

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    :       List[str]        =							{
    "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       a__					(		UpperCAmelCase							:      str      )  ->						int:
  UpperCAmelCase	:							Optional[Any]						=			['''layers''', '''blocks''']
  for k in ignore_keys:
    state_dict.pop(UpperCAmelCase    ,			UpperCAmelCase      )


_lowerCamelCase    :       Optional[Any]        =							{
    "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       a__					(		UpperCAmelCase							:      Any      )  ->						Any:
  UpperCAmelCase	:							List[Any]						=			list(s_dict.keys()      )
  for key in keys:
    UpperCAmelCase	:							Tuple						=			key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        UpperCAmelCase	:							List[Any]						=			new_key.replace(UpperCAmelCase    ,			UpperCAmelCase      )

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

    UpperCAmelCase	:							Dict						=			s_dict.pop(UpperCAmelCase      )
  return s_dict

def       a__					(		UpperCAmelCase							:      Any      )  ->						Optional[int]:
  UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			emb.weight.shape
  UpperCAmelCase	:							List[Any]						=			nn.Linear(UpperCAmelCase    ,			UpperCAmelCase    ,			bias=UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			emb.weight.data
  return lin_layer

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str      )  ->						bytes:
  os.makedirs(UpperCAmelCase    ,			exist_ok=UpperCAmelCase      )
  UpperCAmelCase	:							List[Any]						=			os.path.basename(UpperCAmelCase      )

  UpperCAmelCase	:							Union[str, Any]						=			url.split('''/'''      )[-2]
  UpperCAmelCase	:							Union[str, 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	:							Dict						=			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=1_024      ) as loop:
      while True:
        UpperCAmelCase	:							Optional[int]						=			source.read(8_192      )
        if not buffer:
          break

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

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

  UpperCAmelCase	:							List[str]						=			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	:							List[Any]						=			WhisperForConditionalGeneration(UpperCAmelCase      )
  UpperCAmelCase			,						UpperCAmelCase	:							Optional[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:
    UpperCAmelCase	:							List[str]						=			make_linear_from_emb(model.model.decoder.embed_tokens      )
  else:
    UpperCAmelCase	:							List[str]						=			proj_out_weights

  model.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
     _lowerCamelCase    :       List[Any]        =							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    :       str        =							parser.parse_args()

     convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





