

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    Optional[Any] 	=			['layers', 'blocks']
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase__     ,			lowerCamelCase__      )


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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			list(s_dict.keys()      )
	for key in keys:
		__lowerCamelCase    :    List[str] 	=			key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				__lowerCamelCase    :    Any 	=			new_key.replace(lowerCamelCase__     ,			lowerCamelCase__      )

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

		__lowerCamelCase    :    List[Any] 	=			s_dict.pop(lowerCamelCase__      )
	return s_dict






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			emb.weight.shape
	__lowerCamelCase    :    Optional[int] 	=			nn.Linear(lowerCamelCase__     ,			lowerCamelCase__     ,			bias=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			emb.weight.data
	return lin_layer






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bytes:
	os.makedirs(lowerCamelCase__     ,			exist_ok=lowerCamelCase__      )
	__lowerCamelCase    :    Optional[Any] 	=			os.path.basename(lowerCamelCase__      )

	__lowerCamelCase    :    List[Any] 	=			url.split('/'      )[-2]
	__lowerCamelCase    :    Optional[Any] 	=			os.path.join(lowerCamelCase__     ,			lowerCamelCase__      )

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

	if os.path.isfile(lowerCamelCase__      ):
		__lowerCamelCase    :    Optional[int] 	=			open(lowerCamelCase__     ,			'rb'      ).read()
		if hashlib.shaaaa(lowerCamelCase__      ).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(lowerCamelCase__      ) as source, open(lowerCamelCase__     ,			'wb'      ) as output:
		with tqdm(
		    total=int(source.info().get('Content-Length'      )      )     ,			ncols=8_0     ,			unit='iB'     ,			unit_scale=lowerCamelCase__     ,			unit_divisor=1_0_2_4      ) as loop:
			while True:
				__lowerCamelCase    :    str 	=			source.read(8_1_9_2      )
				if not buffer:
					break

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

	__lowerCamelCase    :    List[str] 	=			open(lowerCamelCase__     ,			'rb'      ).read()
	if hashlib.shaaaa(lowerCamelCase__      ).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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:
	if ".pt" not in checkpoint_path:
		__lowerCamelCase    :    Any 	=			_download(_MODELS[checkpoint_path]      )
	else:
		__lowerCamelCase    :    Optional[int] 	=			torch.load(lowerCamelCase__     ,			map_location='cpu'      )
	__lowerCamelCase    :    int 	=			original_checkpoint['dims']
	__lowerCamelCase    :    int 	=			original_checkpoint['model_state_dict']
	__lowerCamelCase    :    str 	=			state_dict['decoder.token_embedding.weight']
	remove_ignore_keys_(lowerCamelCase__      )
	rename_keys(lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			True
	__lowerCamelCase    :    Optional[int] 	=			state_dict['decoder.layers.0.fc1.weight'].shape[0]

	__lowerCamelCase    :    List[str] 	=			WhisperConfig(
	    vocab_size=dimensions['n_vocab']     ,			encoder_ffn_dim=lowerCamelCase__     ,			decoder_ffn_dim=lowerCamelCase__     ,			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    :    Union[str, Any] 	=			WhisperForConditionalGeneration(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			model.model.load_state_dict(lowerCamelCase__     ,			strict=lowerCamelCase__      )
	if len(lowerCamelCase__      ) > 0 and not set(lowerCamelCase__      ) <= {
	    "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    :    Dict 	=			make_linear_from_emb(model.model.decoder.embed_tokens      )
	else:
		__lowerCamelCase    :    Optional[Any] 	=			proj_out_weights

	model.save_pretrained(lowerCamelCase__      )


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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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




