


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_						:			List[str]													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
snake_case_						:			List[str]													=							[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__	,a__):



				for attribute in key.split('''.'''):
								if is_finetuned:
												if attribute in ["quantizer", "project_q", "project_hid"]:
																# those layers are only relevant for pretraining and should be dropped
																return

												if attribute == "ctc_proj":
																# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																_SCREAMING_SNAKE_CASE			  ='''lm_head'''

								_SCREAMING_SNAKE_CASE			  =getattr(a__	,a__)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE			  =getattr(a__	,a__).shape
				else:
								_SCREAMING_SNAKE_CASE			  =hf_pointer.shape

				assert hf_shape == value.shape, (
				    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
				    f" {value.shape} for {full_name}"
				)

				if weight_type == "weight":
								_SCREAMING_SNAKE_CASE			  =value
				elif weight_type == "weight_g":
								_SCREAMING_SNAKE_CASE			  =value
				elif weight_type == "weight_v":
								_SCREAMING_SNAKE_CASE			  =value
				elif weight_type == "bias":
								_SCREAMING_SNAKE_CASE			  =value
				else:
								_SCREAMING_SNAKE_CASE			  =value

				logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE			  =hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE			  =False
								if "conv_layers" in name:
												load_conv_layer(
												    a__	,a__	,a__	,a__	,hf_model.config.feat_extract_norm == '''group'''	,)
												_SCREAMING_SNAKE_CASE			  =True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_SNAKE_CASE			  ='''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
																				_SCREAMING_SNAKE_CASE			  =True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE			  =name.split(a__)[0].split('''.''')[-2]
																								_SCREAMING_SNAKE_CASE			  =mapped_key.replace('''*'''	,a__)
																				if "weight_g" in name:
																								_SCREAMING_SNAKE_CASE			  ='''weight_g'''
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE			  ='''weight_v'''
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE			  ='''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_SCREAMING_SNAKE_CASE			  ='''weight'''
																				else:
																								_SCREAMING_SNAKE_CASE			  =None
																				set_recursively(a__	,a__	,a__	,a__	,a__	,a__)
																continue
								if not is_used:
												unused_weights.append(a__)

				logger.warning(f"Unused weights: {unused_weights}")







def 						lowerCamelCase(   a__	,a__	,a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =full_name.split('''conv_layers.''')[-1]
				_SCREAMING_SNAKE_CASE			  =name.split('''.''')
				_SCREAMING_SNAKE_CASE			  =int(items[0])
				_SCREAMING_SNAKE_CASE			  =int(items[1])

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
												)
												_SCREAMING_SNAKE_CASE			  =value
												logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
												)
												_SCREAMING_SNAKE_CASE			  =value
												logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
												    " found."
												)
												_SCREAMING_SNAKE_CASE			  =value
												logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
												)
												_SCREAMING_SNAKE_CASE			  =value
												logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
				else:
								unused_weights.append(a__)







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__=None	,a__=None	,a__=True):



				if config_path is not None:
								_SCREAMING_SNAKE_CASE			  =UniSpeechConfig.from_pretrained(a__)
				else:
								_SCREAMING_SNAKE_CASE			  =UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE			  =Dictionary.load_from_json(a__)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_SCREAMING_SNAKE_CASE			  =target_dict.pad_index
												_SCREAMING_SNAKE_CASE			  =target_dict.bos_index
												_SCREAMING_SNAKE_CASE			  =target_dict.eos_index
												_SCREAMING_SNAKE_CASE			  =len(target_dict.symbols)
												_SCREAMING_SNAKE_CASE			  =os.path.join(a__	,'''vocab.json''')
												if not os.path.isdir(a__):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__))
																return
												os.makedirs(a__	,exist_ok=a__)
												_SCREAMING_SNAKE_CASE			  =target_dict.indices

												# fairseq has the <pad> and <s> switched
												_SCREAMING_SNAKE_CASE			  =42
												_SCREAMING_SNAKE_CASE			  =43
												with open(a__	,'''w'''	,encoding='''utf-8''') as vocab_handle:
																json.dump(a__	,a__)
												_SCREAMING_SNAKE_CASE			  =WavaVecaPhonemeCTCTokenizer(
												    a__	,unk_token=target_dict.unk_word	,pad_token=target_dict.pad_word	,bos_token=target_dict.bos_word	,eos_token=target_dict.eos_word	,word_delimiter_token='''|'''	,do_lower_case=a__	,)
												_SCREAMING_SNAKE_CASE			  =True if config.feat_extract_norm == '''layer''' else False
												_SCREAMING_SNAKE_CASE			  =WavaVecaFeatureExtractor(
												    feature_size=1	,sampling_rate=1_6000	,padding_value=0	,do_normalize=a__	,return_attention_mask=a__	,)
												_SCREAMING_SNAKE_CASE			  =WavaVecaProcessor(feature_extractor=a__	,tokenizer=a__)
												processor.save_pretrained(a__)

								_SCREAMING_SNAKE_CASE			  =UniSpeechForCTC(a__)
				else:
								_SCREAMING_SNAKE_CASE			  =UniSpeechForPreTraining(a__)

				if is_finetuned:
								_SCREAMING_SNAKE_CASE			  =fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]	,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
				else:
								_SCREAMING_SNAKE_CASE			  =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				_SCREAMING_SNAKE_CASE			  =model[0].eval()

				recursively_load_weights(a__	,a__	,a__)

				hf_unispeech.save_pretrained(a__)


if __name__ == "__main__":
					snake_case_						:			Any													=							argparse.ArgumentParser()
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
					)
					snake_case_						:			int													=							parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						ProphetNetTokenizer
						UpperCAmelCase             =						False




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =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					:      str				,    _a					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [9, 6, 7, 12, 10, 11]  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer()

										self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  )				,    ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )				,    ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
										self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )				,    ['''hello''']  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a				,    strip_accents=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )				,    ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
										self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )				,    ['''h\u00E9llo''']  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a				,    strip_accents=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )				,    ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
										self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )				,    ['''hello''']  )




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )				,    ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
										self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )				,    ['''hello''']  )




						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )				,    ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a				,    strip_accents=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )				,    ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a				,    strip_accents=_a  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )				,    ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BasicTokenizer(do_lower_case=_a				,    never_split=['''[UNK]''']  )

										self.assertListEqual(
										    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  )				,    ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

										_SCREAMING_SNAKE_CASE			  ={}
										for i, token in enumerate(_a  ):
														_SCREAMING_SNAKE_CASE			  =i
										_SCREAMING_SNAKE_CASE			  =WordpieceTokenizer(vocab=_a				,    unk_token='''[UNK]'''  )

										self.assertListEqual(tokenizer.tokenize(''''''  )				,    []  )

										self.assertListEqual(tokenizer.tokenize('''unwanted running'''  )				,    ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

										self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  )				,    ['''[UNK]''', '''runn''', '''##ing''']  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
										_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a				,    return_tensors='''pt'''  )
										self.assertIsInstance(_a				,    _a  )
										_SCREAMING_SNAKE_CASE			  =list(batch.input_ids.numpy()[0]  )
										self.assertListEqual(_a				,    _a  )

										self.assertEqual((2, 9)				,    batch.input_ids.shape  )
										self.assertEqual((2, 9)				,    batch.attention_mask.shape  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Optional[int]:


										"""simple docstring"""





										self.assertTrue(_is_whitespace(''' '''  )  )
										self.assertTrue(_is_whitespace('''\t'''  )  )
										self.assertTrue(_is_whitespace('''\r'''  )  )
										self.assertTrue(_is_whitespace('''\n'''  )  )
										self.assertTrue(_is_whitespace('''\u00A0'''  )  )

										self.assertFalse(_is_whitespace('''A'''  )  )
										self.assertFalse(_is_whitespace('''-'''  )  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										self.assertTrue(_is_control('''\u0005'''  )  )

										self.assertFalse(_is_control('''A'''  )  )
										self.assertFalse(_is_control(''' '''  )  )
										self.assertFalse(_is_control('''\t'''  )  )
										self.assertFalse(_is_control('''\r'''  )  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     List[str]:


										"""simple docstring"""





										self.assertTrue(_is_punctuation('''-'''  )  )
										self.assertTrue(_is_punctuation('''$'''  )  )
										self.assertTrue(_is_punctuation('''`'''  )  )
										self.assertTrue(_is_punctuation('''.'''  )  )

										self.assertFalse(_is_punctuation('''A'''  )  )
										self.assertFalse(_is_punctuation(''' '''  )  )




						@slow
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode('''sequence builders'''				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode('''multi-sequence build'''				,    add_special_tokens=_a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.build_inputs_with_special_tokens(_a  )
										_SCREAMING_SNAKE_CASE			  =tokenizer.build_inputs_with_special_tokens(_a				,    _a  )

										assert encoded_sentence == text + [102]
										assert encoded_pair == text + [102] + text_a + [102]