






'''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


__a:			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       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= ['''layers''', '''blocks''']
 for k in ignore_keys:
  state_dict.pop(_snake_case		,					_snake_case					)


__a:			str	       =       {
    """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       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Any						= list(s_dict.keys()					)
 for key in keys:
  lowercase__    :   str						= key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    lowercase__    :   Any						= new_key.replace(_snake_case		,					_snake_case					)

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

  lowercase__    :   str						= s_dict.pop(_snake_case					)
 return s_dict
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   int						= emb.weight.shape
 lowercase__    :   List[str]						= nn.Linear(_snake_case		,					_snake_case		,					bias=_snake_case					)
 lowercase__    :   Any						= emb.weight.data
 return lin_layer
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 os.makedirs(_snake_case		,					exist_ok=_snake_case					)
 lowercase__    :   List[str]						= os.path.basename(_snake_case					)

 lowercase__    :   str						= url.split('''/'''					)[-2]
 lowercase__    :   Optional[int]						= os.path.join(_snake_case		,					_snake_case					)

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

 if os.path.isfile(_snake_case					):
  lowercase__    :   List[Any]						= open(_snake_case		,					'''rb'''					).read()
  if hashlib.shaaaa(_snake_case					).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(_snake_case					) as source, open(_snake_case		,					'''wb'''					) as output:
  with tqdm(
      total=int(source.info().get('''Content-Length'''					)					)		,					ncols=80		,					unit='''iB'''		,					unit_scale=_snake_case		,					unit_divisor=1024					) as loop:
   while True:
    lowercase__    :   Union[str, Any]						= source.read(8192					)
    if not buffer:
     break

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

 lowercase__    :   List[Any]						= open(_snake_case		,					'''rb'''					).read()
 if hashlib.shaaaa(_snake_case					).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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):





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

 lowercase__    :   List[Any]						= WhisperConfig(
     vocab_size=dimensions['''n_vocab''']		,					encoder_ffn_dim=_snake_case		,					decoder_ffn_dim=_snake_case		,					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''']		,					)

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

 model.save_pretrained(_snake_case					)


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

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

__a:			Optional[Any]	       =       logging.get_logger(__name__)



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       **__lowerCAmelCase			)  ->  int:
        super().__init__(**__lowerCAmelCase			)

        requires_backends(self     ,       '''vision'''			)
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)

       def __call__(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        return super().__call__(__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   str						= {}
        if "candidate_labels" in kwargs:
         lowercase__    :   str						= kwargs['''candidate_labels''']
        if "hypothesis_template" in kwargs:
         lowercase__    :   str						= kwargs['''hypothesis_template''']

        return preprocess_params, {}, {}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="This is a photo of {}."			)  ->  Any:
        lowercase__    :   Union[str, Any]						= load_image(__lowerCAmelCase			)
        lowercase__    :   List[str]						= self.image_processor(images=[image]     ,       return_tensors=self.framework			)
        lowercase__    :   Union[str, Any]						= candidate_labels
        lowercase__    :   int						= [hypothesis_template.format(__lowerCAmelCase			) for x in candidate_labels]
        lowercase__    :   Any						= self.tokenizer(__lowerCAmelCase     ,       return_tensors=self.framework     ,       padding=__lowerCAmelCase			)
        lowercase__    :   Any						= [text_inputs]
        return inputs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Any						= model_inputs.pop('''candidate_labels'''			)
        lowercase__    :   int						= model_inputs.pop('''text_inputs'''			)
        if isinstance(text_inputs[0]     ,       __lowerCAmelCase			):
         lowercase__    :   Union[str, Any]						= text_inputs[0]
        else:
         # Batching case.
         lowercase__    :   Optional[Any]						= text_inputs[0][0]

        lowercase__    :   Any						= self.model(**__lowerCAmelCase     ,       **__lowerCAmelCase			)

        lowercase__    :   Any						= {
            '''candidate_labels''': candidate_labels,
            '''logits''': outputs.logits_per_image,
        }
        return model_outputs


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= model_outputs.pop('''candidate_labels'''			)
        lowercase__    :   Optional[int]						= model_outputs['''logits'''][0]
        if self.framework == "pt":
         lowercase__    :   Optional[int]						= logits.softmax(dim=-1			).squeeze(-1			)
         lowercase__    :   Any						= probs.tolist()
         if not isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Dict						= [scores]
        elif self.framework == "tf":
         lowercase__    :   List[Any]						= stable_softmax(__lowerCAmelCase     ,       axis=-1			)
         lowercase__    :   str						= probs.numpy().tolist()
        else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""			)

        lowercase__    :   Optional[int]						= [
            {'''score''': score, '''label''': candidate_label}
            for score, candidate_label in sorted(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       key=lambda __lowerCAmelCase			: -x[0]			)
        ]
        return result

