






"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def 						lowercase    (				__snake_case				: Dict		,					__snake_case				: str		,					__snake_case				: Dict=None		,					__snake_case				: Dict=None	):
		if attention_mask is None:
				lowercase_ : List[Any]							       =   tf.cast(tf.math.not_equal(__snake_case		,					config.pad_token_id	)		,					tf.inta	)
		return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   OPTConfig
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   {}
					SCREAMING_SNAKE_CASE_		:				Dict							    =   "gelu"




					def __init__(				self     :						Any    ,						A     :						List[str]    ,						A     :						Dict=13    ,						A     :						Optional[Any]=7    ,						A     :						Dict=True    ,						A     :						Union[str, Any]=False    ,						A     :						List[Any]=99    ,						A     :						List[str]=16    ,						A     :						str=2    ,						A     :						List[Any]=4    ,						A     :						Union[str, Any]=4    ,						A     :						Optional[int]="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						List[str]=0.1    ,						A     :						Optional[Any]=20    ,						A     :						Optional[Any]=2    ,						A     :						str=1    ,						A     :						Union[str, Any]=0    ,						A     :						Optional[Any]=16    ,						A     :						List[Any]=16    ,						)  ->  Union[str, Any]:
							lowercase_ : Dict							       =   parent
							lowercase_ : Dict							       =   batch_size
							lowercase_ : str							       =   seq_length
							lowercase_ : Union[str, Any]							       =   is_training
							lowercase_ : Union[str, Any]							       =   use_labels
							lowercase_ : List[str]							       =   vocab_size
							lowercase_ : Union[str, Any]							       =   hidden_size
							lowercase_ : Optional[int]							       =   num_hidden_layers
							lowercase_ : List[str]							       =   num_attention_heads
							lowercase_ : str							       =   intermediate_size
							lowercase_ : Dict							       =   hidden_act
							lowercase_ : str							       =   hidden_dropout_prob
							lowercase_ : Dict							       =   attention_probs_dropout_prob
							lowercase_ : Dict							       =   max_position_embeddings
							lowercase_ : int							       =   eos_token_id
							lowercase_ : Any							       =   pad_token_id
							lowercase_ : Optional[Any]							       =   bos_token_id
							lowercase_ : Tuple							       =   embed_dim
							lowercase_ : Optional[Any]							       =   word_embed_proj_dim
							lowercase_ : Tuple							       =   False




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

							lowercase_ : int							       =   self.config_cls(
							    vocab_size=self.vocab_size    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						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_id=self.eos_token_id    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						embed_dim=self.embed_dim    ,						word_embed_proj_dim=self.word_embed_proj_dim    ,						is_encoder_decoder=A    ,						**self.config_updates    ,						)
							lowercase_ : Tuple							       =   prepare_opt_inputs_dict(A    ,						A   )
							return config, inputs_dict






					def   A  (				self     :						int    ,						A     :						Union[str, Any]    ,						A     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   TFOPTModel(config=A   )
							lowercase_ : int							       =   inputs_dict['''input_ids''']

							lowercase_ : Dict							       =   input_ids[:1, :]
							lowercase_ : Tuple							       =   inputs_dict['''attention_mask'''][:1, :]
							lowercase_ : Optional[int]							       =   1

							# first forward pass
							lowercase_ : Optional[Any]							       =   model(A    ,						attention_mask=A    ,						use_cache=A   )

							lowercase_       ,					lowercase_ : Optional[int]							       =   outputs.to_tuple()

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

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

							lowercase_ : List[str]							       =   model(A    ,						attention_mask=A   )[0]
							lowercase_ : Any							       =   model(A    ,						attention_mask=A    ,						past_key_values=A   )[0]

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

							# select random slice
							lowercase_ : Dict							       =   int(ids_tensor((1,)    ,						output_from_past.shape[-1]   )   )
							lowercase_ : Optional[Any]							       =   output_from_no_past[:, -3:, random_slice_idx]
							lowercase_ : Dict							       =   output_from_past[:, :, random_slice_idx]

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





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				int							    =   (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (TFOPTForCausalLM,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE_		:				Dict							    =   (
					    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
					)
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False
					SCREAMING_SNAKE_CASE_		:				Dict							    =   10




					def   A  (				self     :						Union[str, Any]   )  ->  str:
							lowercase_ : Dict							       =   TFOPTModelTester(self   )
							lowercase_ : Dict							       =   ConfigTester(self    ,						config_class=A   )




					def   A  (				self     :						List[str]   )  ->  str:
							self.config_tester.run_common_tests()




					def   A  (				self     :						Union[str, Any]   )  ->  str:
							lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*A   )






					def   A  (				self     :						str   )  ->  Optional[Any]:
							lowercase_       ,					lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(A     :						List[str]    ,						A     :						Any   ):
									if hasattr(A    ,						'''weight'''   ):
											return embedding_layer.weight
									else:
											# Here we build the word embeddings weights if not exists.
											# And then we retry to get the attribute once built.
											model.build()
											if hasattr(A    ,						'''weight'''   ):
													return embedding_layer.weight
											else:
													return None

							for model_class in self.all_model_classes:
									for size in [config.vocab_size - 10, config.vocab_size + 10]:
											# build the embeddings
											lowercase_ : str							       =   model_class(config=A   )
											lowercase_ : Any							       =   _get_word_embedding_weight(A    ,						model.get_input_embeddings()   )
											lowercase_ : List[Any]							       =   _get_word_embedding_weight(A    ,						model.get_output_embeddings()   )

											# reshape the embeddings
											model.resize_token_embeddings(A   )
											lowercase_ : Optional[Any]							       =   _get_word_embedding_weight(A    ,						model.get_input_embeddings()   )
											lowercase_ : Optional[int]							       =   _get_word_embedding_weight(A    ,						model.get_output_embeddings()   )

											# check that the resized embeddings size matches the desired size.
											lowercase_ : List[Any]							       =   size if size is not None else config.vocab_size

											self.assertEqual(new_input_embeddings.shape[0]    ,						A   )

											# check that weights remain the same after resizing
											lowercase_ : Optional[int]							       =   True
											for pa, pa in zip(old_input_embeddings.value()    ,						new_input_embeddings.value()   ):
													if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
															lowercase_ : Any							       =   False
											self.assertTrue(A   )

											if old_output_embeddings is not None and new_output_embeddings is not None:
													self.assertEqual(new_output_embeddings.shape[0]    ,						A   )

													lowercase_ : Any							       =   True
													for pa, pa in zip(old_output_embeddings.value()    ,						new_output_embeddings.value()   ):
															if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																	lowercase_ : Dict							       =   False
													self.assertTrue(A   )



def 						lowercase    (				__snake_case				: int	):
		return tf.constant(__snake_case		,					dtype=tf.intaa	)





@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   99




					def   A  (				self     :						Union[str, Any]   )  ->  str:
							lowercase_ : Union[str, Any]							       =   tf.ones((4, 1)    ,						dtype=tf.intaa   ) * 2
							lowercase_ : Optional[Any]							       =   tf.concat([ids_tensor((4, 6)    ,						self.vocab_size - 3   ) + 3, eos_column_vector]    ,						axis=1   )
							lowercase_ : Dict							       =   input_ids.shape[0]
							lowercase_ : Optional[int]							       =   OPTConfig(
							    vocab_size=self.vocab_size    ,						hidden_size=24    ,						num_hidden_layers=2    ,						num_attention_heads=2    ,						ffn_dim=32    ,						max_position_embeddings=48    ,						eos_token_id=2    ,						pad_token_id=1    ,						bos_token_id=0    ,						)
							return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						Any   )  ->  Union[str, Any]:
							lowercase_ : int							       =   TFOPTModel.from_pretrained('''facebook/opt-350m'''   )
							lowercase_ : List[str]							       =   _long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
							lowercase_ : Dict							       =   tf.not_equal(A    ,						model.config.pad_token_id   )
							with tf.GradientTape():
									lowercase_ : str							       =   model(input_ids=A    ,						attention_mask=A   ).last_hidden_state
							lowercase_ : Union[str, Any]							       =   (1, 11, 5_12)
							self.assertEqual(output.shape    ,						A   )
							lowercase_ : Union[str, Any]							       =   tf.constant(
							    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
							self.assertTrue(np.allclose(output[:, :3, :3]    ,						A    ,						atol=4e-3   )   )

							lowercase_ : Dict							       =   tf.function(A    ,						jit_compile=A   )
							lowercase_ : Union[str, Any]							       =   xla_generate(A    ,						A   )[0]
							self.assertTrue(np.allclose(output[:, :3, :3]    ,						A    ,						atol=4e-2   )   )





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




					def   A  (				self     :						List[str]   )  ->  Any:
							super().setUp()
							lowercase_ : Tuple							       =   '''facebook/opt-350m'''






					def   A  (				self     :						Any   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   TFOPTForCausalLM.from_pretrained(self.path_model   )
							lowercase_ : str							       =   GPTaTokenizer.from_pretrained(self.path_model   )

							lowercase_ : Any							       =   [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							lowercase_ : str							       =   tokenizer(A    ,						return_tensors='''tf'''    ,						padding=A    ,						add_special_tokens=A   )
							lowercase_ : str							       =   tf.math.reduce_mean(model(inputs.input_ids    ,						attention_mask=inputs.attention_mask   )[0]    ,						axis=-1   )
							lowercase_ : Any							       =   tf.constant(
							    [
							        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
							        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
							        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
							        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
							    ]   )
							self.assertTrue(np.allclose(A    ,						A    ,						atol=1e-4   )   )

							lowercase_ : str							       =   tf.function(A    ,						jit_compile=A   )
							lowercase_ : Dict							       =   tf.math.reduce_mean(xla_generate(inputs.input_ids    ,						attention_mask=inputs.attention_mask   )[0]    ,						axis=-1   )
							self.assertTrue(np.allclose(A    ,						A    ,						atol=1e-4   )   )





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




					@property
					def   A  (				self     :						Optional[int]   )  ->  Any:
							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]




					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : str							       =   '''facebook/opt-125m'''

							lowercase_ : int							       =   [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							lowercase_ : Tuple							       =   []
							lowercase_ : Optional[int]							       =   GPTaTokenizer.from_pretrained(A   )
							lowercase_ : Any							       =   TFOPTForCausalLM.from_pretrained(A   )

							for prompt in self.prompts:
									lowercase_ : int							       =   tokenizer(A    ,						return_tensors='''tf'''   ).input_ids

									lowercase_ : Any							       =   model.generate(A    ,						max_length=10   )

									lowercase_ : List[Any]							       =   tokenizer.batch_decode(A    ,						skip_special_tokens=A   )
									predicted_outputs += generated_string

							self.assertListEqual(A    ,						A   )




					def   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							lowercase_ : int							       =   '''facebook/opt-350m'''

							lowercase_ : List[str]							       =   GPTaTokenizer.from_pretrained(A   )
							lowercase_ : Tuple							       =   TFOPTForCausalLM.from_pretrained(A   )

							lowercase_ : Any							       =   '''left'''

							# use different length sentences to test batching
							lowercase_ : Union[str, Any]							       =   [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							lowercase_ : List[str]							       =   tokenizer(A    ,						return_tensors='''tf'''    ,						padding=A   )
							lowercase_ : List[Any]							       =   inputs['''input_ids''']

							lowercase_ : List[Any]							       =   model.generate(input_ids=A    ,						attention_mask=inputs['''attention_mask''']   )

							lowercase_ : str							       =   tokenizer(sentences[0]    ,						return_tensors='''tf'''   ).input_ids
							lowercase_ : Dict							       =   model.generate(input_ids=A   )

							lowercase_ : Any							       =   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['''attention_mask'''][-1]    ,						tf.intaa   )   )
							lowercase_ : Dict							       =   tokenizer(sentences[1]    ,						return_tensors='''tf'''   ).input_ids
							lowercase_ : List[str]							       =   model.generate(input_ids=A    ,						max_length=model.config.max_length - num_paddings   )

							lowercase_ : Optional[int]							       =   tokenizer.batch_decode(A    ,						skip_special_tokens=A   )
							lowercase_ : List[Any]							       =   tokenizer.decode(output_non_padded[0]    ,						skip_special_tokens=A   )
							lowercase_ : Tuple							       =   tokenizer.decode(output_padded[0]    ,						skip_special_tokens=A   )

							lowercase_ : Tuple							       =   [
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(A    ,						A   )
							self.assertListEqual(A    ,						[non_padded_sentence, padded_sentence]   )






					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							lowercase_ : Any							       =   '''facebook/opt-350m'''

							lowercase_ : Optional[Any]							       =   [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							lowercase_ : List[Any]							       =   []
							lowercase_ : Dict							       =   GPTaTokenizer.from_pretrained(A   )
							lowercase_ : Tuple							       =   TFOPTForCausalLM.from_pretrained(A   )

							for prompt in self.prompts:
									lowercase_ : Optional[Any]							       =   tokenizer(A    ,						return_tensors='''tf'''   ).input_ids

									lowercase_ : Dict							       =   model.generate(A    ,						max_length=10   )

									lowercase_ : Optional[Any]							       =   tokenizer.batch_decode(A    ,						skip_special_tokens=A   )
									predicted_outputs += generated_string

							self.assertListEqual(A    ,						A   )











"""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 YolosImageProcessor





class 					_UpperCAmelCase       ( unittest.TestCase							):




					def __init__(				self     :						List[Any]    ,						A     :						Any    ,						A     :						Tuple=7    ,						A     :						Tuple=3    ,						A     :						Optional[Any]=30    ,						A     :						List[Any]=4_00    ,						A     :						Tuple=True    ,						A     :						Dict=None    ,						A     :						List[str]=True    ,						A     :						Optional[int]=[0.5, 0.5, 0.5]    ,						A     :						Tuple=[0.5, 0.5, 0.5]    ,						A     :						List[str]=True    ,						A     :						List[Any]=1 / 2_55    ,						A     :						Union[str, Any]=True    ,						)  ->  Tuple:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							lowercase_ : Optional[int]							       =   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
							lowercase_ : Optional[int]							       =   parent
							lowercase_ : str							       =   batch_size
							lowercase_ : Tuple							       =   num_channels
							lowercase_ : str							       =   min_resolution
							lowercase_ : Any							       =   max_resolution
							lowercase_ : str							       =   do_resize
							lowercase_ : Any							       =   size
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   image_mean
							lowercase_ : Optional[Any]							       =   image_std
							lowercase_ : int							       =   do_rescale
							lowercase_ : List[str]							       =   rescale_factor
							lowercase_ : int							       =   do_pad




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

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

							return expected_height, expected_width





@require_torch
@require_vision
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   YolosImageProcessor if is_vision_available() else None




					def   A  (				self     :						Optional[int]   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   YolosImageProcessingTester(self   )




					@property
					def   A  (				self     :						str   )  ->  Any:
							return self.image_processor_tester.prepare_image_processor_dict()




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : Tuple							       =   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   A  (				self     :						Dict   )  ->  Tuple:
							lowercase_ : Dict							       =   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    ,						A   )

							lowercase_ : Tuple							       =   self.image_processing_class.from_dict(
							    self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=A   )
							self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42, '''longest_edge''': 84}   )
							self.assertEqual(image_processor.do_pad    ,						A   )




					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							pass




					def   A  (				self     :						Tuple   )  ->  int:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowercase_ : str							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						Image.Image   )

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

							lowercase_       ,					lowercase_ : 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
							lowercase_       ,					lowercase_ : Dict							       =   self.image_processor_tester.get_expected_values(A    ,						batched=A   )

							lowercase_ : str							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,						)




					def   A  (				self     :						str   )  ->  Any:
							# Initialize image_processing
							lowercase_ : Any							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowercase_ : List[Any]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						numpify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						np.ndarray   )

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

							lowercase_       ,					lowercase_ : 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
							lowercase_ : Optional[int]							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

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

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




					def   A  (				self     :						Tuple   )  ->  Optional[int]:
							# Initialize image_processing
							lowercase_ : Dict							       =   self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowercase_ : Optional[int]							       =   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
							lowercase_ : List[str]							       =   image_processing(image_inputs[0]    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : 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
							lowercase_ : Any							       =   image_processing(A    ,						return_tensors='''pt'''   ).pixel_values

							lowercase_       ,					lowercase_ : List[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   A  (				self     :						Tuple   )  ->  Optional[Any]:
							# Initialize image_processings
							lowercase_ : Optional[Any]							       =   self.image_processing_class(**self.image_processor_dict   )
							lowercase_ : Tuple							       =   self.image_processing_class(do_resize=A    ,						do_normalize=A    ,						do_rescale=A   )
							# create random PyTorch tensors
							lowercase_ : Union[str, Any]							       =   prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=A    ,						torchify=A   )
							for image in image_inputs:
									self.assertIsInstance(A    ,						torch.Tensor   )

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

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




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

							lowercase_ : Tuple							       =   {'''image_id''': 3_97_69, '''annotations''': target}

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

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

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

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






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

							lowercase_ : int							       =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

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

							# encode them
							lowercase_ : int							       =   YolosImageProcessor(format='''coco_panoptic'''   )
							lowercase_ : Any							       =   image_processing(images=A    ,						annotations=A    ,						masks_path=A    ,						return_tensors='''pt'''   )

							# verify pixel values
							lowercase_ : Optional[Any]							       =   torch.Size([1, 3, 8_00, 10_66]   )
							self.assertEqual(encoding['''pixel_values'''].shape    ,						A   )

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

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



