



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class 				__A					( unittest.TestCase      ):





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		logging.get_logger()

						# the current default level is logging.WARNING
						lowerCamelCase__ :      Any								=		logging.get_verbosity()

						logging.set_verbosity_error()
						self.assertEqual(logger.getEffectiveLevel()							,    logging.get_verbosity()				)

						logging.set_verbosity_warning()
						self.assertEqual(logger.getEffectiveLevel()							,    logging.get_verbosity()				)

						logging.set_verbosity_info()
						self.assertEqual(logger.getEffectiveLevel()							,    logging.get_verbosity()				)

						logging.set_verbosity_debug()
						self.assertEqual(logger.getEffectiveLevel()							,    logging.get_verbosity()				)

						# restore to the original level
						logging.set_verbosity(__magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Union[str, Any]								=		logging.get_verbosity()

						lowerCamelCase__ :      Optional[Any]								=		logging.get_logger("""transformers.models.bart.tokenization_bart"""				)
						lowerCamelCase__ :      str								=		"""Testing 1, 2, 3"""

						# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
						if level_origin <= logging.WARNING:
									with CaptureLogger(__magic_name__				) as cl:
												logger.warning(__magic_name__				)
									self.assertEqual(cl.out							,    msg + """\n"""				)

						# this is setting the level for all of `transformers.*` loggers
						logging.set_verbosity_error()

						# should not be able to log warnings
						with CaptureLogger(__magic_name__				) as cl:
									logger.warning(__magic_name__				)
						self.assertEqual(cl.out							,    """"""				)

						# should be able to log warnings again
						logging.set_verbosity_warning()
						with CaptureLogger(__magic_name__				) as cl:
									logger.warning(__magic_name__				)
						self.assertEqual(cl.out							,    msg + """\n"""				)

						# restore to the original level
						logging.set_verbosity(__magic_name__				)





			@mockenv(TRANSFORMERS_VERBOSITY="""error"""				)
			def        _snake_case    (self				):
						# reset for the env var to take effect, next time some logger call is made
						transformers.utils.logging._reset_library_root_logger()
						# this action activates the env var
						lowerCamelCase__ :      int								=		logging.get_logger("""transformers.models.bart.tokenization_bart"""				)

						lowerCamelCase__ :      Optional[int]								=		os.getenv("""TRANSFORMERS_VERBOSITY"""							,    __magic_name__				)
						lowerCamelCase__ :      Any								=		logging.log_levels[env_level_str]

						lowerCamelCase__ :      Dict								=		logging.get_verbosity()
						self.assertEqual(
						    __magic_name__							,    __magic_name__							,    f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"							,    )

						# restore to the original level
						lowerCamelCase__ :      int								=		""""""
						transformers.utils.logging._reset_library_root_logger()





			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""				)
			def        _snake_case    (self				):
						# reset for the env var to take effect, next time some logger call is made
						transformers.utils.logging._reset_library_root_logger()
						lowerCamelCase__ :      str								=		logging.logging.getLogger()
						with CaptureLogger(__magic_name__				) as cl:
									# this action activates the env var
									logging.get_logger("""transformers.models.bart.tokenization_bart"""				)
						self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""							,    cl.out				)

						# no need to restore as nothing was changed





			def        _snake_case    (self				):
						# testing `logger.warning_advice()`
						transformers.utils.logging._reset_library_root_logger()

						lowerCamelCase__ :      Optional[Any]								=		logging.get_logger("""transformers.models.bart.tokenization_bart"""				)
						lowerCamelCase__ :      List[Any]								=		"""Testing 1, 2, 3"""

						with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""				):
									# nothing should be logged as env var disables this method
									with CaptureLogger(__magic_name__				) as cl:
												logger.warning_advice(__magic_name__				)
									self.assertEqual(cl.out							,    """"""				)

						with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""				):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(__magic_name__				) as cl:
												logger.warning_advice(__magic_name__				)
									self.assertEqual(cl.out							,    msg + """\n"""				)







def      _A				()  ->Any:



			'''simple docstring'''







			disable_progress_bar()
			assert are_progress_bars_disabled()

			enable_progress_bar()
			assert not are_progress_bars_disabled()








import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


_lowercase     							=						{
    '''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
    '''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
    '''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
    '''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
    '''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
    '''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
    '''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
    '''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
    '''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
    '''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}






def      _A				(UpperCamelCase		:      Any )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		["""layers""", """blocks"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase      ,			UpperCamelCase )


_lowercase     							=						{
    '''blocks''': '''layers''',
    '''mlp.0''': '''fc1''',
    '''mlp.2''': '''fc2''',
    '''mlp_ln''': '''final_layer_norm''',
    '''.attn.query''': '''.self_attn.q_proj''',
    '''.attn.key''': '''.self_attn.k_proj''',
    '''.attn.value''': '''.self_attn.v_proj''',
    '''.attn_ln''': '''.self_attn_layer_norm''',
    '''.attn.out''': '''.self_attn.out_proj''',
    '''.cross_attn.query''': '''.encoder_attn.q_proj''',
    '''.cross_attn.key''': '''.encoder_attn.k_proj''',
    '''.cross_attn.value''': '''.encoder_attn.v_proj''',
    '''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
    '''.cross_attn.out''': '''.encoder_attn.out_proj''',
    '''decoder.ln.''': '''decoder.layer_norm.''',
    '''encoder.ln.''': '''encoder.layer_norm.''',
    '''token_embedding''': '''embed_tokens''',
    '''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
    '''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
    '''ln_post''': '''layer_norm''',
}






def      _A				(UpperCamelCase		:      Union[str, Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		list(s_dict.keys() )
			for key in keys:
						lowerCamelCase__ :      Dict								=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowerCamelCase__ :      Tuple								=		new_key.replace(UpperCamelCase      ,			UpperCamelCase )

						print(f"{key} -> {new_key}" )

						lowerCamelCase__ :      int								=		s_dict.pop(UpperCamelCase )
			return s_dict






def      _A				(UpperCamelCase		:      List[Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      int								=		emb.weight.shape
			lowerCamelCase__ :      Optional[int]								=		nn.Linear(UpperCamelCase      ,			UpperCamelCase      ,			bias=UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		emb.weight.data
			return lin_layer






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->bytes:



			'''simple docstring'''







			os.makedirs(UpperCamelCase      ,			exist_ok=UpperCamelCase )
			lowerCamelCase__ :      Any								=		os.path.basename(UpperCamelCase )

			lowerCamelCase__ :      int								=		url.split("""/""" )[-2]
			lowerCamelCase__ :      Optional[int]								=		os.path.join(UpperCamelCase      ,			UpperCamelCase )

			if os.path.exists(UpperCamelCase ) and not os.path.isfile(UpperCamelCase ):
						raise RuntimeError(f"{download_target} exists and is not a regular file" )

			if os.path.isfile(UpperCamelCase ):
						lowerCamelCase__ :      Dict								=		open(UpperCamelCase      ,			"""rb""" ).read()
						if hashlib.shaaaa(UpperCamelCase ).hexdigest() == expected_shaaaa:
									return model_bytes
						else:
									warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )

			with urllib.request.urlopen(UpperCamelCase ) as source, open(UpperCamelCase      ,			"""wb""" ) as output:
						with tqdm(
						    total=int(source.info().get("""Content-Length""" ) )      ,			ncols=80      ,			unit="""iB"""      ,			unit_scale=UpperCamelCase      ,			unit_divisor=1024 ) as loop:
									while True:
												lowerCamelCase__ :      Tuple								=		source.read(8192 )
												if not buffer:
															break

												output.write(UpperCamelCase )
												loop.update(len(UpperCamelCase ) )

			lowerCamelCase__ :      Any								=		open(UpperCamelCase      ,			"""rb""" ).read()
			if hashlib.shaaaa(UpperCamelCase ).hexdigest() != expected_shaaaa:
						raise RuntimeError(
						    """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" )

			return model_bytes






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







			if ".pt" not in checkpoint_path:
						lowerCamelCase__ :      Optional[Any]								=		_download(_MODELS[checkpoint_path] )
			else:
						lowerCamelCase__ :      Optional[Any]								=		torch.load(UpperCamelCase      ,			map_location="""cpu""" )
			lowerCamelCase__ :      Optional[int]								=		original_checkpoint["""dims"""]
			lowerCamelCase__ :      int								=		original_checkpoint["""model_state_dict"""]
			lowerCamelCase__ :      Optional[int]								=		state_dict["""decoder.token_embedding.weight"""]
			remove_ignore_keys_(UpperCamelCase )
			rename_keys(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		True
			lowerCamelCase__ :      str								=		state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

			lowerCamelCase__ :      List[str]								=		WhisperConfig(
			    vocab_size=dimensions["""n_vocab"""]      ,			encoder_ffn_dim=UpperCamelCase      ,			decoder_ffn_dim=UpperCamelCase      ,			num_mel_bins=dimensions["""n_mels"""]      ,			d_model=dimensions["""n_audio_state"""]      ,			max_target_positions=dimensions["""n_text_ctx"""]      ,			encoder_layers=dimensions["""n_audio_layer"""]      ,			encoder_attention_heads=dimensions["""n_audio_head"""]      ,			decoder_layers=dimensions["""n_text_layer"""]      ,			decoder_attention_heads=dimensions["""n_text_state"""]      ,			max_source_positions=dimensions["""n_audio_ctx"""]      ,			)

			lowerCamelCase__ :      List[str]								=		WhisperForConditionalGeneration(UpperCamelCase )
			lowerCamelCase__							,lowerCamelCase__ :      str								=		model.model.load_state_dict(UpperCamelCase      ,			strict=UpperCamelCase )
			if len(UpperCamelCase ) > 0 and not set(UpperCamelCase ) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
						    f" but all the following weights are missing {missing}" )

			if tie_embeds:
						lowerCamelCase__ :      List[str]								=		make_linear_from_emb(model.model.decoder.embed_tokens )
			else:
						lowerCamelCase__ :      int								=		proj_out_weights

			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				# # Required parameters
				parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				_lowercase     							=						parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



