






import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase      :    Any   		=   get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
 from transformers.models.plbart.modeling_plbart import shift_tokens_right

__lowerCamelCase      :    str   		=   5_0003
__lowerCamelCase      :    int   		=   5_0002



@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__		(       __lowercase				,     unittest.TestCase		):





     """simple docstring"""






     a_			=	PLBartTokenizer
     a_			=	None
     a_			=	False



     def  _lowercase  (      self  :						int ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      snake_case__				:   List[Any]     =						PLBartTokenizer(__a							,	language_codes="base"							,	keep_accents=__a )
      tokenizer.save_pretrained(self.tmpdirname )



     def  _lowercase  (      self  :						int ):
      snake_case__				:   Optional[Any]     =						PLBartTokenizer(__a							,	language_codes="base"							,	keep_accents=__a )

      snake_case__				:   List[str]     =						tokenizer.tokenize("This is a test" )
      self.assertListEqual(__a							,	["▁This", "▁is", "▁a", "▁t", "est"] )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__a )							,	[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,	)

      snake_case__				:   Optional[Any]     =						tokenizer.tokenize("I was born in 92000, and this is falsé." )
      self.assertListEqual(
          __a							,	[
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "9",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "é",
              ".",
          ]							,	)
      snake_case__				:   str     =						tokenizer.convert_tokens_to_ids(__a )
      self.assertListEqual(
          __a							,	[
              value + tokenizer.fairseq_offset
              for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
          ]							,	)

      snake_case__				:   Dict     =						tokenizer.convert_ids_to_tokens(__a )
      self.assertListEqual(
          __a							,	[
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "<unk>",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "<unk>",
              ".",
          ]							,	)

      snake_case__				:   List[Any]     =						tokenizer.vocab_size
      snake_case__				:   str     =						[tokenizer.convert_ids_to_tokens(__a ) for x in range(end - 4							,	__a )]

      self.assertListEqual(__a							,	["__java__", "__python__", "__en_XX__", "<mask>"] )

      snake_case__				:   Optional[Any]     =						"""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
      snake_case__				:   Optional[int]     =						tokenizer(__a ).input_ids
      self.assertEqual(
          tokenizer.decode(__a							,	skip_special_tokens=__a							,	clean_up_tokenization_spaces=__a )							,	__a							,	)






     def  _lowercase  (      self  :						Optional[int] ):
      snake_case__				:   Optional[Any]     =						PLBartTokenizer(__a							,	language_codes="multi"							,	keep_accents=__a )

      snake_case__				:   Optional[int]     =						tokenizer.tokenize("This is a test" )
      self.assertListEqual(__a							,	["▁This", "▁is", "▁a", "▁t", "est"] )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__a )							,	[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,	)

      snake_case__				:   int     =						tokenizer.tokenize("I was born in 92000, and this is falsé." )
      self.assertListEqual(
          __a							,	[
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "9",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "é",
              ".",
          ]							,	)
      snake_case__				:   Optional[Any]     =						tokenizer.convert_tokens_to_ids(__a )
      self.assertListEqual(
          __a							,	[
              value + tokenizer.fairseq_offset
              for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
          ]							,	)

      snake_case__				:   str     =						tokenizer.convert_ids_to_tokens(__a )
      self.assertListEqual(
          __a							,	[
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "<unk>",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "<unk>",
              ".",
          ]							,	)
      snake_case__				:   Tuple     =						tokenizer.vocab_size
      snake_case__				:   int     =						[tokenizer.convert_ids_to_tokens(__a ) for x in range(end - 7							,	__a )]

      self.assertListEqual(
          __a							,	["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"] )
      snake_case__				:   Tuple     =						"""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
      snake_case__				:   List[Any]     =						tokenizer(__a ).input_ids
      self.assertEqual(
          tokenizer.decode(__a							,	skip_special_tokens=__a							,	clean_up_tokenization_spaces=__a )							,	__a							,	)



@require_torch
@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





     """simple docstring"""






     a_			=	'''uclanlp/plbart-python-en_XX'''
     a_			=	[
         '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
         '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
     ]
     a_			=	[
         '''Returns the maximum value of a b c.''',
         '''Sums the values of a b c.''',
     ]
     a_			=	[
         1_3_4,
         5_4_5_2,
         3_3_4_6_0,
         3_3_4_4_1,
         3_3_4_6_3,
         3_3_4_6_5,
         3_3_4_6_3,
         3_3_4_4_9,
         9_8_8,
         2_0,
         3_3_4_5_6,
         1_9,
         3_3_4_5_6,
         7_7_1,
         3_9,
         4_2_5_8,
         8_8_9,
         3_3_1_8,
         3_3_4_4_1,
         3_3_4_6_3,
         3_3_4_6_5,
         3_3_4_6_3,
         3_3_4_4_9,
         2_4_7_1,
         2,
         PYTHON_CODE,
     ]



     @classmethod
     def  _lowercase  (      cls  :						Any ):
      snake_case__				:   PLBartTokenizer     =						PLBartTokenizer.from_pretrained(
          cls.checkpoint_name							,	language_codes="base"							,	src_lang="python"							,	tgt_lang="en_XX" )
      snake_case__				:   int     =						1
      return cls



     def  _lowercase  (      self  :						Tuple ):
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]							,	5_0_0_0_1 )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]							,	5_0_0_0_2 )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]							,	5_0_0_0_3 )



     def  _lowercase  (      self  :						List[str] ):
      snake_case__				:   Any     =						self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens							,	__a )



     def  _lowercase  (      self  :						Optional[int] ):
      self.assertIn(__a							,	self.tokenizer.all_special_ids )
      snake_case__				:   List[str]     =						[EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
      snake_case__				:   int     =						self.tokenizer.decode(__a							,	skip_special_tokens=__a )
      snake_case__				:   List[str]     =						self.tokenizer.decode(generated_ids[1:]							,	skip_special_tokens=__a )
      self.assertEqual(__a							,	__a )
      self.assertNotIn(self.tokenizer.eos_token							,	__a )



     def  _lowercase  (      self  :						Union[str, Any] ):
      snake_case__				:   List[Any]     =						["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 2_0]
      self.assertIsInstance(src_text[0]							,	__a )
      snake_case__				:   Dict     =						1_0
      snake_case__				:   str     =						self.tokenizer(__a							,	max_length=__a							,	truncation=__a ).input_ids[0]
      self.assertEqual(ids[-2]							,	2 )
      self.assertEqual(ids[-1]							,	__a )
      self.assertEqual(len(__a )							,	__a )



     def  _lowercase  (      self  :						Optional[Any] ):
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"] )							,	[5_0_0_0_4, 5_0_0_0_1] )



     def  _lowercase  (      self  :						int ):
      snake_case__				:   Optional[Any]     =						tempfile.mkdtemp()
      snake_case__				:   Optional[Any]     =						self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__a )
      snake_case__				:   str     =						PLBartTokenizer.from_pretrained(__a )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,	__a )



     @require_torch
     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Optional[int]     =						self.tokenizer(self.src_text							,	text_target=self.tgt_text							,	padding=__a							,	return_tensors="pt" )
      snake_case__				:   int     =						shift_tokens_right(batch["labels"]							,	self.tokenizer.pad_token_id )

      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      self.assertEqual(batch.input_ids[1][-2:].tolist()							,	[2, PYTHON_CODE] )
      self.assertEqual(batch.decoder_input_ids[1][0]							,	__a )
      self.assertEqual(batch.decoder_input_ids[1][-1]							,	2 )
      self.assertEqual(batch.labels[1][-2:].tolist()							,	[2, EN_CODE] )



     @require_torch
     def  _lowercase  (      self  :						Optional[int] ):
      snake_case__				:   Tuple     =						self.tokenizer(
          self.src_text							,	text_target=self.tgt_text							,	padding=__a							,	truncation=__a							,	max_length=len(self.expected_src_tokens )							,	return_tensors="pt"							,	)
      snake_case__				:   Tuple     =						shift_tokens_right(batch["labels"]							,	self.tokenizer.pad_token_id )

      self.assertIsInstance(__a							,	__a )

      self.assertEqual((2, 2_6)							,	batch.input_ids.shape )
      self.assertEqual((2, 2_6)							,	batch.attention_mask.shape )
      snake_case__				:   List[Any]     =						batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens							,	__a )
      self.assertEqual(2							,	batch.decoder_input_ids[0, -1] )  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens							,	[] )
      self.assertEqual(self.tokenizer.suffix_tokens							,	[self.tokenizer.eos_token_id, PYTHON_CODE] )



     def  _lowercase  (      self  :						Optional[int] ):
      snake_case__				:   Dict     =						self.tokenizer(self.src_text							,	padding=__a							,	truncation=__a							,	max_length=3							,	return_tensors="pt" )
      snake_case__				:   List[str]     =						self.tokenizer(
          text_target=self.tgt_text							,	padding=__a							,	truncation=__a							,	max_length=1_0							,	return_tensors="pt" )
      snake_case__				:   Optional[int]     =						targets["""input_ids"""]
      snake_case__				:   int     =						shift_tokens_right(__a							,	self.tokenizer.pad_token_id )

      self.assertEqual(batch.input_ids.shape[1]							,	3 )
      self.assertEqual(batch.decoder_input_ids.shape[1]							,	1_0 )






     @require_torch
     def  _lowercase  (      self  :						List[Any] ):
      snake_case__				:   List[str]     =						self.tokenizer._build_translation_inputs(
          "A test"							,	return_tensors="pt"							,	src_lang="en_XX"							,	tgt_lang="java" )

      self.assertEqual(
          nested_simplify(__a )							,	{
              # A, test, EOS, en_XX
              "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
              "attention_mask": [[1, 1, 1, 1]],
              # java
              "forced_bos_token_id": 5_0_0_0_1,
          }							,	)











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 ConditionalDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






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



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

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

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ConditionalDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessingTester(self )



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



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						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  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						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							,	__A )

						snake_case__				:   Any     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



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

						# Test not batched input
						snake_case__				:   Union[str, Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		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
						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						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  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   List[str]     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   List[str]     =						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
						snake_case__				:   int     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Dict     =						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
						snake_case__				:   Optional[Any]     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   str     =						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  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   str     =						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
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Optional[int]     =						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
						snake_case__				:   Dict     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						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,
						    )							,	)



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

						snake_case__				:   Optional[Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
						snake_case__				:   int     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   str     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Tuple     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Optional[int]     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Tuple     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   List[Any]     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[int]     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   Dict     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[str]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						str ):
						# prepare image, target and masks_path
						snake_case__				:   Optional[Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   int     =						json.loads(f.read() )

						snake_case__				:   Optional[int]     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

						# encode them
						snake_case__				:   Optional[int]     =						ConditionalDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   List[str]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Dict     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   int     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[Any]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   str     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



