"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase     :				Optional[int]    				=    get_tests_dir("fixtures/test_sentencepiece.model")
__lowerCAmelCase     :				List[str]    				=    get_tests_dir("fixtures/test_sentencepiece_bpe.model")

__lowerCAmelCase     :				Tuple    				=    "pt" if is_torch_available() else "tf"

@require_sentencepiece
@require_tokenizers
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[str]      =     CamembertTokenizer
					UpperCamelCase_			:						List[str]      =     CamembertTokenizerFast
					UpperCamelCase_			:						Union[str, Any]      =     True
					UpperCamelCase_			:						Union[str, Any]      =     True




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__   = CamembertTokenizer(snake_case__  )
							tokenizer.save_pretrained(self.tmpdirname  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = """<pad>"""
							lowerCAmelCase__   = 1

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = list(self.get_tokenizer().get_vocab().keys()  )

							self.assertEqual(vocab_keys[0]	,    """<s>NOTUSED"""  )
							self.assertEqual(vocab_keys[1]	,    """<pad>"""  )
							self.assertEqual(vocab_keys[-1]	,    """<mask>"""  )
							self.assertEqual(len(snake_case__  )	,    1004  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							self.assertEqual(self.get_tokenizer().vocab_size	,    1005  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = CamembertTokenizer(snake_case__  )
							tokenizer.save_pretrained(self.tmpdirname  )
							lowerCAmelCase__   = CamembertTokenizerFast.from_pretrained(self.tmpdirname  )

							lowerCAmelCase__   = """I was born in 92000, and this is falsé."""

							lowerCAmelCase__   = tokenizer.encode(snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.encode(snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )

							lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							lowerCAmelCase__   = tokenizer.convert_ids_to_tokens(snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.tokenize(snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							if not self.test_rust_tokenizer:
									return

							lowerCAmelCase__   = self.get_tokenizer()
							lowerCAmelCase__   = self.get_rust_tokenizer()

							lowerCAmelCase__   = """I was born in 92000, and this is falsé."""

							lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.tokenize(snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )

							lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )

							lowerCAmelCase__   = self.get_rust_tokenizer()
							lowerCAmelCase__   = tokenizer.encode(snake_case__  )
							lowerCAmelCase__   = rust_tokenizer.encode(snake_case__  )
							self.assertListEqual(snake_case__	,    snake_case__  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							# fmt: off
							lowerCAmelCase__   = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 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, 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]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							lowerCAmelCase__   = [
							    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
							    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
							    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
							    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
							    """telles que la traduction et la synthèse de texte.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case__	,    model_name="""camembert-base"""	,    revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""	,    sequences=snake_case__	,    )





"""simple docstring"""




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)

__lowerCAmelCase     :				Dict    				=    {
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__lowerCAmelCase     :				Optional[Any]    				=    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		for attribute in key.split("""."""  ):
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  )

		if weight_type is not None:
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  ).shape
		else:
				lowerCAmelCase__   = 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":
				lowerCAmelCase__   = value
		elif weight_type == "weight_g":
				lowerCAmelCase__   = value
		elif weight_type == "weight_v":
				lowerCAmelCase__   = value
		elif weight_type == "bias":
				lowerCAmelCase__   = value
		else:
				lowerCAmelCase__   = value

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		lowerCAmelCase__   = fairseq_model.state_dict()

		lowerCAmelCase__   = hf_model.feature_extractor
		lowerCAmelCase__   = hf_model.adapter

		for name, value in fairseq_dict.items():
				lowerCAmelCase__   = False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	hf_model.config.feat_extract_norm == """group"""      ,	)
						lowerCAmelCase__   = True
				elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]  ):
						load_adapter(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
						lowerCAmelCase__   = True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
										lowerCAmelCase__   = True
										if "*" in mapped_key:
												lowerCAmelCase__   = name.split(lowerCamelCase__  )[0].split("""."""  )[-2]
												lowerCAmelCase__   = mapped_key.replace("""*"""      ,	lowerCamelCase__  )
										if "weight_g" in name:
												lowerCAmelCase__   = """weight_g"""
										elif "weight_v" in name:
												lowerCAmelCase__   = """weight_v"""
										elif "bias" in name:
												lowerCAmelCase__   = """bias"""
										elif "weight" in name:
												lowerCAmelCase__   = """weight"""
										else:
												lowerCAmelCase__   = None
										set_recursively(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
								continue
				if not is_used:
						unused_weights.append(lowerCamelCase__  )

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""conv_layers."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )
		lowerCAmelCase__   = int(items[0]  )
		lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""adaptor."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )

		if items[1].isdigit():
				lowerCAmelCase__   = int(items[1]  )
		else:
				lowerCAmelCase__   = None

		if "adaptor" not in full_name:
				if "proj_ln" in full_name:
						# has to be layer norm
						if "bias" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.bias.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""  )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.weight.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
								lowerCAmelCase__   = value
				else:
						# has to be projection layer
						if "bias" in name:
								assert (
								    value.shape == adapter.proj.bias.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""  )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj.weight.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
		elif isinstance(lowerCamelCase__      ,	lowerCamelCase__  ):
				if "bias" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.bias.data.shape
						), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
				elif "weight" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.weight.data.shape
						), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = emb.weight.shape
		lowerCAmelCase__   = nn.Linear(lowerCamelCase__      ,	lowerCamelCase__      ,	bias=lowerCamelCase__  )
		lowerCAmelCase__   = emb.weight.data
		return lin_layer







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = WavaVecaConfig.from_pretrained(
		    lowerCamelCase__      ,	add_adapter=lowerCamelCase__      ,	adapter_stride=lowerCamelCase__      ,	adapter_kernel_size=lowerCamelCase__      ,	use_auth_token=lowerCamelCase__      ,	output_hidden_size=lowerCamelCase__      ,	)
		lowerCAmelCase__   = MBartConfig.from_pretrained(lowerCamelCase__  )

		# load model
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]      ,	arg_overrides={
		        """config_yaml""": config_yaml_path,
		        """data""": """/""".join(dict_path.split("""/"""  )[:-1]  ),
		        """w2v_path""": checkpoint_path,
		        """load_pretrained_decoder_from""": None,
		    }      ,	)
		lowerCAmelCase__   = model[0].eval()

		# load feature extractor
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__      ,	use_auth_token=lowerCamelCase__  )

		# set weights for wav2vec2 encoder
		lowerCAmelCase__   = WavaVecaModel(lowerCamelCase__  )

		recursively_load_weights_wavaveca(model.encoder      ,	lowerCamelCase__  )

		# load decoder weights
		lowerCAmelCase__   = MBartForCausalLM(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,	strict=lowerCamelCase__  )
		logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""  )
		logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""  )

		lowerCAmelCase__   = SpeechEncoderDecoderModel(encoder=lowerCamelCase__      ,	decoder=lowerCamelCase__  )
		lowerCAmelCase__   = False

		lowerCAmelCase__   = MBartaaTokenizer(lowerCamelCase__  )
		tokenizer.save_pretrained(lowerCamelCase__  )

		lowerCAmelCase__   = hf_wavavec.config.to_dict()
		lowerCAmelCase__   = tokenizer.pad_token_id
		lowerCAmelCase__   = tokenizer.bos_token_id
		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = """mbart50"""
		lowerCAmelCase__   = """wav2vec2"""

		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = 25_0004
		lowerCAmelCase__   = tokenizer.eos_token_id

		lowerCAmelCase__   = SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

		hf_wavavec.save_pretrained(lowerCamelCase__  )
		feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				int    				=    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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
			parser.add_argument(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-xls-r-1b",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/mbart-large-50-one-to-many-mmt",
			    type=str,
			    help="Path to hf decoder checkpoint config",
			)
			parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
			parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
			parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
			parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

			__lowerCAmelCase     :				Union[str, Any]    				=    parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)




