





"""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


A   : str			=				{
    """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 (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       ['''layers''', '''blocks''']
		for k in ignore_keys:
				state_dict.pop(_snake_case   ,					_snake_case   )


A   : str			=				{
    """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 (							__a       :List[str]   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       list(s_dict.keys()   )
		for key in keys:
				A__  				=       key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								A__  				=       new_key.replace(_snake_case   ,					_snake_case   )

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

				A__  				=       s_dict.pop(_snake_case   )
		return s_dict







def        __lowerCamelCase (							__a       :int   )		->    str:






		"""simple docstring"""

		A__  				=       emb.weight.shape
		A__  				=       nn.Linear(_snake_case   ,					_snake_case   ,					bias=_snake_case   )
		A__  				=       emb.weight.data
		return lin_layer







def        __lowerCamelCase (							__a       :str   ,					__a       :str   )		->    bytes:






		"""simple docstring"""

		os.makedirs(_snake_case   ,					exist_ok=_snake_case   )
		A__  				=       os.path.basename(_snake_case   )

		A__  				=       url.split("""/"""   )[-2]
		A__  				=       os.path.join(_snake_case   ,					_snake_case   )

		if os.path.exists(_snake_case   ) and not os.path.isfile(_snake_case   ):
				raise RuntimeError(F'{download_target} exists and is not a regular file'   )

		if os.path.isfile(_snake_case   ):
				A__  				=       open(_snake_case   ,					"""rb"""   ).read()
				if hashlib.shaaaa(_snake_case   ).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(_snake_case   ) as source, open(_snake_case   ,					"""wb"""   ) as output:
				with tqdm(
				    total=int(source.info().get("""Content-Length"""   )   )   ,					ncols=8_0   ,					unit="""iB"""   ,					unit_scale=_snake_case   ,					unit_divisor=1_0_2_4   ) as loop:
						while True:
								A__  				=       source.read(8_1_9_2   )
								if not buffer:
										break

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

		A__  				=       open(_snake_case   ,					"""rb"""   ).read()
		if hashlib.shaaaa(_snake_case   ).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 (							__a       :Any   ,					__a       :int   )		->    str:






		"""simple docstring"""

		if ".pt" not in checkpoint_path:
				A__  				=       _download(_MODELS[checkpoint_path]   )
		else:
				A__  				=       torch.load(_snake_case   ,					map_location="""cpu"""   )
		A__  				=       original_checkpoint['''dims''']
		A__  				=       original_checkpoint['''model_state_dict''']
		A__  				=       state_dict['''decoder.token_embedding.weight''']
		remove_ignore_keys_(_snake_case   )
		rename_keys(_snake_case   )
		A__  				=       True
		A__  				=       state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

		A__  				=       WhisperConfig(
		    vocab_size=dimensions["""n_vocab"""]   ,					encoder_ffn_dim=_snake_case   ,					decoder_ffn_dim=_snake_case   ,					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__  				=       WhisperForConditionalGeneration(_snake_case   )
		A__  				=       model.model.load_state_dict(_snake_case   ,					strict=_snake_case   )
		if len(_snake_case   ) > 0 and not set(_snake_case   ) <= {
		    "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__  				=       make_linear_from_emb(model.model.decoder.embed_tokens   )
		else:
				A__  				=       proj_out_weights

		model.save_pretrained(_snake_case   )


if __name__ == "__main__":
					A   : List[str]			=				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.''')
					A   : List[str]			=				parser.parse_args()

					convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



