
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


snake_case__					=				{
    """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_   (      UpperCAmelCase_				:					List[Any]							):
						lowercase							:     Optional[Any]									=	['layers', 'blocks']
						for k in ignore_keys:
												state_dict.pop(lowerCAmelCase_				,      lowerCAmelCase_							)


snake_case__					=				{
    """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_   (      UpperCAmelCase_				:					Optional[int]							):
						lowercase							:     List[Any]									=	list(s_dict.keys()							)
						for key in keys:
												lowercase							:     str									=	key
												for k, v in WHISPER_MAPPING.items():
																		if k in key:
																								lowercase							:     int									=	new_key.replace(lowerCAmelCase_				,      lowerCAmelCase_							)

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

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

def 							lowerCamelCase_   (      UpperCAmelCase_				:					List[Any]							):
						lowercase							:     Optional[Any]									=	emb.weight.shape
						lowercase							:     int									=	nn.Linear(lowerCAmelCase_				,      lowerCAmelCase_				,      bias=lowerCAmelCase_							)
						lowercase							:     Any									=	emb.weight.data
						return lin_layer

def 							lowerCamelCase_   (      UpperCAmelCase_				:					Union[str, Any]				,      UpperCAmelCase_				:					List[Any]							):
						os.makedirs(lowerCAmelCase_				,      exist_ok=lowerCAmelCase_							)
						lowercase							:     str									=	os.path.basename(lowerCAmelCase_							)

						lowercase							:     int									=	url.split('''/'''							)[-2]
						lowercase							:     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_							):
												lowercase							:     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=10_24							) as loop:
																		while True:
																								lowercase							:     int									=	source.read(81_92							)
																								if not buffer:
																														break

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

						lowercase							:     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_   (      UpperCAmelCase_				:					str				,      UpperCAmelCase_				:					Optional[int]							):
						if ".pt" not in checkpoint_path:
												lowercase							:     List[str]									=	_download(_MODELS[checkpoint_path]							)
						else:
												lowercase							:     int									=	torch.load(lowerCAmelCase_				,      map_location='''cpu'''							)
						lowercase							:     Optional[int]									=	original_checkpoint['dims']
						lowercase							:     Any									=	original_checkpoint['model_state_dict']
						lowercase							:     Tuple									=	state_dict['decoder.token_embedding.weight']
						remove_ignore_keys_(lowerCAmelCase_							)
						rename_keys(lowerCAmelCase_							)
						lowercase							:     Tuple									=	True
						lowercase							:     Dict									=	state_dict['decoder.layers.0.fc1.weight'].shape[0]

						lowercase							:     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''']				,      )

						lowercase							:     Union[str, Any]									=	WhisperForConditionalGeneration(lowerCAmelCase_							)
						lowercase							:     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:
												lowercase							:     str									=	make_linear_from_emb(model.model.decoder.embed_tokens							)
						else:
												lowercase							:     List[str]									=	proj_out_weights

						model.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
				snake_case__					=				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.""")
				snake_case__					=				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
							)




