import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		os.path.join(args.tf_model_dir  ,      '''parameters.json'''     )
    __UpperCamelCase					:Optional[int]        =		json.loads(open(SCREAMING_SNAKE_CASE     ).read()     )
    if not params:
        raise ValueError(
            f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""     )
    if not args.output.endswith('''.pt'''     ):
        __UpperCamelCase					:List[str]        =		args.output + '''.pt'''
    __UpperCamelCase					:Optional[Any]        =		OrderedDict()
    with tf.device('''/CPU:0'''     ):
        __UpperCamelCase					:str        =		tf.train.load_checkpoint(args.tf_model_dir     )
        __UpperCamelCase					:int        =		reader.get_variable_to_shape_map()
        for key_name in shapes.keys():
            __UpperCamelCase					:int        =		reader.get_tensor(SCREAMING_SNAKE_CASE     ).astype(np.floataa     )
            if key_name.endswith('''/adam_m'''     ) or key_name.endswith('''/adam_v'''     ):
                continue
            if key_name.startswith('''pasts/'''     ):
                if key_name.startswith('''pasts/mlp'''     ):
                    __UpperCamelCase					:Optional[Any]        =		int(key_name[9]     )
                elif key_name.startswith('''pasts/out'''     ):
                    __UpperCamelCase					:str        =		8
                __UpperCamelCase					:Optional[int]        =		'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                __UpperCamelCase					:List[str]        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                __UpperCamelCase					:Optional[Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/moe'''     ):
                __UpperCamelCase					:Optional[int]        =		int(key_name[9:].split('''/'''     )[0]     )
                if key_name.endswith('''/switch_gating/kernel'''     ):
                    __UpperCamelCase					:Dict        =		'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                    __UpperCamelCase					:Any        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:Tuple        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/softmlp/kernel'''     ):
                    __UpperCamelCase					:Union[str, Any]        =		'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                    __UpperCamelCase					:Optional[Any]        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:List[str]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/wo/kernel'''     ) or key_name.endswith('''/wi/kernel'''     ):
                    __UpperCamelCase					:List[Any]        =		key_name[-9:-7]
                    for i in range(16     ):
                        __UpperCamelCase					:Optional[Any]        =		'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                        __UpperCamelCase					:int        =		(
                            vnp[i].transpose([1, 0]     ).copy()
                        )  # In Mesh-Tensorflow, it is one array, so it is divided
                        __UpperCamelCase					:Tuple        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/mlp'''     ):
                __UpperCamelCase					:Union[str, Any]        =		int(key_name[9:].split('''/'''     )[0]     )
                if key_name.endswith('''/p1/kernel'''     ):
                    __UpperCamelCase					:List[str]        =		'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                    __UpperCamelCase					:Any        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:int        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/p1/bias'''     ):
                    __UpperCamelCase					:int        =		'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                    __UpperCamelCase					:List[str]        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:Dict        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/p2/kernel'''     ):
                    __UpperCamelCase					:Any        =		'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                    __UpperCamelCase					:str        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:Dict        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/p2/bias'''     ):
                    __UpperCamelCase					:Tuple        =		'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                    __UpperCamelCase					:Any        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:Optional[int]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/ln'''     ):
                __UpperCamelCase					:Union[str, Any]        =		int(key_name[8:].split('''/'''     )[0]     )
                if key_name.endswith('''/b'''     ):
                    __UpperCamelCase					:int        =		'''model.blocks.%d.feed_forward.norm.bias''' % player
                    __UpperCamelCase					:Optional[int]        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:List[str]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/g'''     ):
                    __UpperCamelCase					:str        =		'''model.blocks.%d.feed_forward.norm.weight''' % player
                    __UpperCamelCase					:Optional[Any]        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:List[str]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/att'''     ):
                __UpperCamelCase					:Dict        =		int(key_name[9:].split('''/'''     )[0]     )
                if key_name.endswith('''/qkv/kernel'''     ):
                    __UpperCamelCase					:Optional[Any]        =		vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                    __UpperCamelCase					:str        =		state[:, 0, :, :]
                    __UpperCamelCase					:Optional[int]        =		state[:, 1, :, :]
                    __UpperCamelCase					:int        =		state[:, 2, :, :]
                    __UpperCamelCase					:Optional[int]        =		(
                        state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]     )
                        .transpose([1, 0]     )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:List[Any]        =		(
                        state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]     )
                        .transpose([1, 0]     )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:Any        =		(
                        state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]     )
                        .transpose([1, 0]     )
                        .copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:str        =		'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                    __UpperCamelCase					:Optional[Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                    __UpperCamelCase					:Union[str, Any]        =		'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                    __UpperCamelCase					:Dict        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                    __UpperCamelCase					:Optional[Any]        =		'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                    __UpperCamelCase					:List[Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/o/kernel'''     ):
                    __UpperCamelCase					:List[Any]        =		'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                    __UpperCamelCase					:List[Any]        =		(
                        vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]     ).transpose([1, 0]     ).copy()
                    )  # Mesh-Tensorflow is a diagonal matrix
                    __UpperCamelCase					:List[str]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/an'''     ):
                __UpperCamelCase					:Tuple        =		int(key_name[8:].split('''/'''     )[0]     )
                if key_name.endswith('''/b'''     ):
                    __UpperCamelCase					:List[str]        =		'''model.blocks.%d.self_attn.norm.bias''' % player
                    __UpperCamelCase					:Optional[int]        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:Union[str, Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                elif key_name.endswith('''/g'''     ):
                    __UpperCamelCase					:int        =		'''model.blocks.%d.self_attn.norm.weight''' % player
                    __UpperCamelCase					:Any        =		vnp.copy()  # same because it is one dimensional
                    __UpperCamelCase					:Tuple        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif (
                key_name.startswith('''model/wte'''     )
                or key_name.startswith('''model/wpe'''     )
                or key_name.startswith('''model/ete'''     )
            ):
                __UpperCamelCase					:Union[str, Any]        =		{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                    key_name[-3:]
                ]
                __UpperCamelCase					:Any        =		'''model.%s.weight''' % nlayer
                __UpperCamelCase					:List[Any]        =		vnp.copy()  # same in embedded
                __UpperCamelCase					:Tuple        =		torch.tensor(SCREAMING_SNAKE_CASE     )
                if key_name.startswith('''model/wte'''     ):
                    __UpperCamelCase					:List[str]        =		'''lm_head.weight'''
                    __UpperCamelCase					:List[Any]        =		vnp.copy()  # same in embedded
                    __UpperCamelCase					:int        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name.startswith('''model/wob'''     ):
                __UpperCamelCase					:Optional[Any]        =		'''final_logits_bias'''
                __UpperCamelCase					:Dict        =		vnp.copy()  # same in embedded
                __UpperCamelCase					:List[str]        =		state.reshape((1, -1)     )
                __UpperCamelCase					:Union[str, Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name == "model/dense/kernel":
                __UpperCamelCase					:Optional[Any]        =		'''model.last_project.weight'''
                __UpperCamelCase					:Any        =		vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                __UpperCamelCase					:Optional[Any]        =		torch.tensor(SCREAMING_SNAKE_CASE     )
            elif key_name == "model/dense_1/bias":
                __UpperCamelCase					:Optional[Any]        =		'''model.last_project.bias'''
                __UpperCamelCase					:List[str]        =		vnp.copy()  # same because it is one dimensional
                __UpperCamelCase					:Dict        =		torch.tensor(SCREAMING_SNAKE_CASE     )
    torch.save(SCREAMING_SNAKE_CASE  ,      args.output     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser(
        description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
    parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
    __lowercase										= parser.parse_args()
    convert_tf_gptsan_to_pt(args)




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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )


__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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		list(s_dict.keys()     )
    for key in keys:
        __UpperCamelCase					:Dict        =		key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __UpperCamelCase					:str        =		new_key.replace(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

        __UpperCamelCase					:Any        =		s_dict.pop(SCREAMING_SNAKE_CASE     )
    return s_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		emb.weight.shape
    __UpperCamelCase					:Any        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    os.makedirs(SCREAMING_SNAKE_CASE  ,      exist_ok=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:int        =		os.path.basename(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Optional[Any]        =		url.split('''/'''     )[-2]
    __UpperCamelCase					:Tuple        =		os.path.join(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

    if os.path.isfile(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:List[str]        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
        if hashlib.shaaaa(SCREAMING_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(SCREAMING_SNAKE_CASE     ) as source, open(SCREAMING_SNAKE_CASE  ,      '''wb'''     ) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''     )     )  ,      ncols=80  ,      unit='''iB'''  ,      unit_scale=SCREAMING_SNAKE_CASE  ,      unit_divisor=1_024     ) as loop:
            while True:
                __UpperCamelCase					:Optional[Any]        =		source.read(8_192     )
                if not buffer:
                    break

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

    __UpperCamelCase					:str        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
    if hashlib.shaaaa(SCREAMING_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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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

    __UpperCamelCase					:Dict        =		WhisperConfig(
        vocab_size=dimensions['''n_vocab''']  ,      encoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      decoder_ffn_dim=SCREAMING_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''']  ,      )

    __UpperCamelCase					:str        =		WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase  ,	__UpperCamelCase					:Any        =		model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_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:
        __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.decoder.embed_tokens     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		proj_out_weights

    model.save_pretrained(SCREAMING_SNAKE_CASE     )


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)





