


"""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


__UpperCamelCase						=      {
    '''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     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					['layers', 'blocks']
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


__UpperCamelCase						=      {
    '''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     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple  )   ->						int:
						SCREAMING_SNAKE_CASE													=					list(s_dict.keys()  )
						for key in keys:
												SCREAMING_SNAKE_CASE													=					key
												for k, v in WHISPER_MAPPING.items():
																		if k in key:
																								SCREAMING_SNAKE_CASE													=					new_key.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

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

												SCREAMING_SNAKE_CASE													=					s_dict.pop(SCREAMING_SNAKE_CASE_  )
						return s_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Tuple:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					emb.weight.shape
						SCREAMING_SNAKE_CASE													=					nn.Linear(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			bias=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					emb.weight.data
						return lin_layer


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						bytes:
						os.makedirs(SCREAMING_SNAKE_CASE_ ,			exist_ok=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					os.path.basename(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					url.split('/'  )[-2]
						SCREAMING_SNAKE_CASE													=					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_  ):
												SCREAMING_SNAKE_CASE													=					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=10_24  ) as loop:
																		while True:
																								SCREAMING_SNAKE_CASE													=					source.read(81_92  )
																								if not buffer:
																														break

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

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

						SCREAMING_SNAKE_CASE													=					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'] ,			)

						SCREAMING_SNAKE_CASE													=					WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					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:
												SCREAMING_SNAKE_CASE													=					make_linear_from_emb(model.model.decoder.embed_tokens  )
						else:
												SCREAMING_SNAKE_CASE													=					proj_out_weights

						model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
	__UpperCamelCase						=      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.''')
	__UpperCamelCase						=      parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


