




'''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 		lowerCAmelCase_     :







  '''simple docstring'''

  def __init__(      self							:				Optional[Any] ,   SCREAMING_SNAKE_CASE_							:				int ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=13 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=[30, 30] ,   SCREAMING_SNAKE_CASE_							:				Tuple=2 ,   SCREAMING_SNAKE_CASE_							:				str=3 ,   SCREAMING_SNAKE_CASE_							:				List[str]=True ,   SCREAMING_SNAKE_CASE_							:				Tuple=True ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=32 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=5 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=4 ,   SCREAMING_SNAKE_CASE_							:				Tuple=37 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]="gelu" ,   SCREAMING_SNAKE_CASE_							:				List[str]=0.1 ,   SCREAMING_SNAKE_CASE_							:				Optional[int]=0.1 ,   SCREAMING_SNAKE_CASE_							:				List[str]=10 ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=0.02 ,   SCREAMING_SNAKE_CASE_							:				List[str]=3 ,   SCREAMING_SNAKE_CASE_							:				Optional[Any]=None ,   SCREAMING_SNAKE_CASE_							:				int=8 ,   SCREAMING_SNAKE_CASE_							:				List[Any]=10 ,   )       ->						Tuple:







       '''simple docstring'''



       A:						str						=						parent
       A:						Union[str, Any]						=						batch_size
       A:						List[Any]						=						image_size
       A:						Tuple						=						patch_size
       A:						List[Any]						=						num_channels
       A:						List[Any]						=						is_training
       A:						str						=						use_labels
       A:						Any						=						hidden_size
       A:						List[Any]						=						num_hidden_layers
       A:						List[str]						=						num_attention_heads
       A:						Optional[int]						=						intermediate_size
       A:						Dict						=						hidden_act
       A:						Optional[Any]						=						hidden_dropout_prob
       A:						Any						=						attention_probs_dropout_prob
       A:						Tuple						=						type_sequence_label_size
       A:						int						=						initializer_range
       A:						Union[str, Any]						=						num_labels
       A:						Optional[int]						=						scope
       A:						List[str]						=						n_targets
       A:						int						=						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:						List[str]						=						(image_size[1] // patch_size) * (image_size[0] // patch_size)
       A:						str						=						num_patches + 1 + self.num_detection_tokens



  def  _snake_case			(      self							:				Tuple     )       ->						Any:







       '''simple docstring'''



       A:						Union[str, Any]						=						floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]     )

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

       A:						Tuple						=						self.get_config()

       return config, pixel_values, labels



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







       '''simple docstring'''



       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=UpperCamelCase__ ,   initializer_range=self.initializer_range ,   num_detection_tokens=self.num_detection_tokens ,   num_labels=self.num_labels ,   )



  def  _snake_case			(      self							:				str ,   SCREAMING_SNAKE_CASE_							:				str ,   SCREAMING_SNAKE_CASE_							:				List[str] ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]     )       ->						List[Any]:







       '''simple docstring'''



       A:						List[str]						=						YolosModel(config=UpperCamelCase__     )
       model.to(UpperCamelCase__     )
       model.eval()
       A:						Optional[int]						=						model(UpperCamelCase__     )
       self.parent.assertEqual(
           result.last_hidden_state.shape ,   (self.batch_size, self.expected_seq_len, self.hidden_size)     )



  def  _snake_case			(      self							:				str ,   SCREAMING_SNAKE_CASE_							:				int ,   SCREAMING_SNAKE_CASE_							:				int ,   SCREAMING_SNAKE_CASE_							:				str     )       ->						List[str]:







       '''simple docstring'''



       A:						Tuple						=						YolosForObjectDetection(UpperCamelCase__     )
       model.to(UpperCamelCase__     )
       model.eval()

       A:						Union[str, Any]						=						model(pixel_values=UpperCamelCase__     )
       A:						Tuple						=						model(UpperCamelCase__     )

       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:						Optional[int]						=						model(pixel_values=UpperCamelCase__ ,   labels=UpperCamelCase__     )

       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							:				List[Any]     )       ->						int:







       '''simple docstring'''



       A:						Optional[Any]						=						self.prepare_config_and_inputs()
       A			,  A			,  A:						Union[str, Any]						=						config_and_inputs
       A:						int						=						{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 		lowerCAmelCase_     (    __UpperCAmelCase    , __UpperCAmelCase    , unittest.TestCase				):







  '''simple docstring'''

  UpperCamelCase_       :	Optional[int]				       =							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
  UpperCamelCase_       :	Any				       =							(
      {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
  )

  UpperCamelCase_       :	Tuple				       =							False
  UpperCamelCase_       :	Tuple				       =							False
  UpperCamelCase_       :	List[Any]				       =							False
  UpperCamelCase_       :	Union[str, Any]				       =							False



  def  _snake_case			(      self							:				Dict ,   SCREAMING_SNAKE_CASE_							:				int ,   SCREAMING_SNAKE_CASE_							:				List[Any] ,   SCREAMING_SNAKE_CASE_							:				List[str]=False     )       ->						Optional[int]:







       '''simple docstring'''



       A:						Optional[Any]						=						super()._prepare_for_class(UpperCamelCase__ ,   UpperCamelCase__ ,   return_labels=UpperCamelCase__     )

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

       return inputs_dict



  def  _snake_case			(      self							:				str     )       ->						Dict:







       '''simple docstring'''



       A:						Any						=						YolosModelTester(self     )
       A:						Union[str, Any]						=						ConfigTester(self ,   config_class=UpperCamelCase__ ,   has_text_modality=UpperCamelCase__ ,   hidden_size=37     )



  def  _snake_case			(      self							:				Tuple     )       ->						Tuple:







       '''simple docstring'''



       self.config_tester.run_common_tests()



  def  _snake_case			(      self							:				int     )       ->						Dict:







       '''simple docstring'''



       pass



  def  _snake_case			(      self							:				str     )       ->						str:







       '''simple docstring'''



       A			,  A:						List[str]						=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:						Dict						=						model_class(UpperCamelCase__     )
            self.assertIsInstance(model.get_input_embeddings() ,   (nn.Module)     )
            A:						Union[str, Any]						=						model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(UpperCamelCase__ ,   nn.Linear     )     )



  def  _snake_case			(      self							:				str     )       ->						Any:







       '''simple docstring'''



       A			,  A:						Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:						Union[str, Any]						=						model_class(UpperCamelCase__     )
            A:						Dict						=						inspect.signature(model.forward     )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A:						Optional[int]						=						[*signature.parameters.keys()]

            A:						Dict						=						['''pixel_values''']
            self.assertListEqual(arg_names[:1] ,   UpperCamelCase__     )



  def  _snake_case			(      self							:				Optional[int]     )       ->						Optional[Any]:







       '''simple docstring'''



       A:						List[str]						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__     )



  def  _snake_case			(      self							:				List[Any]     )       ->						List[Any]:







       '''simple docstring'''



       A			,  A:						Any						=						self.model_tester.prepare_config_and_inputs_for_common()
       A:						Optional[int]						=						True

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

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            A:						Dict						=						True
            A:						Union[str, Any]						=						model_class(UpperCamelCase__     )
            model.to(UpperCamelCase__     )
            model.eval()
            with torch.no_grad():
                 A:						Optional[Any]						=						model(**self._prepare_for_class(UpperCamelCase__ ,   UpperCamelCase__     )     )
            A:						int						=						outputs.attentions
            self.assertEqual(len(UpperCamelCase__     ) ,   self.model_tester.num_hidden_layers     )

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

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

            A:						str						=						1
            self.assertEqual(out_len + added_hidden_states ,   len(UpperCamelCase__     )     )

            A:						str						=						outputs.attentions

            self.assertEqual(len(UpperCamelCase__     ) ,   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							:				Tuple     )       ->						List[Any]:







       '''simple docstring'''



       def check_hidden_states_output(SCREAMING_SNAKE_CASE_							:				Any ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any] ,   SCREAMING_SNAKE_CASE_							:				str     ):
            A:						List[Any]						=						model_class(UpperCamelCase__     )
            model.to(UpperCamelCase__     )
            model.eval()

            with torch.no_grad():
                 A:						str						=						model(**self._prepare_for_class(UpperCamelCase__ ,   UpperCamelCase__     )     )

            A:						Optional[Any]						=						outputs.hidden_states

            A:						int						=						getattr(
                self.model_tester ,   '''expected_num_hidden_layers''' ,   self.model_tester.num_hidden_layers + 1     )
            self.assertEqual(len(UpperCamelCase__     ) ,   UpperCamelCase__     )

            # YOLOS has a different seq_length
            A:						int						=						self.model_tester.expected_seq_len

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

       A			,  A:						str						=						self.model_tester.prepare_config_and_inputs_for_common()

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

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

            check_hidden_states_output(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__     )



  def  _snake_case			(      self							:				Any     )       ->						Dict:







       '''simple docstring'''



       A:						str						=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_object_detection(*UpperCamelCase__     )




  @slow
  def  _snake_case			(      self							:				Any     )       ->						str:







       '''simple docstring'''



       for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A:						Optional[int]						=						YolosModel.from_pretrained(UpperCamelCase__     )
            self.assertIsNotNone(UpperCamelCase__     )






def  SCREAMING_SNAKE_CASE(					)       ->      Optional[Any]:
     A:						Dict						=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
     return image






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







  '''simple docstring'''

  @cached_property
  def  _snake_case			(      self							:				Optional[Any]     )       ->						Any:







       '''simple docstring'''



       return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''     ) if is_vision_available() else None




  @slow
  def  _snake_case			(      self							:				Union[str, Any]     )       ->						Union[str, Any]:







       '''simple docstring'''



       A:						Optional[Any]						=						YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''     ).to(UpperCamelCase__     )

       A:						Union[str, Any]						=						self.default_image_processor
       A:						str						=						prepare_img()
       A:						Any						=						image_processor(images=UpperCamelCase__ ,   return_tensors='''pt'''     ).to(UpperCamelCase__     )

       # forward pass
       with torch.no_grad():
            A:						Union[str, Any]						=						model(inputs.pixel_values     )

       # verify outputs
       A:						Tuple						=						torch.Size((1, 1_00, 92)     )
       self.assertEqual(outputs.logits.shape ,   UpperCamelCase__     )

       A:						Optional[Any]						=						torch.tensor(
           [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] ,   device=UpperCamelCase__ ,   )
       A:						Dict						=						torch.tensor(
           [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] ,   device=UpperCamelCase__     )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,   UpperCamelCase__ ,   atol=1E-4     )     )
       self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,   UpperCamelCase__ ,   atol=1E-4     )     )

       # verify postprocessing
       A:						List[Any]						=						image_processor.post_process_object_detection(
           UpperCamelCase__ ,   threshold=0.3 ,   target_sizes=[image.size[::-1]]     )[0]
       A:						str						=						torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]     ).to(UpperCamelCase__     )
       A:						Any						=						[75, 75, 17, 63, 17]
       A:						Optional[Any]						=						torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]     ).to(UpperCamelCase__     )

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












# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




