




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_lowerCamelCase	:		List[Any]							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[List[ImageInput]]:



       """simple docstring"""

       if isinstance(lowercase_ ,				(list, tuple)						) and isinstance(videos[0] ,				(list, tuple)						) and is_valid_image(videos[0][0]						):
              return videos

       elif isinstance(lowercase_ ,				(list, tuple)						) and is_valid_image(videos[0]						):
              return [videos]

       elif is_valid_image(lowercase_						):
              return [[videos]]

       raise ValueError(f"""Could not make batched video from {videos}"""						)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''pixel_values''']


    def __init__(			self      :	Any					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = PILImageResampling.BILINEAR					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Union[int, float] = 1 / 255					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, **UpperCAmelCase__      :	Tuple					, )						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__       =    size if size is not None else {'''shortest_edge''': 224}
           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           A__       =    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__       =    get_size_dict(UpperCAmelCase__					, param_name='''crop_size''')

           A__       =    do_resize
           A__       =    size
           A__       =    do_center_crop
           A__       =    crop_size
           A__       =    resample
           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_normalize
           A__       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Dict[str, int]					, UpperCAmelCase__      :	PILImageResampling = PILImageResampling.BILINEAR					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Tuple					, )						->np.ndarray:





           '''simple docstring'''

           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           if "shortest_edge" in size:
                  A__       =    get_resize_output_image_size(UpperCAmelCase__					, size['''shortest_edge''']					, default_to_square=UpperCAmelCase__)
           elif "height" in size and "width" in size:
                  A__       =    (size['''height'''], size['''width'''])
           else:
                  raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
           return resize(UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Dict[str, int]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Union[str, Any]					, )						->np.ndarray:





           '''simple docstring'''

           A__       =    get_size_dict(UpperCAmelCase__)
           if "height" not in size or "width" not in size:
                  raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
           return center_crop(UpperCAmelCase__					, size=(size['''height'''], size['''width'''])					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Union[int, float]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Optional[Any]					, )						->Union[str, Any]:





           '''simple docstring'''

           return rescale(UpperCAmelCase__					, scale=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	Union[float, List[float]]					, UpperCAmelCase__      :	Union[float, List[float]]					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	List[Any]					, )						->np.ndarray:





           '''simple docstring'''

           return normalize(UpperCAmelCase__					, mean=UpperCAmelCase__					, std=UpperCAmelCase__					, data_format=UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	ImageInput					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	float = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[ChannelDimension] = ChannelDimension.FIRST					, )						->np.ndarray:





           '''simple docstring'''

           if do_resize and size is None or resample is None:
                  raise ValueError('''Size and resample must be specified if do_resize is True.''')

           if do_center_crop and crop_size is None:
                  raise ValueError('''Crop size must be specified if do_center_crop is True.''')

           if do_rescale and rescale_factor is None:
                  raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

           if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

           # All transformations expect numpy arrays.
           A__       =    to_numpy_array(UpperCAmelCase__)

           if do_resize:
                  A__       =    self.resize(image=UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__)

           if do_center_crop:
                  A__       =    self.center_crop(UpperCAmelCase__					, size=UpperCAmelCase__)

           if do_rescale:
                  A__       =    self.rescale(image=UpperCAmelCase__					, scale=UpperCAmelCase__)

           if do_normalize:
                  A__       =    self.normalize(image=UpperCAmelCase__					, mean=UpperCAmelCase__					, std=UpperCAmelCase__)

           A__       =    to_channel_dimension_format(UpperCAmelCase__					, UpperCAmelCase__)
           return image





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	ImageInput					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	PILImageResampling = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Dict[str, int] = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	float = None					, UpperCAmelCase__      :	bool = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[float, List[float]]] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	ChannelDimension = ChannelDimension.FIRST					, **UpperCAmelCase__      :	Optional[Any]					, )						->PIL.Image.Image:





           '''simple docstring'''

           A__       =    do_resize if do_resize is not None else self.do_resize
           A__       =    resample if resample is not None else self.resample
           A__       =    do_center_crop if do_center_crop is not None else self.do_center_crop
           A__       =    do_rescale if do_rescale is not None else self.do_rescale
           A__       =    rescale_factor if rescale_factor is not None else self.rescale_factor
           A__       =    do_normalize if do_normalize is not None else self.do_normalize
           A__       =    image_mean if image_mean is not None else self.image_mean
           A__       =    image_std if image_std is not None else self.image_std

           A__       =    size if size is not None else self.size
           A__       =    get_size_dict(UpperCAmelCase__					, default_to_square=UpperCAmelCase__)
           A__       =    crop_size if crop_size is not None else self.crop_size
           A__       =    get_size_dict(UpperCAmelCase__					, param_name='''crop_size''')

           if not valid_images(UpperCAmelCase__):
                  raise ValueError(
                      '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                      '''torch.Tensor, tf.Tensor or jax.ndarray.''')

           A__       =    make_batched(UpperCAmelCase__)

           A__       =    [
               [
                   self._preprocess_image(
                       image=UpperCAmelCase__					, do_resize=UpperCAmelCase__					, size=UpperCAmelCase__					, resample=UpperCAmelCase__					, do_center_crop=UpperCAmelCase__					, crop_size=UpperCAmelCase__					, do_rescale=UpperCAmelCase__					, rescale_factor=UpperCAmelCase__					, do_normalize=UpperCAmelCase__					, image_mean=UpperCAmelCase__					, image_std=UpperCAmelCase__					, data_format=UpperCAmelCase__					, )
                   for img in video
               ]
               for video in videos
           ]

           A__       =    {'''pixel_values''': videos}
           return BatchFeature(data=UpperCAmelCase__					, tensor_type=UpperCAmelCase__)









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 )		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  ['''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 )		->						int:






				"""simple docstring"""


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

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

								snake_case_						:    Tuple				=  s_dict.pop(_UpperCamelCase )
				return s_dict






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    Dict				=  emb.weight.shape
				snake_case_						:    Tuple				=  nn.Linear(_UpperCamelCase ,				_UpperCamelCase ,				bias=_UpperCamelCase )
				snake_case_						:    Any				=  emb.weight.data
				return lin_layer






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						bytes:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase )
				snake_case_						:    List[Any]				=  os.path.basename(_UpperCamelCase )

				snake_case_						:    Any				=  url.split('''/''' )[-2]
				snake_case_						:    str				=  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 ):
								snake_case_						:    Union[str, 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=80 ,				unit='''iB''' ,				unit_scale=_UpperCamelCase ,				unit_divisor=1_024 ) as loop:
												while True:
																snake_case_						:    Dict				=  source.read(8_192 )
																if not buffer:
																				break

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

				snake_case_						:    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 ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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

				snake_case_						:    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'''] ,				)

				snake_case_						:    Union[str, Any]				=  WhisperForConditionalGeneration(_UpperCamelCase )
				snake_case_			,							snake_case_						:    List[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:
								snake_case_						:    List[str]				=  make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								snake_case_						:    Any				=  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)
