




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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Any			=						["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase,_UpperCamelCase     )


_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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     str			=						list(s_dict.keys()     )
      for key in keys:
            _A							:     Any			=						key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        _A							:     Dict			=						new_key.replace(_UpperCamelCase,_UpperCamelCase     )

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

            _A							:     Union[str, Any]			=						s_dict.pop(_UpperCamelCase     )
      return s_dict







def 				lowerCAmelCase_	(     snake_case_     ):
      _A			,   _A							:     str			=						emb.weight.shape
      _A							:     int			=						nn.Linear(_UpperCamelCase,_UpperCamelCase,bias=_UpperCamelCase     )
      _A							:     Any			=						emb.weight.data
      return lin_layer







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      os.makedirs(_UpperCamelCase,exist_ok=_UpperCamelCase     )
      _A							:     Union[str, Any]			=						os.path.basename(_UpperCamelCase     )

      _A							:     List[str]			=						url.split("""/"""     )[-2]
      _A							:     int			=						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     ):
            _A							:     Optional[int]			=						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=80,unit="""iB""",unit_scale=_UpperCamelCase,unit_divisor=1024     ) as loop:
                  while True:
                        _A							:     Union[str, Any]			=						source.read(8192     )
                        if not buffer:
                              break

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

      _A							:     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_	(     snake_case_,snake_case_     ):




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

      _A							:     Optional[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"""],)

      _A							:     int			=						WhisperForConditionalGeneration(_UpperCamelCase     )
      _A			,   _A							:     int			=						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:
            _A							:     int			=						make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            _A							:     List[str]			=						proj_out_weights

      model.save_pretrained(_UpperCamelCase     )


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"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A     :   str			    =			(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(len(_UpperCamelCase      ) - 1      ):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if data_set == "train":
						return _hypothesis_value(train_data[example_no][0]      )
			elif data_set == "test":
						return _hypothesis_value(test_data[example_no][0]      )
			return None


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(_UpperCamelCase      ):
						if index == -1:
									summation_value += _error(_UpperCamelCase      )
						else:
									summation_value += _error(_UpperCamelCase      ) * train_data[i][0][index]
			return summation_value


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__lowerCAmelCase				    =			0.00_00_02
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			while True:
						j += 1
						__lowerCAmelCase				    =			[0, 0, 0, 0]
						for i in range(0    , len(_UpperCamelCase      )      ):
									__lowerCAmelCase				    =			get_cost_derivative(i - 1      )
									__lowerCAmelCase				    =			(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    _UpperCamelCase    , _UpperCamelCase    , atol=_UpperCamelCase    , rtol=_UpperCamelCase    , ):
									break
						__lowerCAmelCase				    =			temp_parameter_vector
			print(("Number of iterations:", j)      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			for i in range(len(_UpperCamelCase      )      ):
						print(("Actual output value:", output(_UpperCamelCase    , "test"      ))      )
						print(("Hypothesis output:", calculate_hypothesis_value(_UpperCamelCase    , "test"      ))      )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()




