


"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  MODEL_FOR_CAUSAL_LM_MAPPING
 __lowerCAmelCase			 =  TF_MODEL_FOR_CAUSAL_LM_MAPPING


 @require_torch
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=pipeline(task='''text-generation'''			,   model='''sshleifer/tiny-ctrl'''			,   framework='''pt'''						)
     # Using `do_sample=False` to force deterministic output
     a					=text_generator('''This is a test'''			,   do_sample=__A						)
     self.assertEqual(
         __A			,   [
             {
                 '''generated_text''': (
                     '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                     ''' oscope. FiliFili@@'''
                 )
             }
         ]			,   )

     a					=text_generator(['''This is a test''', '''This is a second test''']						)
     self.assertEqual(
         __A			,   [
             [
                 {
                     '''generated_text''': (
                         '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                         ''' oscope. FiliFili@@'''
                     )
                 }
             ],
             [
                 {
                     '''generated_text''': (
                         '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                         ''' oscope. oscope. FiliFili@@'''
                     )
                 }
             ],
         ]			,   )

     a					=text_generator('''This is a test'''			,   do_sample=__A			,   num_return_sequences=2			,   return_tensors=__A						)
     self.assertEqual(
         __A			,   [
             {'''generated_token_ids''': ANY(__A						)},
             {'''generated_token_ids''': ANY(__A						)},
         ]			,   )
     a					=text_generator.model.config.eos_token_id
     a					='''<pad>'''
     a					=text_generator(
         ['''This is a test''', '''This is a second test''']			,   do_sample=__A			,   num_return_sequences=2			,   batch_size=2			,   return_tensors=__A			,   )
     self.assertEqual(
         __A			,   [
             [
                 {'''generated_token_ids''': ANY(__A						)},
                 {'''generated_token_ids''': ANY(__A						)},
             ],
             [
                 {'''generated_token_ids''': ANY(__A						)},
                 {'''generated_token_ids''': ANY(__A						)},
             ],
         ]			,   )


 @require_tf
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=pipeline(task='''text-generation'''			,   model='''sshleifer/tiny-ctrl'''			,   framework='''tf'''						)

     # Using `do_sample=False` to force deterministic output
     a					=text_generator('''This is a test'''			,   do_sample=__A						)
     self.assertEqual(
         __A			,   [
             {
                 '''generated_text''': (
                     '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                     ''' please,'''
                 )
             }
         ]			,   )

     a					=text_generator(['''This is a test''', '''This is a second test''']			,   do_sample=__A						)
     self.assertEqual(
         __A			,   [
             [
                 {
                     '''generated_text''': (
                         '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                         ''' please,'''
                     )
                 }
             ],
             [
                 {
                     '''generated_text''': (
                         '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                         ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                     )
                 }
             ],
         ]			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[str]:
     a					=TextGenerationPipeline(model=__A			,   tokenizer=__A						)
     return text_generator, ["This is a test", "Another test"]


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					='''Hello I believe in'''
     a					=pipeline('''text-generation'''			,   model='''hf-internal-testing/tiny-random-gpt2'''						)
     a					=text_generator(__A						)
     self.assertEqual(
         __A			,   [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]			,   )

     a					=text_generator(__A			,   stop_sequence=''' fe'''						)
     self.assertEqual(__A			,   [{'''generated_text''': '''Hello I believe in fe'''}]						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					str:
     a					=text_generator.model
     a					=text_generator.tokenizer

     a					=text_generator('''This is a test'''						)
     self.assertEqual(__A			,   [{'''generated_text''': ANY(__A						)}]						)
     self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''						)						)

     a					=text_generator('''This is a test'''			,   return_full_text=__A						)
     self.assertEqual(__A			,   [{'''generated_text''': ANY(__A						)}]						)
     self.assertNotIn('''This is a test'''			,   outputs[0]['''generated_text''']						)

     a					=pipeline(task='''text-generation'''			,   model=__A			,   tokenizer=__A			,   return_full_text=__A						)
     a					=text_generator('''This is a test'''						)
     self.assertEqual(__A			,   [{'''generated_text''': ANY(__A						)}]						)
     self.assertNotIn('''This is a test'''			,   outputs[0]['''generated_text''']						)

     a					=text_generator('''This is a test'''			,   return_full_text=__A						)
     self.assertEqual(__A			,   [{'''generated_text''': ANY(__A						)}]						)
     self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''						)						)

     a					=text_generator(['''This is great !''', '''Something else''']			,   num_return_sequences=2			,   do_sample=__A						)
     self.assertEqual(
         __A			,   [
             [{'''generated_text''': ANY(__A						)}, {'''generated_text''': ANY(__A						)}],
             [{'''generated_text''': ANY(__A						)}, {'''generated_text''': ANY(__A						)}],
         ]			,   )

     if text_generator.tokenizer.pad_token is not None:
         a					=text_generator(
             ['''This is great !''', '''Something else''']			,   num_return_sequences=2			,   batch_size=2			,   do_sample=__A						)
         self.assertEqual(
             __A			,   [
                 [{'''generated_text''': ANY(__A						)}, {'''generated_text''': ANY(__A						)}],
                 [{'''generated_text''': ANY(__A						)}, {'''generated_text''': ANY(__A						)}],
             ]			,   )

     with self.assertRaises(__A						):
         a					=text_generator('''test'''			,   return_full_text=__A			,   return_text=__A						)
     with self.assertRaises(__A						):
         a					=text_generator('''test'''			,   return_full_text=__A			,   return_tensors=__A						)
     with self.assertRaises(__A						):
         a					=text_generator('''test'''			,   return_text=__A			,   return_tensors=__A						)

     # Empty prompt is slighly special
     # it requires BOS token to exist.
     # Special case for Pegasus which will always append EOS so will
     # work even without BOS.
     if (
         text_generator.tokenizer.bos_token_id is not None
         or "Pegasus" in tokenizer.__class__.__name__
         or "Git" in model.__class__.__name__
     ):
         a					=text_generator(''''''						)
         self.assertEqual(__A			,   [{'''generated_text''': ANY(__A						)}]						)
     else:
         with self.assertRaises((ValueError, AssertionError)						):
             a					=text_generator(''''''						)

     if text_generator.framework == "tf":
         # TF generation does not support max_new_tokens, and it's impossible
         # to control long generation with only max_length without
         # fancy calculation, dismissing tests for now.
         return
     # We don't care about infinite range models.
     # They already work.
     # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
     a					=['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
     if (
         tokenizer.model_max_length < 1_0000
         and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
     ):
         # Handling of large generations
         with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)						):
             text_generator('''This is a test''' * 500			,   max_new_tokens=20						)

         a					=text_generator('''This is a test''' * 500			,   handle_long_generation='''hole'''			,   max_new_tokens=20						)
         # Hole strategy cannot work
         with self.assertRaises(__A						):
             text_generator(
                 '''This is a test''' * 500			,   handle_long_generation='''hole'''			,   max_new_tokens=tokenizer.model_max_length + 10			,   )


 @require_torch
 @require_accelerate
 @require_torch_gpu
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     import torch

     # Classic `model_kwargs`
     a					=pipeline(
         model='''hf-internal-testing/tiny-random-bloom'''			,   model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}			,   )
     self.assertEqual(pipe.model.device			,   torch.device(0						)						)
     self.assertEqual(pipe.model.lm_head.weight.dtype			,   torch.bfloataa						)
     a					=pipe('''This is a test'''						)
     self.assertEqual(
         __A			,   [
             {
                 '''generated_text''': (
                     '''This is a test test test test test test test test test test test test test test test test'''
                     ''' test'''
                 )
             }
         ]			,   )

     # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
     a					=pipeline(model='''hf-internal-testing/tiny-random-bloom'''			,   device_map='''auto'''			,   torch_dtype=torch.bfloataa						)
     self.assertEqual(pipe.model.device			,   torch.device(0						)						)
     self.assertEqual(pipe.model.lm_head.weight.dtype			,   torch.bfloataa						)
     a					=pipe('''This is a test'''						)
     self.assertEqual(
         __A			,   [
             {
                 '''generated_text''': (
                     '''This is a test test test test test test test test test test test test test test test test'''
                     ''' test'''
                 )
             }
         ]			,   )

     # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
     a					=pipeline(model='''hf-internal-testing/tiny-random-bloom'''			,   device_map='''auto'''						)
     self.assertEqual(pipe.model.device			,   torch.device(0						)						)
     self.assertEqual(pipe.model.lm_head.weight.dtype			,   torch.floataa						)
     a					=pipe('''This is a test'''						)
     self.assertEqual(
         __A			,   [
             {
                 '''generated_text''': (
                     '''This is a test test test test test test test test test test test test test test test test'''
                     ''' test'''
                 )
             }
         ]			,   )


 @require_torch
 @require_torch_gpu
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     import torch

     a					=pipeline(model='''hf-internal-testing/tiny-random-bloom'''			,   device=0			,   torch_dtype=torch.floataa						)
     pipe('''This is a test'''						)


 @require_torch
 @require_accelerate
 @require_torch_gpu
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     import torch

     a					=pipeline(model='''hf-internal-testing/tiny-random-bloom'''			,   device_map='''auto'''			,   torch_dtype=torch.floataa						)
     pipe('''This is a test'''			,   do_sample=__A			,   top_p=0.5						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					='''Hello world'''
     a					=pipeline('''text-generation'''			,   model='''hf-internal-testing/tiny-random-gpt2'''						)
     if text_generator.model.framework == "tf":
         a					=logging.get_logger('''transformers.generation.tf_utils'''						)
     else:
         a					=logging.get_logger('''transformers.generation.utils'''						)
     a					='''Both `max_new_tokens`'''  # The beggining of the message to be checked in this test

     # Both are set by the user -> log warning
     with CaptureLogger(__A						) as cl:
         a					=text_generator(__A			,   max_length=10			,   max_new_tokens=1						)
     self.assertIn(__A			,   cl.out						)

     # The user only sets one -> no warning
     with CaptureLogger(__A						) as cl:
         a					=text_generator(__A			,   max_new_tokens=1						)
     self.assertNotIn(__A			,   cl.out						)

     with CaptureLogger(__A						) as cl:
         a					=text_generator(__A			,   max_length=10						)
     self.assertNotIn(__A			,   cl.out						)



"""simple docstring"""







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 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=13			,   __A=[30, 30]			,   __A=2			,   __A=3			,   __A=True			,   __A=True			,   __A=32			,   __A=5			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=10			,   __A=0.02			,   __A=3			,   __A=None			,   __A=8			,   __A=10			,   ) ->					List[Any]:
     a					=parent
     a					=batch_size
     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=is_training
     a					=use_labels
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=type_sequence_label_size
     a					=initializer_range
     a					=num_labels
     a					=scope
     a					=n_targets
     a					=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
     a					=(image_size[1] // patch_size) * (image_size[0] // patch_size)
     a					=num_patches + 1 + self.num_detection_tokens


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

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

     a					=self.get_config()

     return config, pixel_values, labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     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=__A			,   initializer_range=self.initializer_range			,   num_detection_tokens=self.num_detection_tokens			,   num_labels=self.num_labels			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=YolosModel(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)
     self.parent.assertEqual(
         result.last_hidden_state.shape			,   (self.batch_size, self.expected_seq_len, self.hidden_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Dict:
     a					=YolosForObjectDetection(__A						)
     model.to(__A						)
     model.eval()

     a					=model(pixel_values=__A						)
     a					=model(__A						)

     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)						)

     a					=model(pixel_values=__A			,   labels=__A						)

     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 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.prepare_config_and_inputs()
     a		,				a		,				a					=config_and_inputs
     a					={'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 __lowerCAmelCase			 =  (
     {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
 )

 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=False						) ->					Any:
     a					=super()._prepare_for_class(__A			,   __A			,   return_labels=__A						)

     if return_labels:
         if model_class.__name__ == "YolosForObjectDetection":
             a					=[]
             for i in range(self.model_tester.batch_size						):
                 a					={}
                 a					=torch.ones(
                     size=(self.model_tester.n_targets,)			,   device=__A			,   dtype=torch.long						)
                 a					=torch.ones(
                     self.model_tester.n_targets			,   4			,   device=__A			,   dtype=torch.float						)
                 labels.append(__A						)
             a					=labels

     return inputs_dict


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=YolosModelTester(self						)
     a					=ConfigTester(self			,   config_class=__A			,   has_text_modality=__A			,   hidden_size=37						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self.config_tester.run_common_tests()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     # YOLOS does not use inputs_embeds
     pass


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

     for model_class in self.all_model_classes:
         a					=model_class(__A						)
         self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module)						)
         a					=model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__A			,   nn.Linear						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

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

         a					=['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()
     a					=True

     # in YOLOS, the seq_len is different
     a					=self.model_tester.expected_seq_len
     for model_class in self.all_model_classes:
         a					=True
         a					=False
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)
         a					=outputs.attentions
         self.assertEqual(len(__A						)			,   self.model_tester.num_hidden_layers						)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)
         a					=outputs.attentions
         self.assertEqual(len(__A						)			,   self.model_tester.num_hidden_layers						)

         self.assertListEqual(
             list(attentions[0].shape[-3:]						)			,   [self.model_tester.num_attention_heads, seq_len, seq_len]			,   )
         a					=len(__A						)

         # Check attention is always last and order is fine
         a					=True
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)

         a					=1
         self.assertEqual(out_len + added_hidden_states			,   len(__A						)						)

         a					=outputs.attentions

         self.assertEqual(len(__A						)			,   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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     def check_hidden_states_output(__A			,   __A			,   __A						):
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()

         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)

         a					=outputs.hidden_states

         a					=getattr(
             self.model_tester			,   '''expected_num_hidden_layers'''			,   self.model_tester.num_hidden_layers + 1						)
         self.assertEqual(len(__A						)			,   __A						)

         # YOLOS has a different seq_length
         a					=self.model_tester.expected_seq_len

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

     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         a					=True
         check_hidden_states_output(__A			,   __A			,   __A						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a					=True

         check_hidden_states_output(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_object_detection(*__A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a					=YolosModel.from_pretrained(__A						)
         self.assertIsNotNone(__A						)

def _A						(						):







    """simple docstring"""
    a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






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






 """simple docstring"""




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





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''						).to(__A						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=__A			,   return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
         a					=model(inputs.pixel_values						)

     # verify outputs
     a					=torch.Size((1, 100, 92)						)
     self.assertEqual(outputs.logits.shape			,   __A						)

     a					=torch.tensor(
         [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			,   device=__A			,   )
     a					=torch.tensor(
         [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]			,   device=__A						)
     self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,   __A			,   atol=1E-4						)						)
     self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,   __A			,   atol=1E-4						)						)

     # verify postprocessing
     a					=image_processor.post_process_object_detection(
         __A			,   threshold=0.3			,   target_sizes=[image.size[::-1]]						)[0]
     a					=torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]						).to(__A						)
     a					=[75, 75, 17, 63, 17]
     a					=torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]						).to(__A						)

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