

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

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





