



"""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		=						{
    '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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






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


A		=						{
    '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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






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

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

												snake_case   :			Tuple						    = s_dict.pop(lowerCamelCase_ )
						return s_dict

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						snake_case    ,			snake_case   :			Dict						    = emb.weight.shape
						snake_case   :			List[Any]						    = nn.Linear(lowerCamelCase_     ,		lowerCamelCase_     ,		bias=lowerCamelCase_ )
						snake_case   :			Optional[int]						    = emb.weight.data
						return lin_layer

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						os.makedirs(lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						snake_case   :			List[Any]						    = os.path.basename(lowerCamelCase_ )

						snake_case   :			int						    = url.split("/" )[-2]
						snake_case   :			Dict						    = 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_ ):
												snake_case   :			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=8_0     ,		unit="iB"     ,		unit_scale=lowerCamelCase_     ,		unit_divisor=1_0_2_4 ) as loop:
																		while True:
																								snake_case   :			Dict						    = source.read(8_1_9_2 )
																								if not buffer:
																														break

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

						snake_case   :			List[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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






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

						snake_case   :			int						    = 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"]     ,		)

						snake_case   :			Optional[int]						    = WhisperForConditionalGeneration(lowerCamelCase_ )
						snake_case    ,			snake_case   :			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:
												snake_case   :			Union[str, Any]						    = make_linear_from_emb(model.model.decoder.embed_tokens )
						else:
												snake_case   :			Optional[int]						    = proj_out_weights

						model.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
			A		=						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		=						parser.parse_args()

			convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



