
import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
			from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=None	, ):
				if attention_mask is None:
								lowerCamelCase_						=			input_ids.ne(config.pad_token_id    )
				if decoder_attention_mask is None:
								lowerCamelCase_						=			decoder_input_ids.ne(config.pad_token_id    )
				if head_mask is None:
								lowerCamelCase_						=			torch.ones(config.encoder_layers	, config.encoder_attention_heads	, device=lowerCamelCase__    )
				if decoder_head_mask is None:
								lowerCamelCase_						=			torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=lowerCamelCase__    )
				if cross_attn_head_mask is None:
								lowerCamelCase_						=			torch.ones(config.decoder_layers	, config.decoder_attention_heads	, device=lowerCamelCase__    )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=7						,  lowercase=True						,  lowercase=False						,  lowercase=99						,  lowercase=16						,  lowercase=2						,  lowercase=4						,  lowercase=4						,  lowercase="relu"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=0.0						,  lowercase=0.0						,  lowercase=20						,  lowercase=2						,  lowercase=1						,  lowercase=0						,  )				->       int:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			seq_length
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			vocab_size
					lowerCamelCase_						=			hidden_size
					lowerCamelCase_						=			num_hidden_layers
					lowerCamelCase_						=			num_attention_heads
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			hidden_dropout_prob
					lowerCamelCase_						=			attention_probs_dropout_prob
					lowerCamelCase_						=			encoder_layerdrop
					lowerCamelCase_						=			decoder_layerdrop
					lowerCamelCase_						=			max_position_embeddings
					lowerCamelCase_						=			eos_token_id
					lowerCamelCase_						=			pad_token_id
					lowerCamelCase_						=			bos_token_id



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )
					lowerCamelCase_						=			self.eos_token_id  # Eos Token
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

					# we need to clamp the input ids here to avoid having pad token in between
					# this is because for M2M100 the position_ids are prepared such that
					# all pad tokens have pos id = 2 and rest are between 2..seq_length
					# and the seq_length here is seq_length - num_pad_tokens
					# but when using past, there is no way of knowing if the past input ids had
					# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
					# position_ids being off by num_pad_tokens in past input
					lowerCamelCase_						=			input_ids.clamp(self.pad_token_id + 1 )
					lowerCamelCase_						=			decoder_input_ids.clamp(self.pad_token_id + 1 )

					lowerCamelCase_						=			self.get_config()
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(lowercase						,  lowercase						,  lowercase )
					return config, inputs_dict



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return MaMaaaConfig(
					    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						,  encoder_layerdrop=self.encoder_layerdrop						,  decoder_layerdrop=self.decoder_layerdrop						,  max_position_embeddings=self.max_position_embeddings						,  eos_token_id=self.eos_token_id						,  bos_token_id=self.bos_token_id						,  pad_token_id=self.pad_token_id						,  )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_	,    lowerCamelCase_						=			self.prepare_config_and_inputs()
					return config, inputs_dict



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			MaMaaaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
					lowerCamelCase_						=			inputs_dict["input_ids"]
					lowerCamelCase_						=			inputs_dict["attention_mask"]
					lowerCamelCase_						=			inputs_dict["head_mask"]

					# first forward pass
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase						,  head_mask=lowercase						,  use_cache=lowercase )

					lowerCamelCase_	,    lowerCamelCase_						=			outputs.to_tuple()

					# create hypothetical multiple next token and extent to next_input_ids
					lowerCamelCase_						=			ids_tensor((self.batch_size, 3)						,  config.vocab_size )
					lowerCamelCase_						=			ids_tensor((self.batch_size, 3)						,  2 )

					# append to next input_ids and
					lowerCamelCase_						=			torch.cat([input_ids, next_tokens]						,  dim=-1 )
					lowerCamelCase_						=			torch.cat([attention_mask, next_attn_mask]						,  dim=-1 )

					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase )["last_hidden_state"]
					lowerCamelCase_						=			model(lowercase						,  attention_mask=lowercase						,  past_key_values=lowercase )[
					    "last_hidden_state"
					]

					# select random slice
					lowerCamelCase_						=			ids_tensor((1,)						,  output_from_past.shape[-1] ).item()
					lowerCamelCase_						=			output_from_no_past[:, -3:, random_slice_idx].detach()
					lowerCamelCase_						=			output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(lowercase						,  lowercase						,  atol=1e-2 ) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			MaMaaaModel(config=lowercase ).to(lowercase ).eval()
					lowerCamelCase_						=			model(**lowercase )

					lowerCamelCase_						=			outputs.encoder_last_hidden_state
					lowerCamelCase_						=			outputs.last_hidden_state

					with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_						=			model.get_encoder()
									encoder.save_pretrained(lowercase )
									lowerCamelCase_						=			MaMaaaEncoder.from_pretrained(lowercase ).to(lowercase )

					lowerCamelCase_						=			encoder(inputs_dict["input_ids"]						,  attention_mask=inputs_dict["attention_mask"] )[
					    0
					]

					self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )

					with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_						=			model.get_decoder()
									decoder.save_pretrained(lowercase )
									lowerCamelCase_						=			MaMaaaDecoder.from_pretrained(lowercase ).to(lowercase )

					lowerCamelCase_						=			decoder(
					    input_ids=inputs_dict["decoder_input_ids"]						,  attention_mask=inputs_dict["decoder_attention_mask"]						,  encoder_hidden_states=lowercase						,  encoder_attention_mask=inputs_dict["attention_mask"]						,  )[0]

					self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )




@require_torch
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__         = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	lowerCAmelCase__         = (
	    {
	        'conversational': MaMaaaForConditionalGeneration,
	        'feature-extraction': MaMaaaModel,
	        'summarization': MaMaaaForConditionalGeneration,
	        'text2text-generation': MaMaaaForConditionalGeneration,
	        'translation': MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase__         = True
	lowerCAmelCase__         = True
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if pipeline_test_casse_name == "TranslationPipelineTests":
									# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
									# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
									return True

					return False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			MaMaaaModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(lowercase )

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowercase )
													lowerCamelCase_	,    lowerCamelCase_						=			model_class.from_pretrained(lowercase						,  output_loading_info=lowercase )
									self.assertEqual(info["missing_keys"]						,  [] )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_encoder_decoder_model_standalone(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
									lowerCamelCase_						=			model_class(lowercase )
									model.to(lowercase )
									model.eval()

									lowerCamelCase_						=			copy.deepcopy(self._prepare_for_class(lowercase						,  lowercase ) )

									if not self.is_encoder_decoder:
													lowerCamelCase_						=			inputs["input_ids"]
													del inputs["input_ids"]
									else:
													lowerCamelCase_						=			inputs["input_ids"]
													lowerCamelCase_						=			inputs.get("decoder_input_ids"						,  lowercase )
													del inputs["input_ids"]
													inputs.pop("decoder_input_ids"						,  lowercase )

									lowerCamelCase_						=			model.get_input_embeddings()
									if not self.is_encoder_decoder:
													lowerCamelCase_						=			wte(lowercase )
									else:
													lowerCamelCase_						=			wte(lowercase )
													lowerCamelCase_						=			wte(lowercase )

									with torch.no_grad():
													model(**lowercase )[0]



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					lowerCamelCase_						=			input_dict["input_ids"]
					lowerCamelCase_						=			input_ids.ne(1 ).to(lowercase )
					lowerCamelCase_						=			MaMaaaForConditionalGeneration(lowercase ).eval().to(lowercase )
					if torch_device == "cuda":
									model.half()
					model.generate(lowercase						,  attention_mask=lowercase )
					model.generate(num_beams=4						,  do_sample=lowercase						,  early_stopping=lowercase						,  num_return_sequences=3 )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return torch.tensor(lowerCamelCase__	, dtype=torch.long	, device=lowerCamelCase__    )


__A				=1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
					lowerCamelCase_						=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
					lowerCamelCase_						=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(model.config						,  lowercase						,  lowercase )
					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )[0]
					lowerCamelCase_						=			torch.Size((1, 11, 1024) )
					self.assertEqual(output.shape						,  lowercase )
					# change to expected output here
					lowerCamelCase_						=			torch.tensor(
					    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]						,  device=lowercase )
					self.assertTrue(torch.allclose(output[:, :3, :3]						,  lowercase						,  atol=lowercase ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )

					# change to intended input
					lowerCamelCase_						=			_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
					lowerCamelCase_						=			_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
					lowerCamelCase_						=			prepare_mam_aaa_inputs_dict(model.config						,  lowercase						,  lowercase )
					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )[0]
					lowerCamelCase_						=			torch.Size((1, 11, model.config.vocab_size) )
					self.assertEqual(output.shape						,  lowercase )
					# change to expected output here
					lowerCamelCase_						=			torch.tensor(
					    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]						,  device=lowercase )
					self.assertTrue(torch.allclose(output[:, :3, :3]						,  lowercase						,  atol=lowercase ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(lowercase )
					lowerCamelCase_						=			MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"						,  src_lang="fr"						,  tgt_lang="en" )

					lowerCamelCase_						=			[
					    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
					    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
					    "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
					    " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
					    " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
					]

					# The below article tests that we don't add any hypotheses outside of the top n_beams
					lowerCamelCase_						=			tokenizer(lowercase						,  padding=lowercase						,  return_tensors="pt" )

					lowerCamelCase_						=			model.generate(
					    input_ids=dct["input_ids"].to(lowercase )						,  attention_mask=dct["attention_mask"].to(lowercase )						,  num_beams=5						,  forced_bos_token_id=tokenizer.get_lang_id("en" )						,  )

					lowerCamelCase_						=			[
					    "The NSA case highlights the total absence of intelligence debate",
					    "I think there are two levels of response from the French government.",
					    "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
					    " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
					    " communications in France.",
					]

					lowerCamelCase_						=			tokenizer.batch_decode(
					    hypotheses_batch.tolist()						,  clean_up_tokenization_spaces=lowercase						,  skip_special_tokens=lowercase )
					assert generated == expected_en






'''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 ConditionalDetrImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=True    ,   UpperCamelCase_=1 / 255    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase__   :str												= parent
							UpperCamelCase__   :List[Any]												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :str												= min_resolution
							UpperCamelCase__   :Optional[Any]												= max_resolution
							UpperCamelCase__   :int												= do_resize
							UpperCamelCase__   :Optional[Any]												= size
							UpperCamelCase__   :Tuple												= do_normalize
							UpperCamelCase__   :List[Any]												= image_mean
							UpperCamelCase__   :Dict												= image_std
							UpperCamelCase__   :Union[str, Any]												= do_rescale
							UpperCamelCase__   :Union[str, Any]												= rescale_factor
							UpperCamelCase__   :Union[str, Any]												= do_pad




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							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    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


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

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

							return expected_height, expected_width



@require_torch
@require_vision
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				ConditionalDetrImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_mean'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''image_std'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_normalize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''do_resize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_    ,   '''size'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 18, '''longest_edge''': 1333}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= self.image_processing_class.from_dict(
							    self.image_processor_dict    ,   size=42    ,   max_size=84    ,   pad_and_return_pixel_mask=UpperCamelCase_							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 42, '''longest_edge''': 84}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :List[str]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

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

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__   :Any												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_    ,   numpify=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   np.ndarray							)

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

							UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :Union[str, Any]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.image_processor_tester.get_expected_values(UpperCamelCase_							)

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

							# Test batched
							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							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							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Optional[int]												= json.loads(f.read()							)

							UpperCamelCase__   :Any												= {'''image_id''': 39769, '''annotations''': target}

							# encode them
							UpperCamelCase__   :str												= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
							UpperCamelCase__   :List[Any]												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :List[str]												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :str												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :str												= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Optional[Any]												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[Any]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :int												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :List[str]												= torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify orig_size
							UpperCamelCase__   :Tuple												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Tuple												= json.loads(f.read()							)

							UpperCamelCase__   :List[str]												= {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

							UpperCamelCase__   :Any												= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

							# encode them
							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
							UpperCamelCase__   :Dict												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   masks_path=UpperCamelCase_    ,   return_tensors='''pt'''							)

							# verify pixel values
							UpperCamelCase__   :str												= torch.Size([1, 3, 800, 1066]							)
							self.assertEqual(encoding['''pixel_values'''].shape    ,   UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :Tuple												= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Any												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[str]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :str												= torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify masks
							UpperCamelCase__   :Optional[Any]												= 822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   UpperCamelCase_							)
							# verify orig_size
							UpperCamelCase__   :List[str]												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :List[Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)