


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			Tuple=None      ):
						"""simple docstring"""




						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCamelCase     :      Any       						=						nn.Parameter(UpperCAmelCase      )
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCamelCase     :      Tuple       						=						nn.Parameter(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any      ):
						"""simple docstring"""




						__lowerCamelCase     :      Dict       						=						np.asarray(weights[0]      )
						__lowerCamelCase     :      Tuple       						=						np.asarray(weights[1]      )
						__lowerCamelCase     :      Tuple       						=						np.asarray(weights[2]      )

						set_param(
						    torch_layer.self_attention.query_key						,	torch.tensor(UpperCAmelCase      ).transpose(1						,	2      ).contiguous().view(-1						,	UpperCAmelCase      )						,	)
						set_param(
						    torch_layer.self_attention.value						,	torch.tensor(UpperCAmelCase      ).transpose(1						,	2      ).contiguous().view(-1						,	UpperCAmelCase      )						,	)
						set_param(
						    torch_layer.output.dense						,	torch.tensor(UpperCAmelCase      ).view(-1						,	UpperCAmelCase      ).contiguous().transpose(0						,	1      )						,	)





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
						"""simple docstring"""




						__lowerCamelCase     :      Any       						=						np.asarray(weights[0]      )
						__lowerCamelCase     :      List[Any]       						=						np.asarray(weights[1]      )
						__lowerCamelCase     :      Optional[int]       						=						np.asarray(weights[2]      )
						__lowerCamelCase     :      Tuple       						=						np.asarray(weights[3]      )

						set_param(
						    torch_layer.self_attention.query						,	torch.tensor(UpperCAmelCase      ).transpose(1						,	2      ).contiguous().view(-1						,	UpperCAmelCase      )						,	)
						set_param(
						    torch_layer.self_attention.key						,	torch.tensor(UpperCAmelCase      ).transpose(1						,	2      ).contiguous().view(-1						,	UpperCAmelCase      )						,	)
						set_param(
						    torch_layer.self_attention.value						,	torch.tensor(UpperCAmelCase      ).transpose(1						,	2      ).contiguous().view(-1						,	UpperCAmelCase      )						,	)
						set_param(
						    torch_layer.output.dense						,	torch.tensor(UpperCAmelCase      ).view(-1						,	UpperCAmelCase      ).contiguous().transpose(0						,	1      )						,	)





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
						"""simple docstring"""




						__lowerCamelCase     :      str       						=						weights[0][0][0]
						__lowerCamelCase     :      Union[str, Any]       						=						np.asarray(layer_norm_a[0]      )
						__lowerCamelCase     :      Tuple       						=						np.asarray(layer_norm_a[1]      )
						set_param(
						    torch_block.attention.layer_norm						,	torch.tensor(UpperCAmelCase      )						,	torch.tensor(UpperCAmelCase      )						,	)

						# lsh weights + output
						__lowerCamelCase     :      str       						=						weights[0][1]
						if len(UpperCAmelCase      ) < 4:
												set_layer_weights_in_torch_lsh(UpperCAmelCase						,	torch_block.attention						,	UpperCAmelCase      )
						else:
												set_layer_weights_in_torch_local(UpperCAmelCase						,	torch_block.attention						,	UpperCAmelCase      )

						# intermediate weighs
						__lowerCamelCase     :      Optional[Any]       						=						weights[2][0][1][2]

						# Chunked Feed Forward
						if len(UpperCAmelCase      ) == 4:
												__lowerCamelCase     :      List[str]       						=						intermediate_weights[2]

						# layernorm 2
						__lowerCamelCase     :      Optional[Any]       						=						np.asarray(intermediate_weights[0][0]      )
						__lowerCamelCase     :      Optional[int]       						=						np.asarray(intermediate_weights[0][1]      )
						set_param(
						    torch_block.feed_forward.layer_norm						,	torch.tensor(UpperCAmelCase      )						,	torch.tensor(UpperCAmelCase      )						,	)

						# intermediate dense
						__lowerCamelCase     :      Optional[Any]       						=						np.asarray(intermediate_weights[1][0]      )
						__lowerCamelCase     :      Optional[Any]       						=						np.asarray(intermediate_weights[1][1]      )
						set_param(
						    torch_block.feed_forward.dense.dense						,	torch.tensor(UpperCAmelCase      ).transpose(0						,	1      ).contiguous()						,	torch.tensor(UpperCAmelCase      )						,	)

						# intermediate out
						__lowerCamelCase     :      Dict       						=						np.asarray(intermediate_weights[4][0]      )
						__lowerCamelCase     :      List[Any]       						=						np.asarray(intermediate_weights[4][1]      )
						set_param(
						    torch_block.feed_forward.output.dense						,	torch.tensor(UpperCAmelCase      ).transpose(0						,	1      ).contiguous()						,	torch.tensor(UpperCAmelCase      )						,	)





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Tuple      ):
						"""simple docstring"""




						__lowerCamelCase     :      Tuple       						=						torch_model.reformer

						# word embeds
						__lowerCamelCase     :      Optional[Any]       						=						np.asarray(weights[1]      )
						set_param(
						    torch_model_reformer.embeddings.word_embeddings						,	torch.tensor(UpperCAmelCase      )						,	)

						if isinstance(weights[3]						,	UpperCAmelCase      ):
												__lowerCamelCase     :      int       						=						torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights      )      ):
																		__lowerCamelCase     :      List[str]       						=						np.asarray(weights[3][emb_idx][0]      )
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCamelCase     :      Optional[int]       						=						nn.Parameter(torch.tensor(UpperCAmelCase      )      )

						__lowerCamelCase     :      List[str]       						=						weights[5]
						assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
						    UpperCAmelCase      ), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
												__lowerCamelCase     :      Tuple       						=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

						# output layer norm
						__lowerCamelCase     :      Optional[Any]       						=						np.asarray(weights[7][0]      )
						__lowerCamelCase     :      str       						=						np.asarray(weights[7][1]      )
						set_param(
						    torch_model_reformer.encoder.layer_norm						,	torch.tensor(UpperCAmelCase      )						,	torch.tensor(UpperCAmelCase      )						,	)

						# output embeddings
						__lowerCamelCase     :      List[str]       						=						np.asarray(weights[9][0]      )
						__lowerCamelCase     :      str       						=						np.asarray(weights[9][1]      )
						set_param(
						    torch_model.lm_head.decoder						,	torch.tensor(UpperCAmelCase      ).transpose(0						,	1      ).contiguous()						,	torch.tensor(UpperCAmelCase      )						,	)





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			List[Any]      ):
						"""simple docstring"""




						__lowerCamelCase     :      Dict       						=						ReformerConfig.from_json_file(UpperCAmelCase      )
						print(f"""Building PyTorch model from configuration: {config}"""      )
						__lowerCamelCase     :      str       						=						ReformerModelWithLMHead(UpperCAmelCase      )

						with open(UpperCAmelCase						,	"""rb"""      ) as f:
												__lowerCamelCase     :      List[str]       						=						pickle.load(UpperCAmelCase      )["""weights"""]

						set_model_weights_in_torch(UpperCAmelCase						,	UpperCAmelCase						,	config.hidden_size      )

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""      )
						torch.save(model.state_dict()						,	UpperCAmelCase      )


if __name__ == "__main__":
				__UpperCamelCase    :						Dict     =							argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained Reformer 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.'
				)
				__UpperCamelCase    :						List[Any]     =							parser.parse_args()
				convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)







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


__UpperCamelCase    :						Tuple     =							{
    '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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase						,	UpperCAmelCase      )


__UpperCamelCase    :						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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						list(s_dict.keys()      )
      for key in keys:
            __lowerCamelCase     :      Union[str, Any]       						=						key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        __lowerCamelCase     :      Optional[int]       						=						new_key.replace(UpperCAmelCase						,	UpperCAmelCase      )

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

            __lowerCamelCase     :      Dict       						=						s_dict.pop(UpperCAmelCase      )
      return s_dict





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						emb.weight.shape
      __lowerCamelCase     :      Tuple       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						emb.weight.data
      return lin_layer





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      os.makedirs(UpperCAmelCase						,	exist_ok=UpperCAmelCase      )
      __lowerCamelCase     :      Any       						=						os.path.basename(UpperCAmelCase      )

      __lowerCamelCase     :      int       						=						url.split("""/"""      )[-2]
      __lowerCamelCase     :      str       						=						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     :      Any       						=						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=1_024      ) as loop:
                  while True:
                        __lowerCamelCase     :      Union[str, Any]       						=						source.read(8_192      )
                        if not buffer:
                              break

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

      __lowerCamelCase     :      List[str]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Any       						=						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     :      Tuple       						=						make_linear_from_emb(model.model.decoder.embed_tokens      )
      else:
            __lowerCamelCase     :      Tuple       						=						proj_out_weights

      model.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Union[str, Any]     =							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.')
    __UpperCamelCase    :						Tuple     =							parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



