

'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_    						=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Tuple						)				->  str:
						for attribute in key.split('''.'''						):
												lowercase :							Tuple    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Optional[int]    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							List[Any]    		=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    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":
												lowercase :							Any    		=value
						elif weight_type == "weight_g":
												lowercase :							List[Any]    		=value
						elif weight_type == "weight_v":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "bias":
												lowercase :							Tuple    		=value
						elif weight_type == "running_mean":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "running_var":
												lowercase :							str    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							Tuple    		=value
						elif weight_type == "inv_freq":
												lowercase :							Optional[Any]    		=value
						else:
												lowercase :							Tuple    		=value

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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=[]
						lowercase :							Tuple    		=fairseq_model.state_dict()

						lowercase :							List[Any]    		=hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase :							Tuple    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							List[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase :							Optional[int]    		='''wav2vec2_conformer.''' + 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]:
																														lowercase :							Union[str, Any]    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[int]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							List[str]    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "pos_bias_u" in name:
																																				lowercase :							Optional[Any]    		=None
																														elif "pos_bias_v" in name:
																																				lowercase :							Union[str, Any]    		=None
																														elif "weight_g" in name:
																																				lowercase :							Any    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Optional[int]    		='''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase :							Optional[int]    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Union[str, Any]    		='''running_mean'''
																														elif "inv_freq" in name:
																																				lowercase :							Any    		='''inv_freq'''
																														elif "running_var" in name:
																																				lowercase :							Tuple    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							Dict    		='''num_batches_tracked'''
																														else:
																																				lowercase :							str    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

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






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple						)				->  int:
						lowercase :							Optional[Any]    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Any    		=name.split('''.'''						)
						lowercase :							List[str]    		=int(items[0]						)
						lowercase :							Union[str, Any]    		=int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
																		lowercase :							Union[str, Any]    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
																		lowercase :							Optional[Any]    		=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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
																		lowercase :							Optional[int]    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple=None   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=True						)				->  Union[str, Any]:
						if config_path is not None:
												lowercase :							Optional[Any]    		=WavaVecaConformerConfig.from_pretrained(__magic_name__   , hidden_act='''swish'''						)
						else:
												lowercase :							Optional[int]    		=WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												lowercase :							Dict    		='''rotary'''

						if is_finetuned:
												if dict_path:
																		lowercase :							Optional[Any]    		=Dictionary.load(__magic_name__						)

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

																		# fairseq has the <pad> and <s> switched
																		lowercase :							str    		=0
																		lowercase :							List[Any]    		=1
																		with open(__magic_name__   , '''w'''   , encoding='''utf-8'''						) as vocab_handle:
																								json.dump(__magic_name__   , __magic_name__						)
																		lowercase :							List[str]    		=WavaVecaCTCTokenizer(
																		    __magic_name__   , 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=__magic_name__   , )
																		lowercase :							Optional[int]    		=True if config.feat_extract_norm == '''layer''' else False
																		lowercase :							str    		=WavaVecaFeatureExtractor(
																		    feature_size=1   , sampling_rate=16000   , padding_value=0   , do_normalize=__magic_name__   , return_attention_mask=__magic_name__   , )
																		lowercase :							Tuple    		=WavaVecaProcessor(feature_extractor=__magic_name__   , tokenizer=__magic_name__						)
																		processor.save_pretrained(__magic_name__						)

												lowercase :							str    		=WavaVecaConformerForCTC(__magic_name__						)
						else:
												lowercase :							Tuple    		=WavaVecaConformerForPreTraining(__magic_name__						)

						if is_finetuned:
												lowercase		,	lowercase		,	lowercase :							Optional[int]    		=fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]   , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
						else:
												lowercase :							Dict    		=argparse.Namespace(task='''audio_pretraining'''						)
												lowercase :							Optional[int]    		=fairseq.tasks.setup_task(__magic_name__						)

												lowercase		,	lowercase		,	lowercase :							str    		=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   , task=__magic_name__						)

						lowercase :							List[Any]    		=model[0].eval()

						recursively_load_weights(__magic_name__   , __magic_name__   , not is_finetuned						)

						hf_wavavec.save_pretrained(__magic_name__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       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"""
							)
							UpperCamelCase_    						=       parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)








'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCamelCase_    						=       get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_    						=       128022
UpperCamelCase_    						=       128028





@require_sentencepiece
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    MaMaaaTokenizer
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    True







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										super().setUp()

										lowercase :							Dict    		=['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
										lowercase :							List[Any]    		=dict(zip(UpperCAmelCase__		,					range(len(UpperCAmelCase__   )   )   )   )
										lowercase :							List[Any]    		=Path(self.tmpdirname   )
										save_json(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

										lowercase :							Tuple    		=MaMaaaTokenizer.from_pretrained(self.tmpdirname   )
										tokenizer.save_pretrained(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		Any		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return MaMaaaTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										return (
										    "This is a test",
										    "This is a test",
										)







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''</s>'''
										lowercase :							Union[str, Any]    		=0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__   )		,					UpperCAmelCase__   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.get_tokenizer()
										lowercase :							Optional[Any]    		=list(tokenizer.get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]		,					'''</s>'''   )
										self.assertEqual(vocab_keys[1]		,					'''<unk>'''   )
										self.assertEqual(vocab_keys[-1]		,					'''<s>'''   )
										self.assertEqual(len(UpperCAmelCase__   )		,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()   )   )







				@unittest.skip('''Skip this test while all models are still to be uploaded.'''   )
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_tokenizer()

										lowercase :							str    		=tokenizer.tokenize('''This is a test'''   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					[2, 3, 4, 5, 6]		,					)

										lowercase :							Optional[int]    		=tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										lowercase :							Tuple    		=tokenizer.convert_tokens_to_string(UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					'''This is a test'''   )







				@slow
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										# fmt: off
										lowercase :							int    		={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCAmelCase__		,					model_name='''facebook/m2m100_418M'''		,					revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''		,					)





@require_torch
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):
				lowerCamelCase_		       =    'facebook/m2m100_418M'
				lowerCamelCase_		       =    [
				    'In my opinion, there are two levels of response from the French government.',
				    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
				]
				lowerCamelCase_		       =    [
				    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
				    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
				]

				# fmt: off
				lowerCamelCase_		       =    [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]







				@classmethod
				def  lowerCamelCase_	(       cls     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							MaMaaaTokenizer    		=MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name		,					src_lang='''en'''		,					tgt_lang='''fr'''   )
										lowercase :							Optional[int]    		=1
										return cls







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										self.assertEqual(self.tokenizer.get_lang_id('''ar'''   )		,					128006   )
										self.assertEqual(self.tokenizer.get_lang_id('''en'''   )		,					128022   )
										self.assertEqual(self.tokenizer.get_lang_id('''ro'''   )		,					128076   )
										self.assertEqual(self.tokenizer.get_lang_id('''mr'''   )		,					128063   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.tokenizer.get_vocab()
										self.assertEqual(len(UpperCAmelCase__   )		,					self.tokenizer.vocab_size   )
										self.assertEqual(vocab['''<unk>''']		,					3   )
										self.assertIn(self.tokenizer.get_lang_token('''en'''   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		='''en'''
										lowercase :							Optional[Any]    		=self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
										self.assertListEqual(self.expected_src_tokens		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										self.assertIn(UpperCAmelCase__		,					self.tokenizer.all_special_ids   )
										# fmt: off
										lowercase :							str    		=[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
										# fmt: on
										lowercase :							Optional[Any]    		=self.tokenizer.decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )
										self.assertNotIn(self.tokenizer.eos_token		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=tempfile.mkdtemp()
										lowercase :							Tuple    		=self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=MaMaaaTokenizer.from_pretrained(UpperCAmelCase__   )
										self.assertDictEqual(new_tok.lang_token_to_id		,					UpperCAmelCase__   )







				@require_torch
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''en'''
										lowercase :							int    		='''fr'''

										lowercase :							Union[str, Any]    		=self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=UpperCAmelCase__		,					return_tensors='''pt'''   )

										lowercase :							str    		=shift_tokens_right(
										    batch['''labels''']		,					self.tokenizer.pad_token_id		,					self.tokenizer.eos_token_id   )

										for k in batch:
																lowercase :							int    		=batch[k].tolist()
										# batch = {k: v.tolist() for k,v in batch.items()}
										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										# batch.decoder_inputs_ids[0][0] ==
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == FR_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







				@require_torch
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		='''mr'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )

										lowercase :							Union[str, Any]    		='''zh'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		='''mr'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )

										lowercase :							Optional[Any]    		='''zh'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.tokenizer._build_translation_inputs('''A test'''		,					return_tensors='''pt'''		,					src_lang='''en'''		,					tgt_lang='''ar'''   )

										self.assertEqual(
										    nested_simplify(UpperCAmelCase__   )		,					{
										        # en_XX, A, test, EOS
										        '''input_ids''': [[128022, 58, 4183, 2]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 128006,
										    }		,					)





