from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_							=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_							=      [
      """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TapasForMaskedLM""",
      """TapasForQuestionAnswering""",
      """TapasForSequenceClassification""",
      """TapasModel""",
      """TapasPreTrainedModel""",
      """load_tf_weights_in_tapas""",
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_							=      [
      """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFTapasForMaskedLM""",
      """TFTapasForQuestionAnswering""",
      """TFTapasForSequenceClassification""",
      """TFTapasModel""",
      """TFTapasPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




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       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  str:
  __UpperCAmelCase						    =       ["layers", "blocks"]
  for k in ignore_keys:
    state_dict.pop(A_						,	A_					)


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       __lowerCAmelCase      (						A_   : str					)    ->  List[str]:
  __UpperCAmelCase						    =       list(s_dict.keys()					)
  for key in keys:
    __UpperCAmelCase						    =       key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        __UpperCAmelCase						    =       new_key.replace(A_						,	A_					)

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

    __UpperCAmelCase						    =       s_dict.pop(A_					)
  return s_dict




def       __lowerCAmelCase      (						A_   : List[Any]					)    ->  List[Any]:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       emb.weight.shape
  __UpperCAmelCase						    =       nn.Linear(A_						,	A_						,	bias=A_					)
  __UpperCAmelCase						    =       emb.weight.data
  return lin_layer




def       __lowerCAmelCase      (						A_   : str						,	A_   : str					)    ->  bytes:
  os.makedirs(A_						,	exist_ok=A_					)
  __UpperCAmelCase						    =       os.path.basename(A_					)

  __UpperCAmelCase						    =       url.split("/"					)[-2]
  __UpperCAmelCase						    =       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_					):
    __UpperCAmelCase						    =       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:
        __UpperCAmelCase						    =       source.read(81_92					)
        if not buffer:
          break

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

  __UpperCAmelCase						    =       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       __lowerCAmelCase      (						A_   : Dict						,	A_   : Optional[Any]					)    ->  Optional[Any]:
  if ".pt" not in checkpoint_path:
    __UpperCAmelCase						    =       _download(_MODELS[checkpoint_path]					)
  else:
    __UpperCAmelCase						    =       torch.load(A_						,	map_location="cpu"					)
  __UpperCAmelCase						    =       original_checkpoint["dims"]
  __UpperCAmelCase						    =       original_checkpoint["model_state_dict"]
  __UpperCAmelCase						    =       state_dict["decoder.token_embedding.weight"]
  remove_ignore_keys_(A_					)
  rename_keys(A_					)
  __UpperCAmelCase						    =       True
  __UpperCAmelCase						    =       state_dict["decoder.layers.0.fc1.weight"].shape[0]

  __UpperCAmelCase						    =       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"]						,	)

  __UpperCAmelCase						    =       WhisperForConditionalGeneration(A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       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:
    __UpperCAmelCase						    =       make_linear_from_emb(model.model.decoder.embed_tokens					)
  else:
    __UpperCAmelCase						    =       proj_out_weights

  model.save_pretrained(A_					)


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)



