import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			_SCREAMING_SNAKE_CASE:    Optional[int]     						=							'''platform'''

			import jax
			import jax.numpy as jnp

			from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=None				,						lowerCamelCase_=None	):



			"""simple docstring"""





			if attention_mask is None:
						_snake_case														=   np.where(input_ids != config.pad_token_id				,						1				,						0	)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}

@require_flax
class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=13	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=7	,       __SCREAMING_SNAKE_CASE     :					Tuple=True	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=False	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=99	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=16	,       __SCREAMING_SNAKE_CASE     :					Dict=2	,       __SCREAMING_SNAKE_CASE     :					Tuple=4	,       __SCREAMING_SNAKE_CASE     :					List[Any]=4	,       __SCREAMING_SNAKE_CASE     :					Tuple="gelu"	,       __SCREAMING_SNAKE_CASE     :					Dict=0.1	,       __SCREAMING_SNAKE_CASE     :					Dict=0.1	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=20	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=2	,       __SCREAMING_SNAKE_CASE     :					int=1	,       __SCREAMING_SNAKE_CASE     :					Tuple=0	,       __SCREAMING_SNAKE_CASE     :					Any=16	,       __SCREAMING_SNAKE_CASE     :					List[Any]=16	,       __SCREAMING_SNAKE_CASE     :					str=0.0_2	,       ):






					"""simple docstring"""
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   seq_length
					_snake_case														=   is_training
					_snake_case														=   use_labels
					_snake_case														=   vocab_size
					_snake_case														=   hidden_size
					_snake_case														=   num_hidden_layers
					_snake_case														=   num_attention_heads
					_snake_case														=   intermediate_size
					_snake_case														=   hidden_act
					_snake_case														=   hidden_dropout_prob
					_snake_case														=   attention_probs_dropout_prob
					_snake_case														=   max_position_embeddings
					_snake_case														=   eos_token_id
					_snake_case														=   pad_token_id
					_snake_case														=   bos_token_id
					_snake_case														=   embed_dim
					_snake_case														=   word_embed_proj_dim
					_snake_case														=   initializer_range
					_snake_case														=   False



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,       self.vocab_size )	,       3	,       self.vocab_size )
					_snake_case														=   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,       dtype=np.intaa ))	,       -1 )

					_snake_case														=   OPTConfig(
					    vocab_size=self.vocab_size	,       hidden_size=self.hidden_size	,       num_hidden_layers=self.num_hidden_layers	,       num_attention_heads=self.num_attention_heads	,       ffn_dim=self.intermediate_size	,       dropout=self.hidden_dropout_prob	,       attention_dropout=self.attention_probs_dropout_prob	,       max_position_embeddings=self.max_position_embeddings	,       eos_token_id=self.eos_token_id	,       bos_token_id=self.bos_token_id	,       pad_token_id=self.pad_token_id	,       embed_dim=self.embed_dim	,       is_encoder_decoder=__SCREAMING_SNAKE_CASE	,       word_embed_proj_dim=self.word_embed_proj_dim	,       initializer_range=self.initializer_range	,       use_cache=__SCREAMING_SNAKE_CASE	,       )
					_snake_case														=   prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					return config, inputs_dict



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					_snake_case							,			_snake_case														=   self.prepare_config_and_inputs()
					return config, inputs_dict



		def 					_a					(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   20
					_snake_case														=   model_class_name(__SCREAMING_SNAKE_CASE )

					_snake_case														=   inputs_dict["input_ids"]
					_snake_case														=   inputs_dict["attention_mask"]

					_snake_case														=   model.init_cache(input_ids.shape[0]	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   jnp.ones((input_ids.shape[0], max_length)	,       dtype="i4" )

					_snake_case														=   jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,       (input_ids.shape[0], input_ids.shape[-1] - 1)	,       )
					_snake_case														=   model(
					    input_ids[:, :-1]	,       attention_mask=__SCREAMING_SNAKE_CASE	,       past_key_values=__SCREAMING_SNAKE_CASE	,       position_ids=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,       dtype="i4" )
					_snake_case														=   model(
					    input_ids[:, -1:]	,       attention_mask=__SCREAMING_SNAKE_CASE	,       past_key_values=outputs_cache.past_key_values	,       position_ids=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   model(__SCREAMING_SNAKE_CASE )

					_snake_case														=   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 					_a					(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Dict	,       __SCREAMING_SNAKE_CASE     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   20
					_snake_case														=   model_class_name(__SCREAMING_SNAKE_CASE )

					_snake_case							,			_snake_case														=   (
					    inputs_dict["input_ids"],
					    inputs_dict["attention_mask"],
					)

					_snake_case														=   jnp.concatenate(
					    [
					        attention_mask,
					        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
					    ]	,       axis=-1	,       )

					_snake_case														=   model.init_cache(input_ids.shape[0]	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,       (input_ids.shape[0], input_ids.shape[-1] - 1)	,       )

					_snake_case														=   model(
					    input_ids[:, :-1]	,       attention_mask=__SCREAMING_SNAKE_CASE	,       past_key_values=__SCREAMING_SNAKE_CASE	,       position_ids=__SCREAMING_SNAKE_CASE	,       )
					_snake_case														=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,       dtype="i4" )
					_snake_case														=   model(
					    input_ids[:, -1:]	,       past_key_values=outputs_cache.past_key_values	,       attention_mask=__SCREAMING_SNAKE_CASE	,       position_ids=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   model(__SCREAMING_SNAKE_CASE	,       attention_mask=__SCREAMING_SNAKE_CASE )

					_snake_case														=   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				(  _lowercase   ,  unittest.TestCase   ,  _lowercase				):






		"""simple docstring"""







		A_    			=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
		A_    			=   () if is_flax_available() else ()



		def 					_a					(      self     :					Union[str, Any] ):






					"""simple docstring"""
					_snake_case														=   FlaxOPTModelTester(self )



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case							,			_snake_case														=   self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					_snake_case							,			_snake_case														=   self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )







		@slow
		def 					_a					(      self     :					str ):






					"""simple docstring"""
					for model_class_name in self.all_model_classes:
								_snake_case														=   model_class_name.from_pretrained("facebook/opt-125m" )
								_snake_case														=   np.ones((1, 1) ) * model.config.eos_token_id
								_snake_case														=   model(__SCREAMING_SNAKE_CASE )
								self.assertIsNotNone(__SCREAMING_SNAKE_CASE )

@require_sentencepiece
@require_flax
class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		@slow
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					_snake_case														=   FlaxOPTModel.from_pretrained("facebook/opt-350m" )
					_snake_case														=   jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
					_snake_case														=   model(input_ids=__SCREAMING_SNAKE_CASE ).last_hidden_state
					_snake_case														=   (1, 11, 512)
					self.assertEqual(output.shape	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   jnp.array(
					    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]] )
					self.assertTrue(jnp.allclose(output[:, :3, :3]	,       __SCREAMING_SNAKE_CASE	,       atol=4e-2 ) )

@require_flax
@slow
class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					super().setUp()
					_snake_case														=   "facebook/opt-350m"







		def 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   FlaxOPTForCausalLM.from_pretrained(self.path_model )
					_snake_case														=   GPTaTokenizer.from_pretrained(self.path_model )

					_snake_case														=   [
					    "Today is a beautiful day and I want to",
					    "In the city of",
					    "Paris is the capital of France and",
					    "Computers and mobile phones have taken",
					]
					# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
					_snake_case														=   tokenizer(__SCREAMING_SNAKE_CASE	,       return_tensors="jax"	,       padding=__SCREAMING_SNAKE_CASE	,       add_special_tokens=__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(inputs.input_ids	,       attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
					_snake_case														=   jnp.array(
					    [
					        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
					        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
					        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
					        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
					    ] )
					self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       atol=4e-2 ) )

					_snake_case														=   jax.jit(__SCREAMING_SNAKE_CASE )
					_snake_case														=   model(inputs.input_ids	,       attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
					self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       atol=4e-2 ) )

@require_flax
@slow
class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		@property
		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					return [
					    "Today is a beautiful day and I want",
					    "In the city of",
					    "Paris is the capital of France and",
					    "Computers and mobile phones have taken",
					]



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   "facebook/opt-125m"

					_snake_case														=   [
					    "Today is a beautiful day and I want to",
					    "In the city of New York, the city",
					    "Paris is the capital of France and the capital",
					    "Computers and mobile phones have taken over the",
					]

					_snake_case														=   []

					_snake_case														=   FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )

					for prompt in self.prompts:
								_snake_case														=   tokenizer(__SCREAMING_SNAKE_CASE	,       return_tensors="jax" ).input_ids

								_snake_case														=   model.generate(__SCREAMING_SNAKE_CASE	,       max_length=10 )
								_snake_case														=   generated_ids[0]

								_snake_case														=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )
								predicted_outputs += generated_string

					self.assertListEqual(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   "facebook/opt-350m"

					_snake_case														=   [
					    "Today is a beautiful day and I want to",
					    "In the city of San Francisco, the city",
					    "Paris is the capital of France and the capital",
					    "Computers and mobile phones have taken over the",
					]

					_snake_case														=   []
					_snake_case														=   FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )

					for prompt in self.prompts:
								_snake_case														=   tokenizer(__SCREAMING_SNAKE_CASE	,       return_tensors="jax" ).input_ids

								_snake_case														=   model.generate(__SCREAMING_SNAKE_CASE	,       max_length=10 )
								_snake_case														=   generated_ids[0]

								_snake_case														=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )
								predicted_outputs += generated_string

					self.assertListEqual(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					int ):






					"""simple docstring"""
					_snake_case														=   "facebook/opt-125m"
					_snake_case														=   [
					    "Today is a beautiful day and I want to thank",
					    "In the city of Rome Canaver Canaver Canaver Canaver",
					]
					_snake_case														=   FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   tokenizer(
					    [
					        "Today is a beautiful day and I want to",
					        "In the city of",
					    ]	,       return_tensors="jax"	,       padding=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   jax.jit(model.generate )

					_snake_case														=   jit_generate(inputs["input_ids"]	,       attention_mask=inputs["attention_mask"] ).sequences

					_snake_case														=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )

					self.assertIsNotNone(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   "facebook/opt-350m"

					_snake_case														=   GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
					_snake_case														=   FlaxOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )

					_snake_case														=   "left"

					# use different length sentences to test batching
					_snake_case														=   [
					    "Hello, my dog is a little",
					    "Today, I",
					]

					_snake_case														=   tokenizer(__SCREAMING_SNAKE_CASE	,       return_tensors="jax"	,       padding=__SCREAMING_SNAKE_CASE )
					_snake_case														=   inputs["input_ids"]

					_snake_case														=   model.generate(input_ids=__SCREAMING_SNAKE_CASE	,       attention_mask=inputs["attention_mask"]	,       trace=__SCREAMING_SNAKE_CASE )

					_snake_case														=   tokenizer(sentences[0]	,       return_tensors="jax" ).input_ids
					_snake_case														=   model.generate(input_ids=__SCREAMING_SNAKE_CASE )

					_snake_case														=   inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
					_snake_case														=   tokenizer(sentences[1]	,       return_tensors="jax" ).input_ids
					_snake_case														=   model.generate(input_ids=__SCREAMING_SNAKE_CASE	,       max_length=model.config.max_length - num_paddings )

					_snake_case														=   tokenizer.batch_decode(outputs[0]	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )
					_snake_case														=   tokenizer.decode(output_non_padded[0][0]	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )
					_snake_case														=   tokenizer.decode(output_padded[0][0]	,       skip_special_tokens=__SCREAMING_SNAKE_CASE )

					_snake_case														=   [
					    "Hello, my dog is a little bit of a dork.\nI'm a little bit",
					    "Today, I was in the middle of a conversation with a friend about the",
					]
					self.assertListEqual(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					self.assertListEqual(__SCREAMING_SNAKE_CASE	,       [non_padded_sentence, padded_sentence] )


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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. 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 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   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:
								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." )

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

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

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

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

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

