
"""simple docstring"""

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	_lowercase       (    _UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     LayoutLMTokenizer
	lowercase_				=     LayoutLMTokenizerFast
	lowercase_				=     True
	lowercase_				=     True




	def       _UpperCamelCase   (			self )		->							Tuple:
			super().setUp()

			lowerCamelCase					:      Union[str, Any]          =   [
			    """[UNK]""",
			    """[CLS]""",
			    """[SEP]""",
			    """want""",
			    """##want""",
			    """##ed""",
			    """wa""",
			    """un""",
			    """runn""",
			    """##ing""",
			    """,""",
			    """low""",
			    """lowest""",
			]
			lowerCamelCase					:      Optional[Any]          =   os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['vocab_file'] )
			with open(self.vocab_file		,			'w'		,			encoding='utf-8' ) as vocab_writer:
					vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							List[Any]:
			return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**SCREAMING_SNAKE_CASE_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Dict          =   """UNwant\u00E9d,running"""
			lowerCamelCase					:      Dict          =   """unwanted, running"""
			return input_text, output_text




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      int          =   self.tokenizer_class(self.vocab_file )

			lowerCamelCase					:      Any          =   tokenizer.tokenize('UNwant\u00E9d,running' )
			self.assertListEqual(SCREAMING_SNAKE_CASE_		,			['un', '##want', '##ed', ',', 'runn', '##ing'] )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )		,			[7, 4, 5, 10, 8, 9] )




	def       _UpperCamelCase   (			self )		->							Tuple:
			pass







"""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						=			{
    '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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )


_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 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   list(s_dict.keys()      )
		for key in keys:
				lowerCamelCase					:      List[Any]          =   key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								lowerCamelCase					:      Optional[int]          =   new_key.replace(a_,						a_      )

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

				lowerCamelCase					:      Any          =   s_dict.pop(a_      )
		return s_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      int          =   emb.weight.shape
		lowerCamelCase					:      Dict          =   nn.Linear(a_,						a_,						bias=a_      )
		lowerCamelCase					:      Union[str, Any]          =   emb.weight.data
		return lin_layer
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		os.makedirs(a_,						exist_ok=a_      )
		lowerCamelCase					:      Union[str, Any]          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   url.split('/'      )[-2]
		lowerCamelCase					:      Tuple          =   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_      ):
				lowerCamelCase					:      Union[str, Any]          =   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=1024      ) as loop:
						while True:
								lowerCamelCase					:      Union[str, Any]          =   source.read(8192      )
								if not buffer:
										break

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

		lowerCamelCase					:      int          =   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 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      Optional[int]          =   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'],						)

		lowerCamelCase					:      Union[str, Any]          =   WhisperForConditionalGeneration(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   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:
				lowerCamelCase					:      List[Any]          =   make_linear_from_emb(model.model.decoder.embed_tokens      )
		else:
				lowerCamelCase					:      Tuple          =   proj_out_weights

		model.save_pretrained(a_      )


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)






