


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


__snake_case     :   Optional[Any]            =   {
    "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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




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


__snake_case     :   List[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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




			UpperCamelCase         =					list(s_dict.keys()	)
			for key in keys:
						UpperCamelCase         =					key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												UpperCamelCase         =					new_key.replace(__a       ,  __a	)

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

						UpperCamelCase         =					s_dict.pop(__a	)
			return s_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase         =					emb.weight.shape
			UpperCamelCase         =					nn.Linear(__a       ,  __a       ,  bias=__a	)
			UpperCamelCase         =					emb.weight.data
			return lin_layer






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			os.makedirs(__a       ,  exist_ok=__a	)
			UpperCamelCase         =					os.path.basename(__a	)

			UpperCamelCase         =					url.split("""/"""	)[-2]
			UpperCamelCase         =					os.path.join(__a       ,  __a	)

			if os.path.exists(__a	) and not os.path.isfile(__a	):
						raise RuntimeError(f'{download_target} exists and is not a regular file'	)

			if os.path.isfile(__a	):
						UpperCamelCase         =					open(__a       ,  """rb"""	).read()
						if hashlib.shaaaa(__a	).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(__a	) as source, open(__a       ,  """wb"""	) as output:
						with tqdm(
						    total=int(source.info().get("""Content-Length"""	)	)       ,  ncols=80       ,  unit="""iB"""       ,  unit_scale=__a       ,  unit_divisor=1_024	) as loop:
									while True:
												UpperCamelCase         =					source.read(8_192	)
												if not buffer:
															break

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

			UpperCamelCase         =					open(__a       ,  """rb"""	).read()
			if hashlib.shaaaa(__a	).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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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

			UpperCamelCase         =					WhisperConfig(
			    vocab_size=dimensions["""n_vocab"""]       ,  encoder_ffn_dim=__a       ,  decoder_ffn_dim=__a       ,  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         =					WhisperForConditionalGeneration(__a	)
			UpperCamelCase  ,  UpperCamelCase         =					model.model.load_state_dict(__a       ,  strict=__a	)
			if len(__a	) > 0 and not set(__a	) <= {
			    "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         =					make_linear_from_emb(model.model.decoder.embed_tokens	)
			else:
						UpperCamelCase         =					proj_out_weights

			model.save_pretrained(__a	)


if __name__ == "__main__":
			__snake_case     :   int            =   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.")
			__snake_case     :   str            =   parser.parse_args()

			convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








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


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """markuplm"""




			def __init__(						self							:		Optional[Any]       ,       __magic_name__							:		List[Any]=3_0_5_2_2       ,       __magic_name__							:		int=7_6_8       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		List[Any]=1_2       ,       __magic_name__							:		str=3_0_7_2       ,       __magic_name__							:		Optional[int]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		Optional[int]=0.1       ,       __magic_name__							:		Any=5_1_2       ,       __magic_name__							:		List[str]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		List[str]=1e-12       ,       __magic_name__							:		str=0       ,       __magic_name__							:		List[str]=0       ,       __magic_name__							:		Optional[Any]=2       ,       __magic_name__							:		Dict=2_5_6       ,       __magic_name__							:		Tuple=1_0_2_4       ,       __magic_name__							:		Any=2_1_6       ,       __magic_name__							:		str=1_0_0_1       ,       __magic_name__							:		Dict=3_2       ,       __magic_name__							:		Optional[int]=5_0       ,       __magic_name__							:		List[Any]="absolute"       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Optional[Any]=None       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(
						    pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__       ,       )
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					use_cache
						UpperCamelCase         =					classifier_dropout
						# additional properties
						UpperCamelCase         =					max_depth
						UpperCamelCase         =					max_xpath_tag_unit_embeddings
						UpperCamelCase         =					max_xpath_subs_unit_embeddings
						UpperCamelCase         =					tag_pad_id
						UpperCamelCase         =					subs_pad_id
						UpperCamelCase         =					xpath_unit_hidden_size




