






from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel


@require_tf
class        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     UpperCamelCase_							:						Optional[int]								=		MarianConfig
     UpperCamelCase_							:						int								=		{}
     UpperCamelCase_							:						Any								=		'''gelu'''


     def __init__( self				:		List[Any]    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Dict=13    ,       UpperCAmelCase_				:		List[Any]=7    ,       UpperCAmelCase_				:		Dict=True    ,       UpperCAmelCase_				:		List[Any]=False    ,       UpperCAmelCase_				:		List[Any]=99    ,       UpperCAmelCase_				:		List[Any]=32    ,       UpperCAmelCase_				:		str=2    ,       UpperCAmelCase_				:		str=4    ,       UpperCAmelCase_				:		List[Any]=37    ,       UpperCAmelCase_				:		Dict=0.1    ,       UpperCAmelCase_				:		Any=0.1    ,       UpperCAmelCase_				:		str=20    ,       UpperCAmelCase_				:		Dict=2    ,       UpperCAmelCase_				:		str=1    ,       UpperCAmelCase_				:		List[str]=0    ,       ):
       SCREAMING_SNAKE_CASE				:					Any		  =			parent
       SCREAMING_SNAKE_CASE				:					str		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			seq_length
       SCREAMING_SNAKE_CASE				:					str		  =			is_training
       SCREAMING_SNAKE_CASE				:					str		  =			use_labels
       SCREAMING_SNAKE_CASE				:					Dict		  =			vocab_size
       SCREAMING_SNAKE_CASE				:					str		  =			hidden_size
       SCREAMING_SNAKE_CASE				:					Any		  =			num_hidden_layers
       SCREAMING_SNAKE_CASE				:					int		  =			num_attention_heads
       SCREAMING_SNAKE_CASE				:					Dict		  =			intermediate_size

       SCREAMING_SNAKE_CASE				:					Dict		  =			hidden_dropout_prob
       SCREAMING_SNAKE_CASE				:					Tuple		  =			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE				:					Dict		  =			max_position_embeddings
       SCREAMING_SNAKE_CASE				:					int		  =			eos_token_id
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			pad_token_id
       SCREAMING_SNAKE_CASE				:					int		  =			bos_token_id


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size					)
       SCREAMING_SNAKE_CASE				:					str		  =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)    ,       1					)
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			tf.concat([input_ids, eos_tensor]    ,       axis=1					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.config_cls(
           vocab_size=self.vocab_size    ,       d_model=self.hidden_size    ,       encoder_layers=self.num_hidden_layers    ,       decoder_layers=self.num_hidden_layers    ,       encoder_attention_heads=self.num_attention_heads    ,       decoder_attention_heads=self.num_attention_heads    ,       encoder_ffn_dim=self.intermediate_size    ,       decoder_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_ids=[2]    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       decoder_start_token_id=self.pad_token_id    ,       **self.config_updates    ,       )
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_marian_inputs_dict(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
       return config, inputs_dict






     def  _A				( self				:		int    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		Tuple					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			TFMarianModel(config=UpperCAmelCase_					).get_decoder()
       SCREAMING_SNAKE_CASE				:					Tuple		  =			inputs_dict["input_ids"]

       SCREAMING_SNAKE_CASE				:					int		  =			input_ids[:1, :]
       SCREAMING_SNAKE_CASE				:					int		  =			inputs_dict["attention_mask"][:1, :]
       SCREAMING_SNAKE_CASE				:					List[str]		  =			inputs_dict["head_mask"]
       SCREAMING_SNAKE_CASE				:					Dict		  =			1

       # first forward pass
       SCREAMING_SNAKE_CASE				:					Dict		  =			model(UpperCAmelCase_    ,       attention_mask=UpperCAmelCase_    ,       head_mask=UpperCAmelCase_    ,       use_cache=UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Dict		  =			outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE				:					Tuple		  =			ids_tensor((self.batch_size, 3)    ,       config.vocab_size					)
       SCREAMING_SNAKE_CASE				:					str		  =			tf.cast(ids_tensor((self.batch_size, 3)    ,       2					)    ,       tf.inta					)

       # append to next input_ids and
       SCREAMING_SNAKE_CASE				:					str		  =			tf.concat([input_ids, next_tokens]    ,       axis=-1					)
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			tf.concat([attention_mask, next_attn_mask]    ,       axis=-1					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			model(UpperCAmelCase_    ,       attention_mask=UpperCAmelCase_					)[0]
       SCREAMING_SNAKE_CASE				:					Dict		  =			model(UpperCAmelCase_    ,       attention_mask=UpperCAmelCase_    ,       past_key_values=UpperCAmelCase_					)[0]

       self.parent.assertEqual(next_tokens.shape[1]    ,       output_from_past.shape[1]					)

       # select random slice
       SCREAMING_SNAKE_CASE				:					Dict		  =			int(ids_tensor((1,)    ,       output_from_past.shape[-1]					)					)
       SCREAMING_SNAKE_CASE				:					str		  =			output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE				:					List[str]		  =			output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(UpperCAmelCase_    ,       UpperCAmelCase_    ,       rtol=1E-3					)





def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase=None  ,   lowercase=None  ,   lowercase=None  ,   lowercase=None  ,   lowercase=None  ,   ):

  """simple docstring"""






  if attention_mask is None:
    SCREAMING_SNAKE_CASE				:					Dict		  =			tf.cast(tf.math.not_equal(lowercase  ,   config.pad_token_id )  ,   tf.inta )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape  ,   dtype=tf.inta ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,   config.pad_token_id )  ,   tf.inta ),
        ]  ,   axis=-1  ,   )
  if head_mask is None:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE				:					str		  =			tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }


@require_tf
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     UpperCamelCase_							:						Tuple								=		(TFMarianMTModel,) if is_tf_available() else ()
     UpperCamelCase_							:						Tuple								=		(
         {
             '''conversational''': TFMarianMTModel,
             '''feature-extraction''': TFMarianModel,
             '''summarization''': TFMarianMTModel,
             '''text2text-generation''': TFMarianMTModel,
             '''translation''': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase_							:						List[Any]								=		True
     UpperCamelCase_							:						Optional[Any]								=		False
     UpperCamelCase_							:						Union[str, Any]								=		False


     def  _A				( self				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			TFMarianModelTester(self					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			ConfigTester(self    ,       config_class=UpperCAmelCase_					)


     def  _A				( self				:		Union[str, Any]					):
       self.config_tester.run_common_tests()






     def  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_					)





@require_tf
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Optional[Any]								=		1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def  _A				( cls				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					int		  =			f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
       return cls


     @cached_property
     def  _A				( self				:		str					):
       return AutoTokenizer.from_pretrained(self.model_name					)


     @property
     def  _A				( self				:		Optional[Any]					):
       return self.tokenizer.eos_token_id


     @cached_property
     def  _A				( self				:		int					):
       warnings.simplefilter("error"					)
       SCREAMING_SNAKE_CASE				:					TFMarianMTModel		  =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       assert isinstance(UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					str		  =			model.config
       self.assertListEqual(c.bad_words_ids    ,       [[c.pad_token_id]]					)
       self.assertEqual(c.max_length    ,       512					)
       self.assertEqual(c.decoder_start_token_id    ,       c.pad_token_id					)
       return model


     def  _A				( self				:		List[Any]    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					int		  =			self.translate_src_text(**UpperCAmelCase_					)
       self.assertListEqual(self.expected_text    ,       UpperCAmelCase_					)






     def  _A				( self				:		Dict    ,       **UpperCAmelCase_				:		str					):
       SCREAMING_SNAKE_CASE				:					Any		  =			self.tokenizer(self.src_text    ,       **UpperCAmelCase_    ,       padding=UpperCAmelCase_    ,       return_tensors="tf"					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			self.model.generate(
           model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2    ,       max_length=128					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.tokenizer.batch_decode(generated_ids.numpy()    ,       skip_special_tokens=UpperCAmelCase_					)
       return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		'''mt'''
     UpperCamelCase_							:						List[str]								=		'''en'''
     UpperCamelCase_							:						Any								=		['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
     UpperCamelCase_							:						str								=		['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']


     @unittest.skip("Skipping until #12647 is resolved."					)
     @slow
     def  _A				( self				:		int					):
       self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Dict								=		'''en'''
     UpperCamelCase_							:						str								=		'''zh'''
     UpperCamelCase_							:						int								=		['''My name is Wolfgang and I live in Berlin''']
     UpperCamelCase_							:						List[str]								=		['''我叫沃尔夫冈 我住在柏林''']


     @unittest.skip("Skipping until #12647 is resolved."					)
     @slow
     def  _A				( self				:		Any					):
       self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						int								=		'''en'''
     UpperCamelCase_							:						Any								=		'''ROMANCE'''
     UpperCamelCase_							:						Union[str, Any]								=		[
         '''>>fr<< Don\'t spend so much time watching TV.''',
         '''>>pt<< Your message has been sent.''',
         '''>>es<< He\'s two years older than me.''',
     ]
     UpperCamelCase_							:						Optional[Any]								=		[
         '''Ne passez pas autant de temps à regarder la télé.''',
         '''A sua mensagem foi enviada.''',
         '''Es dos años más viejo que yo.''',
     ]


     @unittest.skip("Skipping until #12647 is resolved."					)
     @slow
     def  _A				( self				:		Optional[Any]					):
       self._assert_generated_batch_equal_expected()






     @unittest.skip("Skipping until #12647 is resolved."					)
     @slow
     def  _A				( self				:		Dict					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			TranslationPipeline(self.model    ,       self.tokenizer    ,       framework="tf"					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			pipeline(self.src_text					)
       self.assertEqual(self.expected_text    ,       [x["translation_text"] for x in output]					)














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			 =						logging.get_logger(__name__)


if is_vision_available():
   import PIL


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						List[Any]								=		['''pixel_values''']


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

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size if size is not None else {"height": 2560, "width": 1920}
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # The previous feature extractor size parameter was in (width, height) format
         SCREAMING_SNAKE_CASE				:					List[str]		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_size_dict(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					int		  =			size
       SCREAMING_SNAKE_CASE				:					str		  =			resample
       SCREAMING_SNAKE_CASE				:					List[str]		  =			do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			do_pad
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_rescale
       SCREAMING_SNAKE_CASE				:					Tuple		  =			rescale_factor
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE				:					Any		  =			image_std if image_std is not None else IMAGENET_STANDARD_STD


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					str		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			size["height"], size["width"]

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			np.rotaa(UpperCAmelCase_    ,       3					)

       if data_format is not None:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					)

       return image


     def  _A				( self				:		int    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[str]		  =			size["height"], size["width"]
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			get_image_size(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_width - input_width
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			output_height - input_height

       if random_padding:
         SCREAMING_SNAKE_CASE				:					int		  =			np.random.randint(low=0    ,       high=delta_height + 1					)
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			np.random.randint(low=0    ,       high=delta_width + 1					)
       else:
         SCREAMING_SNAKE_CASE				:					List[str]		  =			delta_height // 2
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delta_width // 2

       SCREAMING_SNAKE_CASE				:					Any		  =			delta_height - pad_top
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			delta_width - pad_left

       SCREAMING_SNAKE_CASE				:					Dict		  =			((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(UpperCAmelCase_    ,       UpperCAmelCase_    ,       data_format=UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]    ,       *UpperCAmelCase_				:		int    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
       return self.pad_image(*UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		int    ,       ):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			get_image_size(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					int		  =			size["height"], size["width"]

       # We always resize to the smallest of either the input or output size.
       SCREAMING_SNAKE_CASE				:					Any		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			min(UpperCAmelCase_    ,       UpperCAmelCase_					)

       if height == input_height and width == input_width:
         return image

       if input_height > input_width:
         SCREAMING_SNAKE_CASE				:					Tuple		  =			int(input_width * height / input_height					)
       elif input_width > input_height:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(input_height * width / input_width					)

       return resize(
           UpperCAmelCase_    ,       size=(height, width)    ,       resample=UpperCAmelCase_    ,       reducing_gap=2.0    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Dict[str, int]    ,       UpperCAmelCase_				:		PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		Optional[int]    ,       ):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			min(size["height"]    ,       size["width"]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_resize_output_image_size(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       default_to_square=UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			resize(UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)
       return resized_image


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[int, float]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		str    ,       ):
       return rescale(UpperCAmelCase_    ,       scale=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)


     def  _A				( self				:		Tuple    ,       UpperCAmelCase_				:		np.ndarray    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Union[float, List[float]]    ,       UpperCAmelCase_				:		Optional[Union[str, ChannelDimension]] = None    ,       **UpperCAmelCase_				:		List[Any]    ,       ):
       return normalize(UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_					)






     def  _A				( self				:		List[Any]    ,       UpperCAmelCase_				:		ImageInput    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Dict[str, int] = None    ,       UpperCAmelCase_				:		PILImageResampling = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		bool = False    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		float = None    ,       UpperCAmelCase_				:		bool = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[float, List[float]]] = None    ,       UpperCAmelCase_				:		Optional[Union[str, TensorType]] = None    ,       UpperCAmelCase_				:		Optional[ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCAmelCase_				:		Union[str, Any]    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE				:					str		  =			size if size is not None else self.size
       if isinstance(UpperCAmelCase_    ,       (tuple, list)					):
         # Previous feature extractor had size in (width, height) format
         SCREAMING_SNAKE_CASE				:					str		  =			size[::-1]
       SCREAMING_SNAKE_CASE				:					Tuple		  =			get_size_dict(UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       SCREAMING_SNAKE_CASE				:					int		  =			do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE				:					str		  =			do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE				:					int		  =			rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE				:					Tuple		  =			do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE				:					Dict		  =			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_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.
       SCREAMING_SNAKE_CASE				:					int		  =			[to_numpy_array(UpperCAmelCase_					) for image in images]

       if do_align_long_axis:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.align_long_axis(UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

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

       if do_thumbnail:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[self.thumbnail(image=UpperCAmelCase_    ,       size=UpperCAmelCase_					) for image in images]

       if do_pad:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[self.pad_image(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       random_padding=UpperCAmelCase_					) for image in images]

       if do_rescale:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[self.rescale(image=UpperCAmelCase_    ,       scale=UpperCAmelCase_					) for image in images]

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

       SCREAMING_SNAKE_CASE				:					str		  =			[to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_					) for image in images]

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






