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

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

        __UpperCamelCase     :List[str]			  =		s_dict.pop(SCREAMING_SNAKE_CASE	)
    return s_dict



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







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		emb.weight.shape
    __UpperCamelCase     :Tuple			  =		nn.Linear(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			bias=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		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     :List[str]			  =		url.split('''/'''	)[-2]
    __UpperCamelCase     :Dict			  =		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     :Optional[Any]			  =		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     :str			  =		source.read(8_192	)
                if not buffer:
                    break

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

    __UpperCamelCase     :Union[str, Any]			  =		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     :Dict			  =		_download(_MODELS[checkpoint_path]	)
    else:
        __UpperCamelCase     :Optional[Any]			  =		torch.load(SCREAMING_SNAKE_CASE  ,			map_location='''cpu'''	)
    __UpperCamelCase     :Dict			  =		original_checkpoint['''dims''']
    __UpperCamelCase     :Optional[Any]			  =		original_checkpoint['''model_state_dict''']
    __UpperCamelCase     :Optional[int]			  =		state_dict['''decoder.token_embedding.weight''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE	)
    rename_keys(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		True
    __UpperCamelCase     :Union[str, Any]			  =		state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

    __UpperCamelCase     :List[Any]			  =		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     :Union[str, Any]			  =		WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		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     :int			  =		make_linear_from_emb(model.model.decoder.embed_tokens	)
    else:
        __UpperCamelCase     :List[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)






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowercase							 =					logging.get_logger(__name__)



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







    __UpperCamelCase     :Tuple			  =		R'''\w+[.]\d+'''
    __UpperCamelCase     :List[str]			  =		re.findall(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for pat in pats:
        __UpperCamelCase     :int			  =		key.replace(SCREAMING_SNAKE_CASE  ,			'''_'''.join(pat.split('''.'''	)	)	)
    return key



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







    __UpperCamelCase     :Tuple			  =		pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key	)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        __UpperCamelCase     :Any			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        __UpperCamelCase     :List[Any]			  =		pt_tensor.transpose(2  ,			3  ,			1  ,			0	)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        __UpperCamelCase     :Any			  =		pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=42	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    __UpperCamelCase     :str			  =		flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE	)	)

    __UpperCamelCase     :int			  =		flatten_dict(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        __UpperCamelCase     :List[Any]			  =		rename_key(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :List[Any]			  =		tuple(renamed_pt_key.split('''.'''	)	)

        # Correctly rename weight parameters
        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
        __UpperCamelCase     :str			  =		jnp.asarray(SCREAMING_SNAKE_CASE	)

    return unflatten_dict(SCREAMING_SNAKE_CASE	)





