

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__								=		{
    """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 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




 '''simple docstring'''




 _UpperCamelCase   :		int			=						['layers', 'blocks']
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_							,       UpperCAmelCase_	)


lowerCAmelCase__								=		{
    """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 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				List[Any]:




 '''simple docstring'''




 _UpperCamelCase   :		str			=						list(s_dict.keys()	)
 for key in keys:
  _UpperCamelCase   :		Tuple			=						key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    _UpperCamelCase   :		List[str]			=						new_key.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

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

  _UpperCamelCase   :		int			=						s_dict.pop(UpperCAmelCase_	)
 return s_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				List[str]:




 '''simple docstring'''




 _UpperCamelCase   :		Union[str, Any]			=						emb.weight.shape
 _UpperCamelCase   :		Tuple			=						nn.Linear(UpperCAmelCase_							,       UpperCAmelCase_							,       bias=UpperCAmelCase_	)
 _UpperCamelCase   :		Any			=						emb.weight.data
 return lin_layer






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str	)						->				bytes:




 '''simple docstring'''




 os.makedirs(UpperCAmelCase_							,       exist_ok=UpperCAmelCase_	)
 _UpperCamelCase   :		Optional[int]			=						os.path.basename(UpperCAmelCase_	)

 _UpperCamelCase   :		Tuple			=						url.split('/'	)[-2]
 _UpperCamelCase   :		Any			=						os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)

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

 if os.path.isfile(UpperCAmelCase_	):
  _UpperCamelCase   :		List[Any]			=						open(UpperCAmelCase_							,       'rb'	).read()
  if hashlib.shaaaa(UpperCAmelCase_	).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(UpperCAmelCase_	) as source, open(UpperCAmelCase_							,       'wb'	) as output:
  with tqdm(
      total=int(source.info().get('Content-Length'	)	)							,       ncols=8_0							,       unit='iB'							,       unit_scale=UpperCAmelCase_							,       unit_divisor=1_0_2_4	) as loop:
   while True:
    _UpperCamelCase   :		Optional[Any]			=						source.read(8_1_9_2	)
    if not buffer:
     break

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

 _UpperCamelCase   :		Union[str, Any]			=						open(UpperCAmelCase_							,       'rb'	).read()
 if hashlib.shaaaa(UpperCAmelCase_	).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 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     List[Any]	)						->				Optional[Any]:




 '''simple docstring'''




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

 _UpperCamelCase   :		Any			=						WhisperConfig(
     vocab_size=dimensions['n_vocab']							,       encoder_ffn_dim=UpperCAmelCase_							,       decoder_ffn_dim=UpperCAmelCase_							,       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']							,       )

 _UpperCamelCase   :		Any			=						WhisperForConditionalGeneration(UpperCAmelCase_	)
 _UpperCamelCase   :		Optional[Any]			=						model.model.load_state_dict(UpperCAmelCase_							,       strict=UpperCAmelCase_	)
 if len(UpperCAmelCase_	) > 0 and not set(UpperCAmelCase_	) <= {
     "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:
  _UpperCamelCase   :		str			=						make_linear_from_emb(model.model.decoder.embed_tokens	)
 else:
  _UpperCamelCase   :		str			=						proj_out_weights

 model.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
       lowerCAmelCase__								=		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__								=		parser.parse_args()

       convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




