




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 		__A      :

						'''simple docstring'''





						UpperCamelCase     						=					LEDConfig
						UpperCamelCase     						=					{}
						UpperCamelCase     						=					"""gelu"""

						def __init__(		self   :Optional[int]    , __snake_case   :Optional[int]    , __snake_case   :Optional[Any]=13    , __snake_case   :List[Any]=7    , __snake_case   :List[Any]=True    , __snake_case   :Optional[Any]=False    , __snake_case   :Optional[Any]=99    , __snake_case   :Optional[Any]=32    , __snake_case   :Tuple=2    , __snake_case   :Optional[Any]=4    , __snake_case   :Optional[int]=37    , __snake_case   :Tuple=0.1    , __snake_case   :str=0.1    , __snake_case   :Optional[int]=20    , __snake_case   :List[str]=2    , __snake_case   :List[Any]=1    , __snake_case   :List[Any]=0    , __snake_case   :Tuple=4    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=seq_length
										__magic_name__	:	Dict								=is_training
										__magic_name__	:	Any								=use_labels
										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=hidden_size
										__magic_name__	:	Dict								=num_hidden_layers
										__magic_name__	:	int								=num_attention_heads
										__magic_name__	:	Tuple								=intermediate_size

										__magic_name__	:	List[str]								=hidden_dropout_prob
										__magic_name__	:	Tuple								=attention_probs_dropout_prob
										__magic_name__	:	List[Any]								=max_position_embeddings
										__magic_name__	:	Optional[int]								=eos_token_id
										__magic_name__	:	Dict								=pad_token_id
										__magic_name__	:	str								=bos_token_id
										__magic_name__	:	Tuple								=attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										__magic_name__	:	Optional[Any]								=self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										__magic_name__	:	List[Any]								=(
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=ids_tensor([self.batch_size, self.seq_length - 1]    , self.vocab_size )
										__magic_name__	:	str								=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )    , 1 )
										__magic_name__	:	int								=tf.concat([input_ids, eos_tensor]    , axis=1 )

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

										__magic_name__	:	Optional[Any]								=self.config_cls(
										    vocab_size=self.vocab_size    , d_model=self.hidden_size    , encoder_layers=self.num_hidden_layers    , decoder_layers=self.num_hidden_layers    , encoder_attention_heads=self.num_attention_heads    , decoder_attention_heads=self.num_attention_heads    , encoder_ffn_dim=self.intermediate_size    , decoder_ffn_dim=self.intermediate_size    , dropout=self.hidden_dropout_prob    , attention_dropout=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , eos_token_ids=[2]    , bos_token_id=self.bos_token_id    , pad_token_id=self.pad_token_id    , decoder_start_token_id=self.pad_token_id    , attention_window=self.attention_window    , **self.config_updates    , )
										__magic_name__	:	Optional[int]								=prepare_led_inputs_dict(__snake_case    , __snake_case    , __snake_case )
										__magic_name__	:	str								=tf.concat(
										    [tf.zeros_like(__snake_case )[:, :-1], tf.ones_like(__snake_case )[:, -1:]]    , axis=-1    , )
										__magic_name__	:	int								=global_attention_mask
										return config, inputs_dict


						def     A__						(		self   :Dict    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=TFLEDModel(config=__snake_case ).get_decoder()
										__magic_name__	:	Tuple								=inputs_dict["""input_ids"""]

										__magic_name__	:	Union[str, Any]								=input_ids[:1, :]
										__magic_name__	:	Tuple								=inputs_dict["""attention_mask"""][:1, :]
										__magic_name__	:	Optional[int]								=1

										# first forward pass
										__magic_name__	:	Any								=model(__snake_case    , attention_mask=__snake_case    , use_cache=__snake_case )

										__magic_name__	:	str								=outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__magic_name__	:	Any								=ids_tensor((self.batch_size, 3)    , config.vocab_size )
										__magic_name__	:	List[Any]								=tf.cast(ids_tensor((self.batch_size, 3)    , 2 )    , tf.inta )

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

										__magic_name__	:	Any								=model(__snake_case    , attention_mask=__snake_case )[0]
										__magic_name__	:	List[str]								=model(__snake_case    , attention_mask=__snake_case    , past_key_values=__snake_case )[0]

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

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

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




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=None	,     lowerCamelCase=None	,     lowerCamelCase=None	,     lowerCamelCase=None	,     ):
				if attention_mask is None:
								__magic_name__	:	List[Any]								=tf.cast(tf.math.not_equal(lowerCamelCase	,     config.pad_token_id   )	,     tf.inta   )
				if decoder_attention_mask is None:
								__magic_name__	:	Union[str, 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:
								__magic_name__	:	Any								=tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								__magic_name__	:	List[str]								=tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				    "decoder_input_ids": decoder_input_ids,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				}




@require_tf
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):

						'''simple docstring'''





						UpperCamelCase     						=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
						UpperCamelCase     						=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
						UpperCamelCase     						=					(
						    {
						        """conversational""": TFLEDForConditionalGeneration,
						        """feature-extraction""": TFLEDModel,
						        """summarization""": TFLEDForConditionalGeneration,
						        """text2text-generation""": TFLEDForConditionalGeneration,
						        """translation""": TFLEDForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCamelCase     						=					True
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=TFLEDModelTester(self )
										__magic_name__	:	Tuple								=ConfigTester(self    , config_class=__snake_case )

						def     A__						(		self   :str ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	str								=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__snake_case )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__	:	Union[str, Any]								=tf.zeros_like(inputs_dict["""attention_mask"""] )
										__magic_name__	:	Tuple								=2
										__magic_name__	:	List[Any]								=tf.where(
										    tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices    , 1    , inputs_dict["""global_attention_mask"""]    , )

										__magic_name__	:	List[str]								=True
										__magic_name__	:	List[str]								=self.model_tester.seq_length
										__magic_name__	:	Optional[Any]								=self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__snake_case   :Optional[Any] ):
														__magic_name__	:	Optional[Any]								=outputs.decoder_attentions
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertListEqual(
														    list(decoder_attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, seq_length, seq_length]    , )

										def check_encoder_attentions_output(__snake_case   :Any ):
														__magic_name__	:	Tuple								=[t.numpy() for t in outputs.encoder_attentions]
														__magic_name__	:	Optional[Any]								=[t.numpy() for t in outputs.encoder_global_attentions]
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertEqual(len(__snake_case )    , self.model_tester.num_hidden_layers )
														self.assertListEqual(
														    list(attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, seq_length, seq_length]    , )
														self.assertListEqual(
														    list(global_attentions[0].shape[-3:] )    , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    , )

										for model_class in self.all_model_classes:
														__magic_name__	:	int								=True
														__magic_name__	:	Tuple								=False
														__magic_name__	:	Any								=False
														__magic_name__	:	Tuple								=model_class(__snake_case )
														__magic_name__	:	List[Any]								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
														__magic_name__	:	Dict								=len(__snake_case )
														self.assertEqual(config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

														if self.is_encoder_decoder:
																		__magic_name__	:	Any								=model_class(__snake_case )
																		__magic_name__	:	Any								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
																		self.assertEqual(config.output_hidden_states    , __snake_case )
																		check_decoder_attentions_output(__snake_case )

														# Check that output attentions can also be changed via the config
														del inputs_dict["output_attentions"]
														__magic_name__	:	Any								=True
														__magic_name__	:	str								=model_class(__snake_case )
														__magic_name__	:	str								=model(self._prepare_for_class(__snake_case    , __snake_case ) )
														self.assertEqual(config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

														# Check attention is always last and order is fine
														__magic_name__	:	List[Any]								=True
														__magic_name__	:	Dict								=True
														__magic_name__	:	Tuple								=model_class(__snake_case )
														__magic_name__	:	int								=model(self._prepare_for_class(__snake_case    , __snake_case ) )

														self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    , len(__snake_case ) )
														self.assertEqual(model.config.output_hidden_states    , __snake_case )
														check_encoder_attentions_output(__snake_case )

						@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""" )
						def     A__						(		self   :int ):
										'''simple docstring'''

										pass


						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										pass




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				return tf.constant(lowerCamelCase	,     dtype=tf.intaa   )


UpperCAmelCase_		:				Optional[Any]									=     1e-4




@slow
@require_tf
class 		__A      (			unittest.TestCase			):

						'''simple docstring'''





						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ).led

										# change to intended input here
										__magic_name__	:	Dict								=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	str								=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	List[Any]								=prepare_led_inputs_dict(model.config    , __snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=model(**__snake_case )[0]
										__magic_name__	:	str								=(1, 10_24, 7_68)
										self.assertEqual(output.shape    , __snake_case )
										# change to expected output here
										__magic_name__	:	str								=tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]    , )
										tf.debugging.assert_near(output[:, :3, :3]    , __snake_case    , atol=1E-3 )


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Dict								=TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" )

										# change to intended input here
										__magic_name__	:	Any								=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	Tuple								=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
										__magic_name__	:	Any								=prepare_led_inputs_dict(model.config    , __snake_case    , __snake_case )
										__magic_name__	:	Dict								=model(**__snake_case )[0]
										__magic_name__	:	str								=(1, 10_24, model.config.vocab_size)
										self.assertEqual(output.shape    , __snake_case )
										# change to expected output here
										__magic_name__	:	Any								=tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]    , )
										tf.debugging.assert_near(output[:, :3, :3]    , __snake_case    , atol=1E-3    , rtol=1E-3 )










import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DetaImageProcessor




class 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''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     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

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

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

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
										self.assertEqual(image_processor.do_pad    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__snake_case    , 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     A__						(		self   :List[str] ):
										'''simple docstring'''

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

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

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

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

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

						@slow
						def     A__						(		self   :str ):
										'''simple docstring'''

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

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	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:
														__magic_name__	:	str								=json.loads(f.read() )

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




