import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A__           =							logging.getLogger(__name__)
@dataclass
class   _lowerCAmelCase		:
							_UpperCAmelCase						    =     4_2
							_UpperCAmelCase						    =     4_2
							_UpperCAmelCase						    =     4_2


@dataclass
class   _lowerCAmelCase		:
							_UpperCAmelCase						    =     4_2
							_UpperCAmelCase						    =     4_2
							_UpperCAmelCase						    =     None
							_UpperCAmelCase						    =     None


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'train'
							_UpperCAmelCase						    =     'dev'
							_UpperCAmelCase						    =     'test'


class   _lowerCAmelCase		:







							@staticmethod
							def 				snake_case  (				__snake_case     :    Optional[Any]	,					__snake_case     :    Optional[Any]		):
										raise NotImplementedError







							@staticmethod
							def 				snake_case  (				__snake_case     :    List[Any]		):
										raise NotImplementedError







							@staticmethod
							def 				snake_case  (				__snake_case     :    List[str]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    Any=False	,					__snake_case     :    Tuple="[CLS]"	,					__snake_case     :    str=1	,					__snake_case     :    str="[SEP]"	,					__snake_case     :    Optional[Any]=False	,					__snake_case     :    List[Any]=False	,					__snake_case     :    Union[str, Any]=0	,					__snake_case     :    Optional[int]=0	,					__snake_case     :    List[str]=-100	,					__snake_case     :    Union[str, Any]=0	,					__snake_case     :    int=True	,					):
										lowerCamelCase						:Union[str, Any]						  =     {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE		)}

										lowerCamelCase						:str						  =     []
										for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE		):
													if ex_index % 10000 == 0:
																logger.info('''Writing example %d of %d'''	,					_SCREAMING_SNAKE_CASE	,					len(_SCREAMING_SNAKE_CASE		)		)

													lowerCamelCase						:str						  =     []
													lowerCamelCase						:int						  =     []
													for word, label in zip(example.words	,					example.labels		):
																lowerCamelCase						:List[str]						  =     tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)

																# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																if len(_SCREAMING_SNAKE_CASE		) > 0:
																			tokens.extend(_SCREAMING_SNAKE_CASE		)
																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
													lowerCamelCase						:Optional[int]						  =     tokenizer.num_special_tokens_to_add()
													if len(_SCREAMING_SNAKE_CASE		) > max_seq_length - special_tokens_count:
																lowerCamelCase						:Optional[Any]						  =     tokens[: (max_seq_length - special_tokens_count)]
																lowerCamelCase						:List[str]						  =     label_ids[: (max_seq_length - special_tokens_count)]

													# The convention in BERT is:
													# (a) For sequence pairs:
													#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
													#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
													# (b) For single sequences:
													#  tokens:   [CLS] the dog is hairy . [SEP]
													#  type_ids:   0   0   0   0  0     0   0
													#
													# Where "type_ids" are used to indicate whether this is the first
													# sequence or the second sequence. The embedding vectors for `type=0` and
													# `type=1` were learned during pre-training and are added to the wordpiece
													# embedding vector (and position vector). This is not *strictly* necessary
													# since the [SEP] token unambiguously separates the sequences, but it makes
													# it easier for the model to learn the concept of sequences.
													#
													# For classification tasks, the first vector (corresponding to [CLS]) is
													# used as the "sentence vector". Note that this only makes sense because
													# the entire model is fine-tuned.
													tokens += [sep_token]
													label_ids += [pad_token_label_id]
													if sep_token_extra:
																# roberta uses an extra separator b/w pairs of sentences
																tokens += [sep_token]
																label_ids += [pad_token_label_id]
													lowerCamelCase						:int						  =     [sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE		)

													if cls_token_at_end:
																tokens += [cls_token]
																label_ids += [pad_token_label_id]
																segment_ids += [cls_token_segment_id]
													else:
																lowerCamelCase						:List[Any]						  =     [cls_token] + tokens
																lowerCamelCase						:str						  =     [pad_token_label_id] + label_ids
																lowerCamelCase						:Any						  =     [cls_token_segment_id] + segment_ids

													lowerCamelCase						:int						  =     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)

													# The mask has 1 for real tokens and 0 for padding tokens. Only real
													# tokens are attended to.
													lowerCamelCase						:Optional[Any]						  =     [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE		)

													# Zero-pad up to the sequence length.
													lowerCamelCase						:Tuple						  =     max_seq_length - len(_SCREAMING_SNAKE_CASE		)
													if pad_on_left:
																lowerCamelCase						:int						  =     ([pad_token] * padding_length) + input_ids
																lowerCamelCase						:Optional[int]						  =     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																lowerCamelCase						:Optional[int]						  =     ([pad_token_segment_id] * padding_length) + segment_ids
																lowerCamelCase						:int						  =     ([pad_token_label_id] * padding_length) + label_ids
													else:
																input_ids += [pad_token] * padding_length
																input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																segment_ids += [pad_token_segment_id] * padding_length
																label_ids += [pad_token_label_id] * padding_length

													assert len(_SCREAMING_SNAKE_CASE		) == max_seq_length
													assert len(_SCREAMING_SNAKE_CASE		) == max_seq_length
													assert len(_SCREAMING_SNAKE_CASE		) == max_seq_length
													assert len(_SCREAMING_SNAKE_CASE		) == max_seq_length

													if ex_index < 5:
																logger.info('''*** Example ***'''		)
																logger.info('''guid: %s'''	,					example.guid		)
																logger.info('''tokens: %s'''	,					''' '''.join([str(_SCREAMING_SNAKE_CASE		) for x in tokens]		)		)
																logger.info('''input_ids: %s'''	,					''' '''.join([str(_SCREAMING_SNAKE_CASE		) for x in input_ids]		)		)
																logger.info('''input_mask: %s'''	,					''' '''.join([str(_SCREAMING_SNAKE_CASE		) for x in input_mask]		)		)
																logger.info('''segment_ids: %s'''	,					''' '''.join([str(_SCREAMING_SNAKE_CASE		) for x in segment_ids]		)		)
																logger.info('''label_ids: %s'''	,					''' '''.join([str(_SCREAMING_SNAKE_CASE		) for x in label_ids]		)		)

													if "token_type_ids" not in tokenizer.model_input_names:
																lowerCamelCase						:Union[str, Any]						  =     None

													features.append(
													    InputFeatures(
													        input_ids=_SCREAMING_SNAKE_CASE	,					attention_mask=_SCREAMING_SNAKE_CASE	,					token_type_ids=_SCREAMING_SNAKE_CASE	,					label_ids=_SCREAMING_SNAKE_CASE		)		)
										return features


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset
			class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
										_UpperCAmelCase						    =     4_2
										_UpperCAmelCase						    =     nn.CrossEntropyLoss().ignore_index







										def __init__(				self     :    Any	,					__snake_case     :    Any	,					__snake_case     :    Optional[Any]	,					__snake_case     :    Any	,					__snake_case     :    str	,					__snake_case     :    List[Any]	,					__snake_case     :    Dict = None	,					__snake_case     :    Union[str, Any]=False	,					__snake_case     :    List[str] = Split.train	,					):
													lowerCamelCase						:List[Any]						  =     os.path.join(
													    _SCREAMING_SNAKE_CASE	,					'''cached_{}_{}_{}'''.format(mode.value	,					tokenizer.__class__.__name__	,					str(_SCREAMING_SNAKE_CASE		)		)	,					)

													# Make sure only the first process in distributed training processes the dataset,
													# and the others will use the cache.
													lowerCamelCase						:Dict						  =     cached_features_file + '''.lock'''
													with FileLock(_SCREAMING_SNAKE_CASE		):
																if os.path.exists(_SCREAMING_SNAKE_CASE		) and not overwrite_cache:
																			logger.info(F"Loading features from cached file {cached_features_file}"		)
																			lowerCamelCase						:Any						  =     torch.load(_SCREAMING_SNAKE_CASE		)
																else:
																			logger.info(F"Creating features from dataset file at {data_dir}"		)
																			lowerCamelCase						:int						  =     token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		)
																			# TODO clean up all this to leverage built-in features of tokenizers
																			lowerCamelCase						:List[Any]						  =     token_classification_task.convert_examples_to_features(
																			    _SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					cls_token_at_end=bool(model_type in ['''xlnet''']		)	,					cls_token=tokenizer.cls_token	,					cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0	,					sep_token=tokenizer.sep_token	,					sep_token_extra=_SCREAMING_SNAKE_CASE	,					pad_on_left=bool(tokenizer.padding_side == '''left'''		)	,					pad_token=tokenizer.pad_token_id	,					pad_token_segment_id=tokenizer.pad_token_type_id	,					pad_token_label_id=self.pad_token_label_id	,					)
																			logger.info(F"Saving features into cached file {cached_features_file}"		)
																			torch.save(self.features	,					_SCREAMING_SNAKE_CASE		)







										def __len__(				self     :    Tuple		):
													return len(self.features		)







										def __getitem__(				self     :    Optional[int]	,					__snake_case     :    Any		):
													return self.features[i]


if is_tf_available():
			import tensorflow as tf
			class   _lowerCAmelCase		:
										_UpperCAmelCase						    =     4_2
										_UpperCAmelCase						    =     -1_0_0







										def __init__(				self     :    List[str]	,					__snake_case     :    List[Any]	,					__snake_case     :    Any	,					__snake_case     :    Dict	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Optional[Any] = None	,					__snake_case     :    List[Any]=False	,					__snake_case     :    str = Split.train	,					):
													lowerCamelCase						:Union[str, Any]						  =     token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		)
													# TODO clean up all this to leverage built-in features of tokenizers
													lowerCamelCase						:List[Any]						  =     token_classification_task.convert_examples_to_features(
													    _SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					cls_token_at_end=bool(model_type in ['''xlnet''']		)	,					cls_token=tokenizer.cls_token	,					cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0	,					sep_token=tokenizer.sep_token	,					sep_token_extra=_SCREAMING_SNAKE_CASE	,					pad_on_left=bool(tokenizer.padding_side == '''left'''		)	,					pad_token=tokenizer.pad_token_id	,					pad_token_segment_id=tokenizer.pad_token_type_id	,					pad_token_label_id=self.pad_token_label_id	,					)

													def gen():
																for ex in self.features:
																			if ex.token_type_ids is None:
																						yield (
																						    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																						    ex.label_ids,
																						)
																			else:
																						yield (
																						    {
																						        "input_ids": ex.input_ids,
																						        "attention_mask": ex.attention_mask,
																						        "token_type_ids": ex.token_type_ids,
																						    },
																						    ex.label_ids,
																						)

													if "token_type_ids" not in tokenizer.model_input_names:
																lowerCamelCase						:int						  =     tf.data.Dataset.from_generator(
																    _SCREAMING_SNAKE_CASE	,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)	,					(
																        {'''input_ids''': tf.TensorShape([None]		), '''attention_mask''': tf.TensorShape([None]		)},
																        tf.TensorShape([None]		),
																    )	,					)
													else:
																lowerCamelCase						:List[str]						  =     tf.data.Dataset.from_generator(
																    _SCREAMING_SNAKE_CASE	,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)	,					(
																        {
																            '''input_ids''': tf.TensorShape([None]		),
																            '''attention_mask''': tf.TensorShape([None]		),
																            '''token_type_ids''': tf.TensorShape([None]		),
																        },
																        tf.TensorShape([None]		),
																    )	,					)







										def 				snake_case  (				self     :    Union[str, Any]		):
													lowerCamelCase						:Optional[Any]						  =     self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

													return self.dataset







										def __len__(				self     :    Union[str, Any]		):
													return len(self.features		)







										def __getitem__(				self     :    Optional[int]	,					__snake_case     :    List[Any]		):
													return self.features[i]





import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import YolosForObjectDetection, YolosModel
			from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor
class   _lowerCAmelCase		:







							def __init__(				self     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    int=13	,					__snake_case     :    str=[30, 30]	,					__snake_case     :    Tuple=2	,					__snake_case     :    Optional[Any]=3	,					__snake_case     :    int=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    List[Any]=32	,					__snake_case     :    int=5	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    str="gelu"	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Union[str, Any]=10	,					__snake_case     :    str=0.0_2	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    List[str]=8	,					__snake_case     :    Any=10	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:Any						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Any						  =     num_channels
										lowerCamelCase						:List[Any]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:List[Any]						  =     num_hidden_layers
										lowerCamelCase						:List[str]						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[str]						  =     hidden_act
										lowerCamelCase						:List[str]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     type_sequence_label_size
										lowerCamelCase						:Optional[int]						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     num_labels
										lowerCamelCase						:Any						  =     scope
										lowerCamelCase						:Union[str, Any]						  =     n_targets
										lowerCamelCase						:Optional[Any]						  =     num_detection_tokens
										# we set the expected sequence length (which is used in several tests)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
										lowerCamelCase						:Tuple						  =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase						:str						  =     num_patches + 1 + self.num_detection_tokens







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:str						  =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

										lowerCamelCase						:List[str]						  =     None
										if self.use_labels:
													# labels is a list of Dict (each Dict being the labels for a given example in the batch)
													lowerCamelCase						:Optional[int]						  =     []
													for i in range(self.batch_size		):
																lowerCamelCase						:List[str]						  =     {}
																lowerCamelCase						:Tuple						  =     torch.randint(
																    high=self.num_labels	,					size=(self.n_targets,)	,					device=__snake_case		)
																lowerCamelCase						:List[str]						  =     torch.rand(self.n_targets	,					4	,					device=__snake_case		)
																labels.append(__snake_case		)

										lowerCamelCase						:str						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Union[str, Any]		):
										return YolosConfig(
										    image_size=self.image_size	,					patch_size=self.patch_size	,					num_channels=self.num_channels	,					hidden_size=self.hidden_size	,					num_hidden_layers=self.num_hidden_layers	,					num_attention_heads=self.num_attention_heads	,					intermediate_size=self.intermediate_size	,					hidden_act=self.hidden_act	,					hidden_dropout_prob=self.hidden_dropout_prob	,					attention_probs_dropout_prob=self.attention_probs_dropout_prob	,					is_decoder=__snake_case	,					initializer_range=self.initializer_range	,					num_detection_tokens=self.num_detection_tokens	,					num_labels=self.num_labels	,					)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Any		):
										lowerCamelCase						:Optional[Any]						  =     YolosModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Union[str, Any]						  =     model(__snake_case		)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,					(self.batch_size, self.expected_seq_len, self.hidden_size)		)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]		):
										lowerCamelCase						:int						  =     YolosForObjectDetection(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:str						  =     model(pixel_values=__snake_case		)
										lowerCamelCase						:Any						  =     model(__snake_case		)

										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)

										lowerCamelCase						:int						  =     model(pixel_values=__snake_case	,					labels=__snake_case		)

										self.parent.assertEqual(result.loss.shape	,					()		)
										self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
										self.parent.assertEqual(result.pred_boxes.shape	,					(self.batch_size, self.num_detection_tokens, 4)		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     config_and_inputs
										lowerCamelCase						:Union[str, Any]						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Dict=False		):
										lowerCamelCase						:Optional[int]						  =     super()._prepare_for_class(__snake_case	,					__snake_case	,					return_labels=__snake_case		)

										if return_labels:
													if model_class.__name__ == "YolosForObjectDetection":
																lowerCamelCase						:Dict						  =     []
																for i in range(self.model_tester.batch_size		):
																			lowerCamelCase						:Optional[Any]						  =     {}
																			lowerCamelCase						:List[Any]						  =     torch.ones(
																			    size=(self.model_tester.n_targets,)	,					device=__snake_case	,					dtype=torch.long		)
																			lowerCamelCase						:str						  =     torch.ones(
																			    self.model_tester.n_targets	,					4	,					device=__snake_case	,					dtype=torch.float		)
																			labels.append(__snake_case		)
																lowerCamelCase						:Union[str, Any]						  =     labels

										return inputs_dict







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     YolosModelTester(self		)
										lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[Any]		):
										# YOLOS does not use inputs_embeds
										pass







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)
													self.assertIsInstance(model.get_input_embeddings()	,					(nn.Module)		)
													lowerCamelCase						:str						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													lowerCamelCase						:Any						  =     inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase						:Tuple						  =     [*signature.parameters.keys()]

													lowerCamelCase						:Tuple						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:int						  =     True

										# in YOLOS, the seq_len is different
										lowerCamelCase						:str						  =     self.model_tester.expected_seq_len
										for model_class in self.all_model_classes:
													lowerCamelCase						:str						  =     True
													lowerCamelCase						:Tuple						  =     False
													lowerCamelCase						:Optional[int]						  =     True
													lowerCamelCase						:int						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:str						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:str						  =     outputs.attentions
													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase						:Optional[Any]						  =     True
													lowerCamelCase						:str						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:Tuple						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)
													lowerCamelCase						:Tuple						  =     outputs.attentions
													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_len, seq_len]	,					)
													lowerCamelCase						:Optional[int]						  =     len(__snake_case		)

													# Check attention is always last and order is fine
													lowerCamelCase						:Union[str, Any]						  =     True
													lowerCamelCase						:List[Any]						  =     True
													lowerCamelCase						:Tuple						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:int						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Dict						  =     1
													self.assertEqual(out_len + added_hidden_states	,					len(__snake_case		)		)

													lowerCamelCase						:Dict						  =     outputs.attentions

													self.assertEqual(len(__snake_case		)	,					self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, seq_len, seq_len]	,					)







							def 				snake_case  (				self     :    List[str]		):
										def check_hidden_states_output(__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple		):
													lowerCamelCase						:Union[str, Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Any						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Optional[Any]						  =     outputs.hidden_states

													lowerCamelCase						:Any						  =     getattr(
													    self.model_tester	,					'''expected_num_hidden_layers'''	,					self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(__snake_case		)	,					__snake_case		)

													# YOLOS has a different seq_length
													lowerCamelCase						:List[str]						  =     self.model_tester.expected_seq_len

													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]		)	,					[seq_length, self.model_tester.hidden_size]	,					)

										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Union[str, Any]						  =     True
													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase						:Any						  =     True

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[Any]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Dict		):
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     YolosModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


@require_torch
@require_vision
class   _lowerCAmelCase		(     unittest.TestCase				):







							@cached_property
							def 				snake_case  (				self     :    Tuple		):
										return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     self.default_image_processor
										lowerCamelCase						:str						  =     prepare_img()
										lowerCamelCase						:Dict						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

										# forward pass
										with torch.no_grad():
													lowerCamelCase						:Optional[Any]						  =     model(inputs.pixel_values		)

										# verify outputs
										lowerCamelCase						:int						  =     torch.Size((1, 100, 92)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						:Any						  =     torch.tensor(
										    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]	,					device=__snake_case	,					)
										lowerCamelCase						:Any						  =     torch.tensor(
										    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]	,					device=__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)

										# verify postprocessing
										lowerCamelCase						:List[str]						  =     image_processor.post_process_object_detection(
										    __snake_case	,					threshold=0.3	,					target_sizes=[image.size[::-1]]		)[0]
										lowerCamelCase						:List[str]						  =     torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(__snake_case		)
										lowerCamelCase						:str						  =     [75, 75, 17, 63, 17]
										lowerCamelCase						:Tuple						  =     torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(__snake_case		)

										self.assertEqual(len(results['''scores''']		)	,					5		)
										self.assertTrue(torch.allclose(results['''scores''']	,					__snake_case	,					atol=1e-4		)		)
										self.assertSequenceEqual(results['''labels'''].tolist()	,					__snake_case		)
										self.assertTrue(torch.allclose(results['''boxes'''][0, :]	,					__snake_case		)		)



