
'''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
							__lowercase	=    '''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 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ,					):
		if attention_mask is None:
				lowerCAmelCase_   :       str    							=np.where(input_ids != config.pad_token_id  ,					1  ,					0  )
		if decoder_attention_mask is None:
				lowerCAmelCase_   :       str    							=np.where(decoder_input_ids != config.pad_token_id  ,					1  ,					0  )
		if head_mask is None:
				lowerCAmelCase_   :       Tuple    							=np.ones((config.encoder_layers, config.encoder_attention_heads)  )
		if decoder_head_mask is None:
				lowerCAmelCase_   :       Union[str, Any]    							=np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		if cross_attn_head_mask is None:
				lowerCAmelCase_   :       str    							=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 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Union[str, Any]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Optional[Any]=13					,						UpperCamelCase_				: Tuple=7					,						UpperCamelCase_				: List[Any]=True					,						UpperCamelCase_				: Optional[int]=False					,						UpperCamelCase_				: Optional[int]=99					,						UpperCamelCase_				: List[Any]=16					,						UpperCamelCase_				: List[Any]=2					,						UpperCamelCase_				: Optional[int]=4					,						UpperCamelCase_				: Any=4					,						UpperCamelCase_				: Dict="gelu"					,						UpperCamelCase_				: int=0.1					,						UpperCamelCase_				: Any=0.1					,						UpperCamelCase_				: Any=32					,						UpperCamelCase_				: int=2					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: str=0					,						UpperCamelCase_				: Union[str, Any]=0.0_2					,						):
			lowerCAmelCase_   :       Optional[Any]    							=parent
			lowerCAmelCase_   :       Any    							=batch_size
			lowerCAmelCase_   :       Tuple    							=seq_length
			lowerCAmelCase_   :       List[Any]    							=is_training
			lowerCAmelCase_   :       Any    							=use_labels
			lowerCAmelCase_   :       int    							=vocab_size
			lowerCAmelCase_   :       Tuple    							=hidden_size
			lowerCAmelCase_   :       Optional[Any]    							=num_hidden_layers
			lowerCAmelCase_   :       str    							=num_attention_heads
			lowerCAmelCase_   :       Optional[Any]    							=intermediate_size
			lowerCAmelCase_   :       int    							=hidden_act
			lowerCAmelCase_   :       List[Any]    							=hidden_dropout_prob
			lowerCAmelCase_   :       Optional[Any]    							=attention_probs_dropout_prob
			lowerCAmelCase_   :       int    							=max_position_embeddings
			lowerCAmelCase_   :       str    							=eos_token_id
			lowerCAmelCase_   :       List[Any]    							=pad_token_id
			lowerCAmelCase_   :       Any    							=bos_token_id
			lowerCAmelCase_   :       Dict    							=initializer_range




	def 	__A      (						self				: Any      ):
			lowerCAmelCase_   :       Optional[int]    							=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,						self.vocab_size      )					,						3					,						self.vocab_size      )
			lowerCAmelCase_   :       str    							=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,						dtype=np.intaa      ))					,						-1      )

			lowerCAmelCase_   :       str    							=shift_tokens_right(UpperCamelCase_					,						1					,						2      )

			lowerCAmelCase_   :       str    							=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=UpperCamelCase_					,						)
			lowerCAmelCase_   :       List[Any]    							=prepare_marian_inputs_dict(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )
			return config, inputs_dict




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=self.prepare_config_and_inputs()
			return config, inputs_dict




	def 	__A      (						self				: Any					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: List[str]      ):
			lowerCAmelCase_   :       Optional[Any]    							=20
			lowerCAmelCase_   :       str    							=model_class_name(UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=model.encode(inputs_dict['''input_ids''']      )

			lowerCAmelCase_							,		lowerCAmelCase_   :       Dict    							=(
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

			lowerCAmelCase_   :       str    							=model.init_cache(decoder_input_ids.shape[0]					,						UpperCamelCase_					,						UpperCamelCase_      )
			lowerCAmelCase_   :       Dict    							=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,						dtype='''i4'''      )

			lowerCAmelCase_   :       str    							=jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)
			lowerCAmelCase_   :       Any    							=model.decode(
			    decoder_input_ids[:, :-1]					,						UpperCamelCase_					,						decoder_attention_mask=UpperCamelCase_					,						past_key_values=UpperCamelCase_					,						decoder_position_ids=UpperCamelCase_					,						)

			lowerCAmelCase_   :       str    							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype='''i4'''      )
			lowerCAmelCase_   :       List[str]    							=model.decode(
			    decoder_input_ids[:, -1:]					,						UpperCamelCase_					,						decoder_attention_mask=UpperCamelCase_					,						past_key_values=outputs_cache.past_key_values					,						decoder_position_ids=UpperCamelCase_					,						)

			lowerCAmelCase_   :       List[Any]    							=model.decode(UpperCamelCase_					,						UpperCamelCase_      )

			lowerCAmelCase_   :       List[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 	__A      (						self				: Tuple					,						UpperCamelCase_				: int					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Optional[Any]      ):
			lowerCAmelCase_   :       List[str]    							=20
			lowerCAmelCase_   :       Tuple    							=model_class_name(UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							=model.encode(inputs_dict['''input_ids''']      )

			lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[Any]    							=(
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

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

			lowerCAmelCase_   :       List[str]    							=model.init_cache(decoder_input_ids.shape[0]					,						UpperCamelCase_					,						UpperCamelCase_      )
			lowerCAmelCase_   :       int    							=jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]					,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,						)

			lowerCAmelCase_   :       List[str]    							=model.decode(
			    decoder_input_ids[:, :-1]					,						UpperCamelCase_					,						decoder_attention_mask=UpperCamelCase_					,						past_key_values=UpperCamelCase_					,						decoder_position_ids=UpperCamelCase_					,						)
			lowerCAmelCase_   :       int    							=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype='''i4'''      )
			lowerCAmelCase_   :       List[Any]    							=model.decode(
			    decoder_input_ids[:, -1:]					,						UpperCamelCase_					,						past_key_values=outputs_cache.past_key_values					,						decoder_attention_mask=UpperCamelCase_					,						decoder_position_ids=UpperCamelCase_					,						)

			lowerCAmelCase_   :       Optional[int]    							=model.decode(UpperCamelCase_					,						UpperCamelCase_					,						decoder_attention_mask=UpperCamelCase_      )

			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}'      )







@require_flax
class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase						,    lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		True
	_UpperCamelCase							:  str								=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	_UpperCamelCase							:  int								=		(FlaxMarianMTModel,) if is_flax_available() else ()




	def 	__A      (						self				: Any      ):
			lowerCAmelCase_   :       Union[str, Any]    							=FlaxMarianModelTester(self      )




	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[Any]    							=self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )




	def 	__A      (						self				: Any      ):
			lowerCAmelCase_							,		lowerCAmelCase_   :       Union[str, Any]    							=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(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_      )




	def 	__A      (						self				: Any      ):
			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__      ):
							lowerCAmelCase_   :       List[str]    							=self._prepare_for_class(UpperCamelCase_					,						UpperCamelCase_      )
							lowerCAmelCase_   :       Union[str, Any]    							=model_class(UpperCamelCase_      )

							@jax.jit
							def encode_jitted(UpperCamelCase_				: Tuple					,						UpperCamelCase_				: str=None					,						**UpperCamelCase_				: List[Any]      ):
									return model.encode(input_ids=UpperCamelCase_					,						attention_mask=UpperCamelCase_      )

							with self.subTest('''JIT Enabled'''      ):
									lowerCAmelCase_   :       Optional[int]    							=encode_jitted(**UpperCamelCase_      ).to_tuple()

							with self.subTest('''JIT Disabled'''      ):
									with jax.disable_jit():
											lowerCAmelCase_   :       Dict    							=encode_jitted(**UpperCamelCase_      ).to_tuple()

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




	def 	__A      (						self				: int      ):
			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__      ):
							lowerCAmelCase_   :       Optional[Any]    							=model_class(UpperCamelCase_      )
							lowerCAmelCase_   :       Tuple    							=model.encode(inputs_dict['''input_ids''']					,						inputs_dict['''attention_mask''']      )

							lowerCAmelCase_   :       Tuple    							={
							    '''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(UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Dict      ):
									return model.decode(
									    decoder_input_ids=UpperCamelCase_					,						decoder_attention_mask=UpperCamelCase_					,						encoder_outputs=UpperCamelCase_					,						)

							with self.subTest('''JIT Enabled'''      ):
									lowerCAmelCase_   :       List[Any]    							=decode_jitted(**UpperCamelCase_      ).to_tuple()

							with self.subTest('''JIT Disabled'''      ):
									with jax.disable_jit():
											lowerCAmelCase_   :       Union[str, Any]    							=decode_jitted(**UpperCamelCase_      ).to_tuple()

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





	@slow
	def 	__A      (						self				: Any      ):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase_   :       int    							=model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''      )
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					lowerCAmelCase_   :       Union[str, Any]    							=np.ones((1, 1)      ) * model.config.eos_token_id
					lowerCAmelCase_   :       Optional[int]    							=model(UpperCamelCase_      )
					self.assertIsNotNone(UpperCamelCase_      )







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Optional[Any]								=		None
	_UpperCamelCase							:  Any								=		None




	@classmethod
	def 	__A      (						cls				: List[str]      ):
			lowerCAmelCase_   :       Tuple    							=F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
			return cls




	@cached_property
	def 	__A      (						self				: List[Any]      ):
			return MarianTokenizer.from_pretrained(self.model_name      )




	@property
	def 	__A      (						self				: Any      ):
			return self.tokenizer.eos_token_id




	@cached_property
	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       FlaxMarianMTModel    							=FlaxMarianMTModel.from_pretrained(self.model_name      )
			self.assertEqual(model.config.decoder_start_token_id					,						model.config.pad_token_id      )
			return model




	def 	__A      (						self				: Tuple					,						**UpperCamelCase_				: List[Any]      ):
			lowerCAmelCase_   :       str    							=self.translate_src_text(**UpperCamelCase_      )
			self.assertListEqual(self.expected_text					,						UpperCamelCase_      )





	def 	__A      (						self				: Optional[int]					,						**UpperCamelCase_				: str      ):
			lowerCAmelCase_   :       Optional[int]    							=self.tokenizer(self.src_text					,						padding=UpperCamelCase_					,						return_tensors='''np'''					,						**UpperCamelCase_      )
			lowerCAmelCase_   :       List[str]    							=self.model.generate(
			    model_inputs.input_ids					,						attention_mask=model_inputs.attention_mask					,						num_beams=2					,						max_length=128					,						).sequences
			lowerCAmelCase_   :       Union[str, Any]    							=self.tokenizer.batch_decode(UpperCamelCase_					,						skip_special_tokens=UpperCamelCase_      )
			return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''en'''
	_UpperCamelCase							:  Union[str, Any]								=		'''fr'''
	_UpperCamelCase							:  Optional[int]								=		[
	    '''I am a small frog.''',
	    '''Now I can forget the 100 words of german that I know.''',
	]
	_UpperCamelCase							:  Dict								=		[
	    '''Je suis une petite grenouille.''',
	    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
	]




	@slow
	def 	__A      (						self				: str      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  str								=		'''fr'''
	_UpperCamelCase							:  Union[str, Any]								=		'''en'''
	_UpperCamelCase							:  Any								=		[
	    '''Donnez moi le micro.''',
	    '''Tom et Mary étaient assis à une table.''',  # Accents
	]
	_UpperCamelCase							:  Optional[Any]								=		[
	    '''Give me the microphone.''',
	    '''Tom and Mary were sitting at a table.''',
	]




	@slow
	def 	__A      (						self				: Optional[Any]      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





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




	@slow
	def 	__A      (						self				: str      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Union[str, Any]								=		'''en'''
	_UpperCamelCase							:  Union[str, Any]								=		'''de'''
	_UpperCamelCase							:  List[Any]								=		[
	    '''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.''',
	]
	_UpperCamelCase							:  Optional[Any]								=		[
	    '''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 	__A      (						self				: Any      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





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




	@slow
	def 	__A      (						self				: List[str]      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Tuple								=		'''ru'''
	_UpperCamelCase							:  int								=		'''fr'''
	_UpperCamelCase							:  Any								=		['''Он показал мне рукопись своей новой пьесы.''']
	_UpperCamelCase							:  str								=		['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




	@slow
	def 	__A      (						self				: List[Any]      ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





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




	@slow
	def 	__A      (						self				: Any      ):
			self._assert_generated_batch_equal_expected()








'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor

class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def __init__(						self				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Any=7					,						UpperCamelCase_				: Optional[Any]=3					,						UpperCamelCase_				: Optional[Any]=30					,						UpperCamelCase_				: List[Any]=400					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: List[str]=None					,						UpperCamelCase_				: str=True					,						UpperCamelCase_				: Tuple=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: int=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: str=False					,						):
			lowerCAmelCase_   :       Tuple    							=size if size is not None else {'''height''': 30, '''width''': 30}
			lowerCAmelCase_   :       List[Any]    							=parent
			lowerCAmelCase_   :       int    							=batch_size
			lowerCAmelCase_   :       int    							=num_channels
			lowerCAmelCase_   :       Union[str, Any]    							=min_resolution
			lowerCAmelCase_   :       Tuple    							=max_resolution
			lowerCAmelCase_   :       Tuple    							=do_resize
			lowerCAmelCase_   :       List[str]    							=size
			lowerCAmelCase_   :       int    							=do_normalize
			lowerCAmelCase_   :       int    							=image_mean
			lowerCAmelCase_   :       str    							=image_std
			lowerCAmelCase_   :       List[Any]    							=do_reduce_labels





	def 	__A      (						self				: Optional[Any]      ):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_reduce_labels": self.do_reduce_labels,
			}







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Optional[int]    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Optional[Any]    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )

		return image, map







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Tuple    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )
		lowerCAmelCase_   :       Optional[int]    							=Image.open(dataset[2]['''file''']  )
		lowerCAmelCase_   :       Union[str, Any]    							=Image.open(dataset[3]['''file''']  )

		return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		SegformerImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							=SegformerImageProcessingTester(self      )




	@property
	def 	__A      (						self				: str      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_resize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''size'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_normalize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_mean'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_std'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_reduce_labels'''      )      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       str    							=self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size					,						{'''height''': 30, '''width''': 30}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Tuple    							=self.image_processing_class.from_dict(self.image_processor_dict					,						size=42					,						reduce_labels=UpperCamelCase_      )
			self.assertEqual(image_processor.size					,						{'''height''': 42, '''width''': 42}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )




	def 	__A      (						self				: Dict      ):
			pass




	def 	__A      (						self				: Any      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       Dict    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						Image.Image      )

			# Test not batched input
			lowerCAmelCase_   :       int    							=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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Union[str, Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			lowerCAmelCase_   :       List[str]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						numpify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						np.ndarray      )

			# Test not batched input
			lowerCAmelCase_   :       Any    							=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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       str    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[int]    							=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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       str    							=image_processing(UpperCamelCase_					,						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['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       Optional[Any]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=[]
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )
					maps.append(torch.zeros(image.shape[-2:]      ).long()      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(image_inputs[0]					,						maps[0]					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched
			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test not batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       Any    							=prepare_semantic_batch_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        2,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        2,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )





	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )

			# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()
			lowerCAmelCase_   :       Dict    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 150      )

			lowerCAmelCase_   :       Optional[Any]    							=True
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )






