


"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
 snake_case__						:   List[Any]			 = '''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class 						snake_case_:
     __UpperCamelCase     =    PegasusConfig
     __UpperCamelCase     =    {}
     __UpperCamelCase     =    '''gelu'''






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Any=1_3    , UpperCamelCase_     :    List[Any]=7    , UpperCamelCase_     :    Tuple=True    , UpperCamelCase_     :    List[Any]=False    , UpperCamelCase_     :    Optional[Any]=9_9    , UpperCamelCase_     :    Any=3_2    , UpperCamelCase_     :    List[Any]=5    , UpperCamelCase_     :    str=4    , UpperCamelCase_     :    str=3_7    , UpperCamelCase_     :    Dict=0.1    , UpperCamelCase_     :    Dict=0.1    , UpperCamelCase_     :    Any=2_0    , UpperCamelCase_     :    Dict=2    , UpperCamelCase_     :    List[str]=1    , UpperCamelCase_     :    Any=0    , ):
           lowerCAmelCase    :		List[Any]				=      parent
           lowerCAmelCase    :		Optional[int]				=      batch_size
           lowerCAmelCase    :		Any				=      seq_length
           lowerCAmelCase    :		Dict				=      is_training
           lowerCAmelCase    :		Optional[int]				=      use_labels
           lowerCAmelCase    :		Union[str, Any]				=      vocab_size
           lowerCAmelCase    :		Tuple				=      hidden_size
           lowerCAmelCase    :		Any				=      num_hidden_layers
           lowerCAmelCase    :		List[str]				=      num_attention_heads
           lowerCAmelCase    :		Optional[Any]				=      intermediate_size

           lowerCAmelCase    :		Optional[int]				=      hidden_dropout_prob
           lowerCAmelCase    :		List[Any]				=      attention_probs_dropout_prob
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		str				=      eos_token_id
           lowerCAmelCase    :		List[Any]				=      pad_token_id
           lowerCAmelCase    :		List[str]				=      bos_token_id






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Optional[int]				=      ids_tensor([self.batch_size, self.seq_length - 1]    , self.vocab_size ).clip(3    , self.vocab_size )
           lowerCAmelCase    :		Union[str, Any]				=      np.expand_dims(np.array([self.eos_token_id] * self.batch_size )    , 1 )
           lowerCAmelCase    :		List[str]				=      np.concatenate([input_ids, eos_tensor]    , axis=1 )

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

           lowerCAmelCase    :		Optional[Any]				=      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    , )
           lowerCAmelCase    :		Dict				=      prepare_pegasus_inputs_dict(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )
           return config, inputs_dict






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Any				=      2_0
           lowerCAmelCase    :		Any				=      model_class_name(UpperCamelCase_ )

           lowerCAmelCase    :		List[str]				=      model.encode(inputs_dict['''input_ids'''] )

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

           lowerCAmelCase    :		Any				=      model.init_cache(decoder_input_ids.shape[0]    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    , dtype='''i4''' )

           lowerCAmelCase    :		Dict				=      jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    , )
           lowerCAmelCase    :		Optional[int]				=      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    :		int				=      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    :		Dict				=      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    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Dict				=      2_0
           lowerCAmelCase    :		Union[str, Any]				=      model_class_name(UpperCamelCase_ )

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

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

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

           lowerCAmelCase    :		Dict				=      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  (  _snake_case :	Tuple       ,      _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	Optional[Any]=None       ,      _snake_case :	Dict=None       ,      ):
      if attention_mask is None:
            lowerCAmelCase    :		Tuple				=      np.not_equal(_snake_case       ,      config.pad_token_id	).astype(np.inta	)
      if decoder_attention_mask is None:
            lowerCAmelCase    :		Dict				=      np.concatenate(
                [
                    np.ones(decoder_input_ids[:, :1].shape       ,      dtype=np.inta	),
                    np.not_equal(decoder_input_ids[:, 1:]       ,      config.pad_token_id	).astype(np.inta	),
                ]       ,      axis=-1       ,      )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }






@require_flax
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     __UpperCamelCase     =    (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     __UpperCamelCase     =    True
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      FlaxPegasusModelTester(self )
           lowerCAmelCase    :		Tuple				=      ConfigTester(self    , config_class=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str ):
           self.config_tester.run_common_tests()






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase,					lowerCAmelCase    :		int				=      self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase,					lowerCAmelCase    :		Optional[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    :		str				=      self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_ )
                       lowerCAmelCase    :		Tuple				=      model_class(UpperCamelCase_ )

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

                       with self.subTest('''JIT Enabled''' ):
                             lowerCAmelCase    :		Tuple				=      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    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      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[int]				=      model_class(UpperCamelCase_ )
                       lowerCAmelCase    :		Union[str, Any]				=      model.encode(inputs_dict['''input_ids''']    , inputs_dict['''attention_mask'''] )

                       lowerCAmelCase    :		Any				=      {
                           '''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_     :    Dict    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[Any] ):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_    , decoder_attention_mask=UpperCamelCase_    , encoder_outputs=UpperCamelCase_    , )

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

                       with self.subTest('''JIT Disabled''' ):
                             with jax.disable_jit():
                                   lowerCAmelCase    :		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    lowerCamelCase__   (		self     :    str ):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase    :		int				=      model_class_name.from_pretrained('''google/pegasus-large'''    , from_pt=UpperCamelCase_ )
                 lowerCAmelCase    :		List[Any]				=      np.ones((1, 1) )
                 lowerCAmelCase    :		str				=      model(UpperCamelCase_ )
                 self.assertIsNotNone(UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Any				=      FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
           lowerCAmelCase    :		List[Any]				=      PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )

           lowerCAmelCase    :		int				=      [
               ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
               ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
           ]

           lowerCAmelCase    :		str				=      [
               '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
               '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
           ]

           lowerCAmelCase    :		Optional[Any]				=      tokenizer(UpperCamelCase_    , return_tensors='''np'''    , truncation=UpperCamelCase_    , max_length=5_1_2    , padding=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      model.generate(**UpperCamelCase_    , num_beams=2 ).sequences
           lowerCAmelCase    :		Tuple				=      tokenizer.batch_decode(UpperCamelCase_    , skip_special_tokens=UpperCamelCase_ )
           assert tgt_text == decoded





"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       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_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Optional[Any]  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						List[str]  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Any  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	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__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	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,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	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__	:						List[str]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	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__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)




