





import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""		)
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}]							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									pass


							@slow
							@require_torch
							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""laion/clap-htsat-unfused"""							,				)
									# This is an audio of a dog
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)

									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									        {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									    ]							,				)

									SCREAMING_SNAKE_CASE__			=      audio_classifier([audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)
									SCREAMING_SNAKE_CASE__			=      audio_classifier(
									    [audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]							,				batch_size=5		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :str		)   ->   Optional[int]:



									"""simple docstring"""



									pass






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
_lowerCamelCase   				=			data_utils.TransfoXLTokenizer
_lowerCamelCase   				=			data_utils.TransfoXLCorpus
_lowerCamelCase   				=			data_utils
_lowerCamelCase   				=			data_utils






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

				SCREAMING_SNAKE_CASE__			=      corpus.__dict__
				corpus_dict_no_vocab.pop("""vocab"""					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      pytorch_dump_folder_path + """/""" + CORPUS_NAME
				print(f'''Save dataset to {pytorch_dataset_dump_path}'''    )
				torch.save(UpperCamelCase__					,   UpperCamelCase__    )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.abspath(UpperCamelCase__    )

				print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig()
				else:
						SCREAMING_SNAKE_CASE__			=      TransfoXLConfig.from_json_file(UpperCamelCase__    )
				print(f'''Building PyTorch model from configuration: {config}'''    )
				SCREAMING_SNAKE_CASE__			=      TransfoXLLMHeadModel(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      load_tf_weights_in_transfo_xl(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
				# Save pytorch-model
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      os.path.join(UpperCamelCase__					,   UpperCamelCase__    )
				print(f'''Save PyTorch model to {os.path.abspath(UpperCamelCase__    )}'''    )
				torch.save(model.state_dict()					,   UpperCamelCase__    )
				print(f'''Save configuration file to {os.path.abspath(UpperCamelCase__    )}'''    )
				with open(UpperCamelCase__					,   """w"""					,   encoding="""utf-8"""    ) as f:
						f.write(config.to_json_string()    )


if __name__ == "__main__":
	_lowerCamelCase   				=			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.',
	)
	_lowerCamelCase   				=			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,
	)