





"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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__ =       "platform"

       import jax
       import jax.numpy as jnp

       from transformers import MarianTokenizer
       from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						int=None							,						SCREAMING_SNAKE_CASE					:						int=None							,						SCREAMING_SNAKE_CASE					:						Tuple=None							,						SCREAMING_SNAKE_CASE					:						str=None							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						):







      '''simple docstring'''




      if attention_mask is None:
            lowerCAmelCase  						=	np.where(input_ids != config.pad_token_id							,						1							,						0      )
      if decoder_attention_mask is None:
            lowerCAmelCase  						=	np.where(decoder_input_ids != config.pad_token_id							,						1							,						0      )
      if head_mask is None:
            lowerCAmelCase  						=	np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            lowerCAmelCase  						=	np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            lowerCAmelCase  						=	np.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": attention_mask,
      }

class 		lowercase :





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=7				,						lowercase=True				,						lowercase=False				,						lowercase=99				,						lowercase=16				,						lowercase=2				,						lowercase=4				,						lowercase=4				,						lowercase="gelu"				,						lowercase=0.1				,						lowercase=0.1				,						lowercase=32				,						lowercase=2				,						lowercase=1				,						lowercase=0				,						lowercase=0.02				,						)		->					str:
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	seq_length
            lowerCAmelCase  						=	is_training
            lowerCAmelCase  						=	use_labels
            lowerCAmelCase  						=	vocab_size
            lowerCAmelCase  						=	hidden_size
            lowerCAmelCase  						=	num_hidden_layers
            lowerCAmelCase  						=	num_attention_heads
            lowerCAmelCase  						=	intermediate_size
            lowerCAmelCase  						=	hidden_act
            lowerCAmelCase  						=	hidden_dropout_prob
            lowerCAmelCase  						=	attention_probs_dropout_prob
            lowerCAmelCase  						=	max_position_embeddings
            lowerCAmelCase  						=	eos_token_id
            lowerCAmelCase  						=	pad_token_id
            lowerCAmelCase  						=	bos_token_id
            lowerCAmelCase  						=	initializer_range





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size			)				,						3				,						self.vocab_size			)
            lowerCAmelCase  						=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,						dtype=np.intaa			))				,						-1			)

            lowerCAmelCase  						=	shift_tokens_right(lowercase				,						1				,						2			)

            lowerCAmelCase  						=	MarianConfig(
                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_id=self.eos_token_id				,						bos_token_id=self.bos_token_id				,						pad_token_id=self.pad_token_id				,						initializer_range=self.initializer_range				,						use_cache=lowercase				,						)
            lowerCAmelCase  						=	prepare_marian_inputs_dict(lowercase				,						lowercase				,						lowercase			)
            return config, inputs_dict





      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.prepare_config_and_inputs()
            return config, inputs_dict





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase			)		->					str:
            lowerCAmelCase  						=	20
            lowerCAmelCase  						=	model_class_name(lowercase			)

            lowerCAmelCase  						=	model.encode(inputs_dict["""input_ids"""]			)

            lowerCAmelCase						,		lowerCAmelCase  						=	(
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            lowerCAmelCase  						=	model.init_cache(decoder_input_ids.shape[0]				,						lowercase				,						lowercase			)
            lowerCAmelCase  						=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,						dtype="""i4"""			)

            lowerCAmelCase  						=	jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)
            lowerCAmelCase  						=	model.decode(
                decoder_input_ids[:, :-1]				,						lowercase				,						decoder_attention_mask=lowercase				,						past_key_values=lowercase				,						decoder_position_ids=lowercase				,						)

            lowerCAmelCase  						=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype="""i4"""			)
            lowerCAmelCase  						=	model.decode(
                decoder_input_ids[:, -1:]				,						lowercase				,						decoder_attention_mask=lowercase				,						past_key_values=outputs_cache.past_key_values				,						decoder_position_ids=lowercase				,						)

            lowerCAmelCase  						=	model.decode(lowercase				,						lowercase			)

            lowerCAmelCase  						=	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    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	20
            lowerCAmelCase  						=	model_class_name(lowercase			)

            lowerCAmelCase  						=	model.encode(inputs_dict["""input_ids"""]			)

            lowerCAmelCase						,		lowerCAmelCase  						=	(
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            lowerCAmelCase  						=	jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
                ]				,						axis=-1				,						)

            lowerCAmelCase  						=	model.init_cache(decoder_input_ids.shape[0]				,						lowercase				,						lowercase			)
            lowerCAmelCase  						=	jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]				,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,						)

            lowerCAmelCase  						=	model.decode(
                decoder_input_ids[:, :-1]				,						lowercase				,						decoder_attention_mask=lowercase				,						past_key_values=lowercase				,						decoder_position_ids=lowercase				,						)
            lowerCAmelCase  						=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,						dtype="""i4"""			)
            lowerCAmelCase  						=	model.decode(
                decoder_input_ids[:, -1:]				,						lowercase				,						past_key_values=outputs_cache.past_key_values				,						decoder_attention_mask=lowercase				,						decoder_position_ids=lowercase				,						)

            lowerCAmelCase  						=	model.decode(lowercase				,						lowercase				,						decoder_attention_mask=lowercase			)

            lowerCAmelCase  						=	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 		lowercase ( _UpperCAmelCase					,  unittest.TestCase					,  _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				True
      _SCREAMING_SNAKE_CASE         =				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      _SCREAMING_SNAKE_CASE         =				(FlaxMarianMTModel,) if is_flax_available() else ()





      def    _snake_case      (	self			)		->					Union[str, Any]:
            lowerCAmelCase  						=	FlaxMarianModelTester(self			)





      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(lowercase				,						lowercase				,						lowercase			)





      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=	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(lowercase				,						lowercase				,						lowercase			)





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__			):
                        lowerCAmelCase  						=	self._prepare_for_class(lowercase				,						lowercase			)
                        lowerCAmelCase  						=	model_class(lowercase			)

                        @jax.jit
                        def encode_jitted(lowercase				,						lowercase=None				,						**lowercase			):
                              return model.encode(input_ids=lowercase				,						attention_mask=lowercase			)

                        with self.subTest("""JIT Enabled"""			):
                              lowerCAmelCase  						=	encode_jitted(**lowercase			).to_tuple()

                        with self.subTest("""JIT Disabled"""			):
                              with jax.disable_jit():
                                    lowerCAmelCase  						=	encode_jitted(**lowercase			).to_tuple()

                        self.assertEqual(len(lowercase			)				,						len(lowercase			)			)
                        for jitted_output, output in zip(lowercase				,						lowercase			):
                              self.assertEqual(jitted_output.shape				,						output.shape			)





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__			):
                        lowerCAmelCase  						=	model_class(lowercase			)
                        lowerCAmelCase  						=	model.encode(inputs_dict["""input_ids"""]				,						inputs_dict["""attention_mask"""]			)

                        lowerCAmelCase  						=	{
                            """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                            """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                            """encoder_outputs""": encoder_outputs,
                        }

                        @jax.jit
                        def decode_jitted(lowercase				,						lowercase				,						lowercase			):
                              return model.decode(
                                  decoder_input_ids=lowercase				,						decoder_attention_mask=lowercase				,						encoder_outputs=lowercase				,						)

                        with self.subTest("""JIT Enabled"""			):
                              lowerCAmelCase  						=	decode_jitted(**lowercase			).to_tuple()

                        with self.subTest("""JIT Disabled"""			):
                              with jax.disable_jit():
                                    lowerCAmelCase  						=	decode_jitted(**lowercase			).to_tuple()

                        self.assertEqual(len(lowercase			)				,						len(lowercase			)			)
                        for jitted_output, output in zip(lowercase				,						lowercase			):
                              self.assertEqual(jitted_output.shape				,						output.shape			)





      @slow
      def    _snake_case      (	self			)		->					List[Any]:
            for model_class_name in self.all_model_classes:
                  lowerCAmelCase  						=	model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""			)
                  # FlaxMarianForSequenceClassification expects eos token in input_ids
                  lowerCAmelCase  						=	np.ones((1, 1)			) * model.config.eos_token_id
                  lowerCAmelCase  						=	model(lowercase			)
                  self.assertIsNotNone(lowercase			)







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				None
      _SCREAMING_SNAKE_CASE         =				None





      @classmethod
      def    _snake_case      (	cls			)		->					None:
            lowerCAmelCase  						=	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls





      @cached_property
      def    _snake_case      (	self			)		->					Tuple:
            return MarianTokenizer.from_pretrained(self.model_name			)





      @property
      def    _snake_case      (	self			)		->					int:
            return self.tokenizer.eos_token_id





      @cached_property
      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	FlaxMarianMTModel.from_pretrained(self.model_name			)
            self.assertEqual(model.config.decoder_start_token_id				,						model.config.pad_token_id			)
            return model





      def    _snake_case      (	self				,						**lowercase			)		->					Any:
            lowerCAmelCase  						=	self.translate_src_text(**lowercase			)
            self.assertListEqual(self.expected_text				,						lowercase			)





      def    _snake_case      (	self				,						**lowercase			)		->					str:
            lowerCAmelCase  						=	self.tokenizer(self.src_text				,						padding=lowercase				,						return_tensors="""np"""				,						**lowercase			)
            lowerCAmelCase  						=	self.model.generate(
                model_inputs.input_ids				,						attention_mask=model_inputs.attention_mask				,						num_beams=2				,						max_length=128				,						).sequences
            lowerCAmelCase  						=	self.tokenizer.batch_decode(lowercase				,						skip_special_tokens=lowercase			)
            return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'fr'
      _SCREAMING_SNAKE_CASE         =				[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          'Je suis une petite grenouille.',
          'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
      ]





      @slow
      def    _snake_case      (	self			)		->					Optional[Any]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'fr'
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				[
          'Donnez moi le micro.',
          'Tom et Mary étaient assis à une table.',  # Accents
      ]
      _SCREAMING_SNAKE_CASE         =				[
          'Give me the microphone.',
          'Tom and Mary were sitting at a table.',
      ]





      @slow
      def    _snake_case      (	self			)		->					str:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'mt'
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      _SCREAMING_SNAKE_CASE         =				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





      @slow
      def    _snake_case      (	self			)		->					str:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'de'
      _SCREAMING_SNAKE_CASE         =				[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
          'Tom asked his teacher for advice.',
          'That\'s how I would do it.',
          'Tom really admired Mary\'s courage.',
          'Turn around and close your eyes.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          'Ich bin ein kleiner Frosch.',
          'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
          'Tom bat seinen Lehrer um Rat.',
          'So würde ich das machen.',
          'Tom bewunderte Marias Mut wirklich.',
          'Drehen Sie sich um und schließen Sie die Augen.',
      ]





      @slow
      def    _snake_case      (	self			)		->					Optional[int]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'zh'
      _SCREAMING_SNAKE_CASE         =				['My name is Wolfgang and I live in Berlin']
      _SCREAMING_SNAKE_CASE         =				['我叫沃尔夫冈 我住在柏林']





      @slow
      def    _snake_case      (	self			)		->					List[str]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'ru'
      _SCREAMING_SNAKE_CASE         =				'fr'
      _SCREAMING_SNAKE_CASE         =				['Он показал мне рукопись своей новой пьесы.']
      _SCREAMING_SNAKE_CASE         =				['Il m\'a montré le manuscrit de sa nouvelle pièce.']





      @slow
      def    _snake_case      (	self			)		->					Optional[int]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'ROMANCE'
      _SCREAMING_SNAKE_CASE         =				[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]





      @slow
      def    _snake_case      (	self			)		->					Tuple:
            self._assert_generated_batch_equal_expected()













"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor

class 		lowercase ( unittest.TestCase ):





      def __init__(	self				,						lowercase				,						lowercase=7				,						lowercase=3				,						lowercase=18				,						lowercase=30				,						lowercase=400				,						lowercase=True				,						lowercase=None				,						lowercase=0.875				,						lowercase=True				,						lowercase=[0.5, 0.5, 0.5]				,						lowercase=[0.5, 0.5, 0.5]				,						)		->					List[Any]:
            lowerCAmelCase  						=	size if size is not None else {"""shortest_edge""": 20}
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	min_resolution
            lowerCAmelCase  						=	max_resolution
            lowerCAmelCase  						=	do_resize
            lowerCAmelCase  						=	size
            lowerCAmelCase  						=	crop_pct
            lowerCAmelCase  						=	do_normalize
            lowerCAmelCase  						=	image_mean
            lowerCAmelCase  						=	image_std





      def    _snake_case      (	self			)		->					Optional[Any]:
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "crop_pct": self.crop_pct,
            }







@require_torch
@require_vision
class 		lowercase ( _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				ConvNextImageProcessor if is_vision_available() else None





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ConvNextImageProcessingTester(self			)





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            self.assertTrue(hasattr(lowercase				,						"""do_resize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""size"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""crop_pct"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_normalize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_mean"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_std"""			)			)





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 20}			)

            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict				,						size=42			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 42}			)





      def    _snake_case      (	self			)		->					Optional[int]:
            pass





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PIL images
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						Image.Image			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					str:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random numpy tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						numpify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						np.ndarray			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PyTorch tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						torchify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						torch.Tensor			)

            # Test not batched input
            lowerCAmelCase  						=	image_processing(image_inputs[0]				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						return_tensors="""pt"""			).pixel_values
            self.assertEqual(
                encoded_images.shape				,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )				,						)






