



'''simple docstring'''

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


__lowerCAmelCase		     =  {
    '''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 	__lowerCamelCase	( lowerCAmelCase_    )     -> List[Any]:
		_a	: Optional[Any]								=				['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase_		,     lowerCAmelCase_    )


__lowerCAmelCase		     =  {
    '''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 	__lowerCamelCase	( lowerCAmelCase_    )     -> Optional[int]:
		_a	: List[Any]								=				list(s_dict.keys()    )
		for key in keys:
				_a	: str								=				key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								_a	: int								=				new_key.replace(lowerCAmelCase_		,     lowerCAmelCase_    )

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

				_a	: Tuple								=				s_dict.pop(lowerCAmelCase_    )
		return s_dict



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> Optional[Any]:
		_a			,   _a	: Optional[Any]								=				emb.weight.shape
		_a	: int								=				nn.Linear(lowerCAmelCase_		,     lowerCAmelCase_		,     bias=lowerCAmelCase_    )
		_a	: Any								=				emb.weight.data
		return lin_layer



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> bytes:
		os.makedirs(lowerCAmelCase_		,     exist_ok=lowerCAmelCase_    )
		_a	: str								=				os.path.basename(lowerCAmelCase_    )

		_a	: int								=				url.split('/'    )[-2]
		_a	: Tuple								=				os.path.join(lowerCAmelCase_		,     lowerCAmelCase_    )

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

		if os.path.isfile(lowerCAmelCase_    ):
				_a	: Union[str, Any]								=				open(lowerCAmelCase_		,     'rb'    ).read()
				if hashlib.shaaaa(lowerCAmelCase_    ).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(lowerCAmelCase_    ) as source, open(lowerCAmelCase_		,     'wb'    ) as output:
				with tqdm(
				    total=int(source.info().get('Content-Length'    )    )		,     ncols=80		,     unit='iB'		,     unit_scale=lowerCAmelCase_		,     unit_divisor=1024    ) as loop:
						while True:
								_a	: int								=				source.read(8192    )
								if not buffer:
										break

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

		_a	: str								=				open(lowerCAmelCase_		,     'rb'    ).read()
		if hashlib.shaaaa(lowerCAmelCase_    ).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 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> Optional[Any]:
		if ".pt" not in checkpoint_path:
				_a	: List[str]								=				_download(_MODELS[checkpoint_path]    )
		else:
				_a	: int								=				torch.load(lowerCAmelCase_		,     map_location='cpu'    )
		_a	: Optional[int]								=				original_checkpoint['dims']
		_a	: Any								=				original_checkpoint['model_state_dict']
		_a	: Tuple								=				state_dict['decoder.token_embedding.weight']
		remove_ignore_keys_(lowerCAmelCase_    )
		rename_keys(lowerCAmelCase_    )
		_a	: Tuple								=				True
		_a	: Dict								=				state_dict['decoder.layers.0.fc1.weight'].shape[0]

		_a	: List[Any]								=				WhisperConfig(
		    vocab_size=dimensions['n_vocab']		,     encoder_ffn_dim=lowerCAmelCase_		,     decoder_ffn_dim=lowerCAmelCase_		,     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']		,     )

		_a	: Union[str, Any]								=				WhisperForConditionalGeneration(lowerCAmelCase_    )
		_a			,   _a	: Optional[int]								=				model.model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )
		if len(lowerCAmelCase_    ) > 0 and not set(lowerCAmelCase_    ) <= {
		    "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:
				_a	: str								=				make_linear_from_emb(model.model.decoder.embed_tokens    )
		else:
				_a	: List[str]								=				proj_out_weights

		model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  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.''')
							__lowerCAmelCase		     =  parser.parse_args()

							convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				BigBirdForPreTraining(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(lowerCAmelCase_		,     lowerCAmelCase_		,     is_trivia_qa=lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
							)
							parser.add_argument(
							    '''--big_bird_config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''The config json file corresponding to the pre-trained BERT model. \n'''
							        '''This specifies the model architecture.'''
							    ),
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
							)




