





'''simple docstring'''




import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase__       : Optional[int]         =       data_utils.TransfoXLTokenizer
lowercase__       : str         =       data_utils.TransfoXLCorpus
lowercase__       : Union[str, Any]         =       data_utils
lowercase__       : int         =       data_utils






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Dict					,				__snake_case    :					List[Any]     )	->	Optional[Any]:
     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__snake_case					,				'rb'     ) as fp:
               __A		:					Union[str, Any]						=			pickle.load(__snake_case					,				encoding='latin1'     )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          __A		:					Any						=			pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
          print(f'Save vocabulary to {pytorch_vocab_dump_path}'     )
          __A		:					Optional[Any]						=			corpus.vocab.__dict__
          torch.save(__snake_case					,				__snake_case     )

          __A		:					List[str]						=			corpus.__dict__
          corpus_dict_no_vocab.pop('vocab'					,				__snake_case     )
          __A		:					Dict						=			pytorch_dump_folder_path + '/' + CORPUS_NAME
          print(f'Save dataset to {pytorch_dataset_dump_path}'     )
          torch.save(__snake_case					,				__snake_case     )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          __A		:					Any						=			os.path.abspath(__snake_case     )
          __A		:					List[Any]						=			os.path.abspath(__snake_case     )

          print(f'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'     )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               __A		:					List[str]						=			TransfoXLConfig()
          else:
               __A		:					str						=			TransfoXLConfig.from_json_file(__snake_case     )
          print(f'Building PyTorch model from configuration: {config}'     )
          __A		:					Any						=			TransfoXLLMHeadModel(__snake_case     )

          __A		:					Optional[Any]						=			load_tf_weights_in_transfo_xl(__snake_case					,				__snake_case					,				__snake_case     )
          # Save pytorch-model
          __A		:					Dict						=			os.path.join(__snake_case					,				__snake_case     )
          __A		:					Optional[int]						=			os.path.join(__snake_case					,				__snake_case     )
          print(f'Save PyTorch model to {os.path.abspath(__snake_case     )}'     )
          torch.save(model.state_dict()					,				__snake_case     )
          print(f'Save configuration file to {os.path.abspath(__snake_case     )}'     )
          with open(__snake_case					,				'w'					,				encoding='utf-8'     ) as f:
               f.write(config.to_json_string()     )


if __name__ == "__main__":
  lowercase__       : List[Any]         =       argparse.ArgumentParser()
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''',
      default='''''',
      type=str,
      help='''An optional path to a TensorFlow checkpoint path to be converted.''',
  )
  parser.add_argument(
      '''--transfo_xl_config_file''',
      default='''''',
      type=str,
      help=(
          '''An optional config json file corresponding to the pre-trained BERT model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--transfo_xl_dataset_file''',
      default='''''',
      type=str,
      help='''An optional dataset file to be converted in a vocabulary.''',
  )
  lowercase__       : int         =       parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )






'''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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=[30, 30]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=10		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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

          __A		:					Any						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.expected_seq_len, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					str						=			model(pixel_values=_UpperCAmelCase)
          __A		:					List[str]						=			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		:					Union[str, Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     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		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[Any]						=			super()._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)

          if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                    __A		:					Any						=			[]
                    for i in range(self.model_tester.batch_size):
                         __A		:					Tuple						=			{}
                         __A		:					Tuple						=			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		:					str						=			labels

          return inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			YolosModelTester(self)
          __A		:					Dict						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

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

               __A		:					List[str]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			True

          # in YOLOS, the seq_len is different
          __A		:					Dict						=			self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
               __A		:					Dict						=			True
               __A		:					Dict						=			False
               __A		:					Union[str, Any]						=			True
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					Any						=			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		:					List[Any]						=			True
               __A		:					List[str]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Optional[Any]						=			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		:					Dict						=			True
               __A		:					Dict						=			True
               __A		:					Dict						=			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]						=			1
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Optional[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.hidden_states

               __A		:					List[str]						=			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		:					Dict						=			self.model_tester.expected_seq_len

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

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

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			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[int]						=			True

               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					List[Any]						=			YolosModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	int:
     __A		:					Dict						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     return image







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


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)

          __A		:					Any						=			self.default_image_processor
          __A		:					str						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

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

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

          __A		:					Dict						=			torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		,      device=_UpperCAmelCase		,      )
          __A		:					int						=			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[str]						=			image_processor.post_process_object_detection(
              _UpperCAmelCase		,      threshold=0.3		,      target_sizes=[image.size[::-1]])[0]
          __A		:					Optional[int]						=			torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			[75, 75, 17, 63, 17]
          __A		:					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))