

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 a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= ['layers', 'blocks']
	for k in ignore_keys:
		state_dict.pop(lowercase__			, lowercase__	)


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 a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= list(s_dict.keys()	)
	for key in keys:
		snake_case_      		= key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				snake_case_      		= new_key.replace(lowercase__			, lowercase__	)

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

		snake_case_      		= s_dict.pop(lowercase__	)
	return s_dict





def a(lowercase__	):





	'''simple docstring'''
	snake_case_		,       snake_case_      		= emb.weight.shape
	snake_case_      		= nn.Linear(lowercase__			, lowercase__			, bias=lowercase__	)
	snake_case_      		= emb.weight.data
	return lin_layer





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	os.makedirs(lowercase__			, exist_ok=lowercase__	)
	snake_case_      		= os.path.basename(lowercase__	)

	snake_case_      		= url.split('/'	)[-2]
	snake_case_      		= os.path.join(lowercase__			, lowercase__	)

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

	if os.path.isfile(lowercase__	):
		snake_case_      		= open(lowercase__			, 'rb'	).read()
		if hashlib.shaaaa(lowercase__	).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(lowercase__	) as source, open(lowercase__			, 'wb'	) as output:
		with tqdm(
		    total=int(source.info().get('Content-Length'	)	)			, ncols=80			, unit='iB'			, unit_scale=lowercase__			, unit_divisor=1024	) as loop:
			while True:
				snake_case_      		= source.read(8192	)
				if not buffer:
					break

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

	snake_case_      		= open(lowercase__			, 'rb'	).read()
	if hashlib.shaaaa(lowercase__	).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 a(lowercase__			, lowercase__	):





	'''simple docstring'''
	if ".pt" not in checkpoint_path:
		snake_case_      		= _download(_MODELS[checkpoint_path]	)
	else:
		snake_case_      		= torch.load(lowercase__			, map_location='cpu'	)
	snake_case_      		= original_checkpoint['dims']
	snake_case_      		= original_checkpoint['model_state_dict']
	snake_case_      		= state_dict['decoder.token_embedding.weight']
	remove_ignore_keys_(lowercase__	)
	rename_keys(lowercase__	)
	snake_case_      		= True
	snake_case_      		= state_dict['decoder.layers.0.fc1.weight'].shape[0]

	snake_case_      		= WhisperConfig(
	    vocab_size=dimensions['n_vocab']			, encoder_ffn_dim=lowercase__			, decoder_ffn_dim=lowercase__			, 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_      		= WhisperForConditionalGeneration(lowercase__	)
	snake_case_		,       snake_case_      		= model.model.load_state_dict(lowercase__			, strict=lowercase__	)
	if len(lowercase__	) > 0 and not set(lowercase__	) <= {
	    "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_      		= make_linear_from_emb(model.model.decoder.embed_tokens	)
	else:
		snake_case_      		= proj_out_weights

	model.save_pretrained(lowercase__	)


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)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
