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


__lowerCamelCase					:	int       =  {
    '''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       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    ["layers", "blocks"]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase   , lowerCAmelCase	)


__lowerCamelCase					:	List[Any]       =  {
    '''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       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







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

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

				SCREAMING_SNAKE_CASE_  :		List[str]				      =    s_dict.pop(lowerCAmelCase	)
		return s_dict




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    emb.weight.shape
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    nn.Linear(lowerCAmelCase   , lowerCAmelCase   , bias=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    emb.weight.data
		return lin_layer




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		os.makedirs(lowerCAmelCase   , exist_ok=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    os.path.basename(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    url.split("/"	)[-2]
		SCREAMING_SNAKE_CASE_  :		str				      =    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	):
				SCREAMING_SNAKE_CASE_  :		Dict				      =    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:
								SCREAMING_SNAKE_CASE_  :		int				      =    source.read(8_1_9_2	)
								if not buffer:
										break

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

		SCREAMING_SNAKE_CASE_  :		Any				      =    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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		Optional[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"]   , )

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    WhisperForConditionalGeneration(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[str]				      =    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:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    make_linear_from_emb(model.model.decoder.embed_tokens	)
		else:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    proj_out_weights

		model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
		__lowerCamelCase					:	Optional[int]       =  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.''')
		__lowerCamelCase					:	str       =  parser.parse_args()

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)

# Copyright 2021 The HuggingFace 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    ArgumentParser("Accelerate CLI tool"   , usage="accelerate <command> [<args>]"   , allow_abbrev=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    parser.add_subparsers(help="accelerate command helpers"	)

		# Register commands
		get_config_parser(subparsers=lowerCAmelCase	)
		env_command_parser(subparsers=lowerCAmelCase	)
		launch_command_parser(subparsers=lowerCAmelCase	)
		tpu_command_parser(subparsers=lowerCAmelCase	)
		test_command_parser(subparsers=lowerCAmelCase	)

		# Let's go
		SCREAMING_SNAKE_CASE_  :		Dict				      =    parser.parse_args()

		if not hasattr(lowerCAmelCase   , "func"	):
				parser.print_help()
				exit(1	)

		# Run
		args.func(lowerCAmelCase	)


if __name__ == "__main__":
		main()
