



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 				__A					( A_      ):
			UpperCamelCase					:List[str]     =			'''vivit'''





			def __init__(self							,    __magic_name__=224							,    __magic_name__=32							,    __magic_name__=[2, 16, 16]							,    __magic_name__=3							,    __magic_name__=768							,    __magic_name__=12							,    __magic_name__=12							,    __magic_name__=3072							,    __magic_name__="gelu_fast"							,    __magic_name__=0.0							,    __magic_name__=0.0							,    __magic_name__=0.02							,    __magic_name__=1E-06							,    __magic_name__=True							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Tuple								=		hidden_size
						lowerCamelCase__ :      Dict								=		num_hidden_layers
						lowerCamelCase__ :      Optional[int]								=		num_attention_heads
						lowerCamelCase__ :      Any								=		intermediate_size
						lowerCamelCase__ :      Union[str, Any]								=		hidden_act
						lowerCamelCase__ :      Union[str, Any]								=		hidden_dropout_prob
						lowerCamelCase__ :      List[str]								=		attention_probs_dropout_prob
						lowerCamelCase__ :      Optional[Any]								=		initializer_range
						lowerCamelCase__ :      List[Any]								=		layer_norm_eps

						lowerCamelCase__ :      Tuple								=		image_size
						lowerCamelCase__ :      List[str]								=		num_frames
						lowerCamelCase__ :      Optional[int]								=		tubelet_size
						lowerCamelCase__ :      Optional[int]								=		num_channels
						lowerCamelCase__ :      Union[str, Any]								=		qkv_bias

						super().__init__(**__magic_name__				)








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


_lowercase     							=						{
    '''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		:      Any )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		["""layers""", """blocks"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase      ,			UpperCamelCase )


_lowercase     							=						{
    '''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		:      Union[str, Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		list(s_dict.keys() )
			for key in keys:
						lowerCamelCase__ :      Dict								=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowerCamelCase__ :      Tuple								=		new_key.replace(UpperCamelCase      ,			UpperCamelCase )

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

						lowerCamelCase__ :      int								=		s_dict.pop(UpperCamelCase )
			return s_dict






def      _A				(UpperCamelCase		:      List[Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      int								=		emb.weight.shape
			lowerCamelCase__ :      Optional[int]								=		nn.Linear(UpperCamelCase      ,			UpperCamelCase      ,			bias=UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		emb.weight.data
			return lin_layer






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->bytes:



			'''simple docstring'''







			os.makedirs(UpperCamelCase      ,			exist_ok=UpperCamelCase )
			lowerCamelCase__ :      Any								=		os.path.basename(UpperCamelCase )

			lowerCamelCase__ :      int								=		url.split("""/""" )[-2]
			lowerCamelCase__ :      Optional[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 ):
						lowerCamelCase__ :      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=1024 ) as loop:
									while True:
												lowerCamelCase__ :      Tuple								=		source.read(8192 )
												if not buffer:
															break

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

			lowerCamelCase__ :      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		:      Dict      ,			UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







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

			lowerCamelCase__ :      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"""]      ,			)

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

			model.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_lowercase     							=						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.''')
				_lowercase     							=						parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



