'''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


snake_case_			=     {
    """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      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= ['''layers''', '''blocks''']
				for k in ignore_keys:
								state_dict.pop(__a    ,	__a       )


snake_case_			=     {
    """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      :Dict       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= list(s_dict.keys()       )
				for key in keys:
								SCREAMING_SNAKE_CASE  :						Dict      					= key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																SCREAMING_SNAKE_CASE  :						int      					= new_key.replace(__a    ,	__a       )

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

								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= s_dict.pop(__a       )
				return s_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= emb.weight.shape
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= nn.Linear(__a    ,	__a    ,	bias=__a       )
				SCREAMING_SNAKE_CASE  :						int      					= emb.weight.data
				return lin_layer






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :str       ):
				os.makedirs(__a    ,	exist_ok=__a       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= os.path.basename(__a       )

				SCREAMING_SNAKE_CASE  :						List[str]      					= url.split('''/'''       )[-2]
				SCREAMING_SNAKE_CASE  :						int      					= os.path.join(__a    ,	__a       )

				if os.path.exists(__a       ) and not os.path.isfile(__a       ):
								raise RuntimeError(F'''{download_target} exists and is not a regular file'''       )

				if os.path.isfile(__a       ):
								SCREAMING_SNAKE_CASE  :						Tuple      					= open(__a    ,	'''rb'''       ).read()
								if hashlib.shaaaa(__a       ).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(__a       ) as source, open(__a    ,	'''wb'''       ) as output:
								with tqdm(
								    total=int(source.info().get('''Content-Length'''       )       )    ,	ncols=80    ,	unit='''iB'''    ,	unit_scale=__a    ,	unit_divisor=10_24       ) as loop:
												while True:
																SCREAMING_SNAKE_CASE  :						Tuple      					= source.read(81_92       )
																if not buffer:
																				break

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

				SCREAMING_SNAKE_CASE  :						List[str]      					= open(__a    ,	'''rb'''       ).read()
				if hashlib.shaaaa(__a       ).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      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):




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

				SCREAMING_SNAKE_CASE  :						str      					= WhisperConfig(
				    vocab_size=dimensions['''n_vocab''']    ,	encoder_ffn_dim=__a    ,	decoder_ffn_dim=__a    ,	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  :						int      					= WhisperForConditionalGeneration(__a       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= model.model.load_state_dict(__a    ,	strict=__a       )
				if len(__a       ) > 0 and not set(__a       ) <= {
				    "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  :						Optional[Any]      					= make_linear_from_emb(model.model.decoder.embed_tokens       )
				else:
								SCREAMING_SNAKE_CASE  :						int      					= proj_out_weights

				model.save_pretrained(__a       )


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

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




