
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 			lowercase_		:
       __lowerCamelCase				       =						PegasusConfig
       __lowerCamelCase				       =						{}
       __lowerCamelCase				       =						"gelu"


       def __init__(    self						,       __A						,       __A=13						,       __A=7						,       __A=True						,       __A=False						,       __A=99						,       __A=32						,       __A=2						,       __A=4						,       __A=37						,       __A=0.1						,       __A=0.1						,       __A=40						,       __A=2						,       __A=1						,       __A=0						,       )						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=parent
              SCREAMING_SNAKE_CASE_							:				List[str]		=batch_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=seq_length
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=is_training
              SCREAMING_SNAKE_CASE_							:				Any		=use_labels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=vocab_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				List[str]		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				str		=intermediate_size

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				str		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=eos_token_id
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=pad_token_id
              SCREAMING_SNAKE_CASE_							:				Any		=bos_token_id


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=ids_tensor([self.batch_size, self.seq_length - 1]						,       self.vocab_size				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)						,       1				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tf.concat([input_ids, eos_tensor]						,       axis=1				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.config_cls(
                  vocab_size=self.vocab_size						,       d_model=self.hidden_size						,       encoder_layers=self.num_hidden_layers						,       decoder_layers=self.num_hidden_layers						,       encoder_attention_heads=self.num_attention_heads						,       decoder_attention_heads=self.num_attention_heads						,       encoder_ffn_dim=self.intermediate_size						,       decoder_ffn_dim=self.intermediate_size						,       dropout=self.hidden_dropout_prob						,       attention_dropout=self.attention_probs_dropout_prob						,       max_position_embeddings=self.max_position_embeddings						,       eos_token_ids=[2]						,       bos_token_id=self.bos_token_id						,       pad_token_id=self.pad_token_id						,       decoder_start_token_id=self.pad_token_id						,       **self.config_updates						,       )
              SCREAMING_SNAKE_CASE_							:				List[Any]		=prepare_pegasus_inputs_dict(__A						,       __A						,       __A				)
              return config, inputs_dict





       def 	_snake_case				(    self						,       __A						,       __A				)						->    int:
              SCREAMING_SNAKE_CASE_							:				int		=TFPegasusModel(config=__A				).get_decoder()
              SCREAMING_SNAKE_CASE_							:				Dict		=inputs_dict['''input_ids''']

              SCREAMING_SNAKE_CASE_							:				Any		=input_ids[:1, :]
              SCREAMING_SNAKE_CASE_							:				Tuple		=inputs_dict['''attention_mask'''][:1, :]
              SCREAMING_SNAKE_CASE_							:				List[str]		=inputs_dict['''head_mask''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=1

              # first forward pass
              SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       attention_mask=__A						,       head_mask=__A						,       use_cache=__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=outputs.to_tuple()

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

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

              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A						,       attention_mask=__A				)[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       attention_mask=__A						,       past_key_values=__A				)[0]

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

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

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






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


@require_tf
class 			lowercase_		(      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
       __lowerCamelCase				       =						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
       __lowerCamelCase				       =						(
           {
               "conversational": TFPegasusForConditionalGeneration,
               "feature-extraction": TFPegasusModel,
               "summarization": TFPegasusForConditionalGeneration,
               "text2text-generation": TFPegasusForConditionalGeneration,
               "translation": TFPegasusForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Dict		=TFPegasusModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=ConfigTester(self						,       config_class=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              self.config_tester.run_common_tests()





       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_decoder_model_past_large_inputs(*__A				)






@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowercase_		(      unittest.TestCase       ):
       __lowerCamelCase				       =						[
           " 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				       =						[
           "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
           " reduce the risk of wildfires.",
           "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
       ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
       __lowerCamelCase				       =						"google/pegasus-xsum"


       @cached_property
       def 	_snake_case				(    self				)						->    str:
              return AutoTokenizer.from_pretrained(self.model_name				)


       @cached_property
       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
              return model


       def 	_snake_case				(    self						,       **__A				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.translate_src_text(**__A				)
              assert self.expected_text == generated_words


       def 	_snake_case				(    self						,       **__A				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Any		=self.tokenizer(self.src_text						,       **__A						,       padding=__A						,       return_tensors='''tf'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=self.model.generate(
                  model_inputs.input_ids						,       attention_mask=model_inputs.attention_mask						,       num_beams=2						,       use_cache=__A						,       )
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.tokenizer.batch_decode(generated_ids.numpy()						,       skip_special_tokens=__A				)
              return generated_words





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


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 YolosImageProcessor


class 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       __A=[0.5, 0.5, 0.5]						,       __A=[0.5, 0.5, 0.5]						,       __A=True						,       __A=1 / 255						,       __A=True						,       )						->    Tuple:
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              SCREAMING_SNAKE_CASE_							:				Dict		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_channels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				str		=max_resolution
              SCREAMING_SNAKE_CASE_							:				int		=do_resize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=size
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_mean
              SCREAMING_SNAKE_CASE_							:				Any		=image_std
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rescale_factor
              SCREAMING_SNAKE_CASE_							:				str		=do_pad


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

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

              return expected_height, expected_width






@require_torch
@require_vision
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						YolosImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=YolosImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Dict:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(__A						,       '''image_mean'''				)				)
              self.assertTrue(hasattr(__A						,       '''image_std'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_normalize'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_resize'''				)				)
              self.assertTrue(hasattr(__A						,       '''size'''				)				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
              self.assertEqual(image_processor.do_pad						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class.from_dict(
                  self.image_processor_dict						,       size=42						,       max_size=84						,       pad_and_return_pixel_mask=__A				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 42, '''longest_edge''': 84}				)
              self.assertEqual(image_processor.do_pad						,       __A				)


       def 	_snake_case				(    self				)						->    List[str]:
              pass


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

              # Test not batched input
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=image_processing(__A						,       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 	_snake_case				(    self				)						->    Optional[Any]:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       numpify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       np.ndarray				)

              # Test not batched input
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

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


       def 	_snake_case				(    self				)						->    Dict:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

              # Test not batched input
              SCREAMING_SNAKE_CASE_							:				Any		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				str		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

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


       def 	_snake_case				(    self				)						->    List[Any]:
              # Initialize image_processings
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processing_class(**self.image_processor_dict				)
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(do_resize=__A						,       do_normalize=__A						,       do_rescale=__A				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

              # Test whether the method "pad" and calling the image processor return the same tensors
              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_processing_a.pad(__A						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_processing_a(__A						,       return_tensors='''pt'''				)

              self.assertTrue(
                  torch.allclose(encoded_images_with_method['''pixel_values''']						,       encoded_images['''pixel_values''']						,       atol=1e-4				)				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              # prepare image and target
              SCREAMING_SNAKE_CASE_							:				Dict		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,       '''r'''				) as f:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Dict		={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(images=__A						,       annotations=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([75, 75, 63, 65, 17, 17]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)





       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              # prepare image, target and masks_path
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,       '''r'''				) as f:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

              SCREAMING_SNAKE_CASE_							:				Tuple		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

              # encode them
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=YolosImageProcessor(format='''coco_panoptic'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_processing(images=__A						,       annotations=__A						,       masks_path=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([17, 17, 63, 75, 75, 93]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify masks
              SCREAMING_SNAKE_CASE_							:				List[Any]		=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,       __A				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)
