




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
		from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			)      ->       int:
	if attention_mask is None:
		a										=							input_ids.ne(config.pad_token_id    )
	if decoder_attention_mask is None:
		a										=							decoder_input_ids.ne(config.pad_token_id    )
	if head_mask is None:
		a										=							torch.ones(config.encoder_layers							,			config.encoder_attention_heads							,			device=__lowerCamelCase    )
	if decoder_head_mask is None:
		a										=							torch.ones(config.decoder_layers							,			config.decoder_attention_heads							,			device=__lowerCamelCase    )
	if cross_attn_head_mask is None:
		a										=							torch.ones(config.decoder_layers							,			config.decoder_attention_heads							,			device=__lowerCamelCase    )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:int=13		,      __magic_name__						:Optional[int]=7		,      __magic_name__						:Tuple=True		,      __magic_name__						:Optional[int]=False		,      __magic_name__						:List[Any]=99		,      __magic_name__						:List[str]=16		,      __magic_name__						:Dict=2		,      __magic_name__						:List[Any]=4		,      __magic_name__						:Dict=4		,      __magic_name__						:Optional[Any]="relu"		,      __magic_name__						:int=0.1		,      __magic_name__						:str=0.1		,      __magic_name__						:Any=0.0		,      __magic_name__						:Optional[int]=0.0		,      __magic_name__						:Any=20		,      __magic_name__						:Dict=2		,      __magic_name__						:Dict=1		,      __magic_name__						:Any=0		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							seq_length
								a										=							is_training
								a										=							use_labels
								a										=							vocab_size
								a										=							hidden_size
								a										=							num_hidden_layers
								a										=							num_attention_heads
								a										=							intermediate_size
								a										=							hidden_act
								a										=							hidden_dropout_prob
								a										=							attention_probs_dropout_prob
								a										=							encoder_layerdrop
								a										=							decoder_layerdrop
								a										=							max_position_embeddings
								a										=							eos_token_id
								a										=							pad_token_id
								a										=							bos_token_id
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size				)
								a										=							self.eos_token_id  # Eos Token
								a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size				)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for M2M100 the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								a										=							input_ids.clamp(self.pad_token_id + 1				)
								a										=							decoder_input_ids.clamp(self.pad_token_id + 1				)

								a										=							self.get_config()
								a										=							prepare_mam_aaa_inputs_dict(__magic_name__		,      __magic_name__		,      __magic_name__				)
								return config, inputs_dict
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return MaMaaaConfig(
								    vocab_size=self.vocab_size		,      d_model=self.hidden_size		,      encoder_layers=self.num_hidden_layers		,      decoder_layers=self.num_hidden_layers		,      encoder_attention_heads=self.num_attention_heads		,      decoder_attention_heads=self.num_attention_heads		,      encoder_ffn_dim=self.intermediate_size		,      decoder_ffn_dim=self.intermediate_size		,      dropout=self.hidden_dropout_prob		,      attention_dropout=self.attention_probs_dropout_prob		,      encoder_layerdrop=self.encoder_layerdrop		,      decoder_layerdrop=self.decoder_layerdrop		,      max_position_embeddings=self.max_position_embeddings		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.bos_token_id		,      pad_token_id=self.pad_token_id		,      )
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a				, a										=							self.prepare_config_and_inputs()
								return config, inputs_dict
							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:List[Any]		,      __magic_name__						:Any				):







								'''simple docstring'''







								a										=							MaMaaaModel(config=__magic_name__				).get_decoder().to(__magic_name__				).eval()
								a										=							inputs_dict["""input_ids"""]
								a										=							inputs_dict["""attention_mask"""]
								a										=							inputs_dict["""head_mask"""]

								# first forward pass
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      head_mask=__magic_name__		,      use_cache=__magic_name__				)

								a				, a										=							outputs.to_tuple()

								# create hypothetical multiple next token and extent to next_input_ids
								a										=							ids_tensor((self.batch_size, 3)		,      config.vocab_size				)
								a										=							ids_tensor((self.batch_size, 3)		,      2				)

								# append to next input_ids and
								a										=							torch.cat([input_ids, next_tokens]		,      dim=-1				)
								a										=							torch.cat([attention_mask, next_attn_mask]		,      dim=-1				)

								a										=							model(__magic_name__		,      attention_mask=__magic_name__				)["""last_hidden_state"""]
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      past_key_values=__magic_name__				)[
								    """last_hidden_state"""
								]

								# select random slice
								a										=							ids_tensor((1,)		,      output_from_past.shape[-1]				).item()
								a										=							output_from_no_past[:, -3:, random_slice_idx].detach()
								a										=							output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(__magic_name__		,      __magic_name__		,      atol=1E-2				)				)





							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:str		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							MaMaaaModel(config=__magic_name__				).to(__magic_name__				).eval()
								a										=							model(**__magic_name__				)

								a										=							outputs.encoder_last_hidden_state
								a										=							outputs.last_hidden_state

								with tempfile.TemporaryDirectory() as tmpdirname:
									a										=							model.get_encoder()
									encoder.save_pretrained(__magic_name__				)
									a										=							MaMaaaEncoder.from_pretrained(__magic_name__				).to(__magic_name__				)

								a										=							encoder(inputs_dict["""input_ids"""]		,      attention_mask=inputs_dict["""attention_mask"""]				)[
								    0
								]

								self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

								with tempfile.TemporaryDirectory() as tmpdirname:
									a										=							model.get_decoder()
									decoder.save_pretrained(__magic_name__				)
									a										=							MaMaaaDecoder.from_pretrained(__magic_name__				).to(__magic_name__				)

								a										=							decoder(
								    input_ids=inputs_dict["""decoder_input_ids"""]		,      attention_mask=inputs_dict["""decoder_attention_mask"""]		,      encoder_hidden_states=__magic_name__		,      encoder_attention_mask=inputs_dict["""attention_mask"""]		,      )[0]

								self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)







@require_torch
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  (
							    (
							        MaMaaaModel,
							        MaMaaaForConditionalGeneration,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__					     =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
							UpperCamelCase__					     =  (
							    {
							        '''conversational''': MaMaaaForConditionalGeneration,
							        '''feature-extraction''': MaMaaaModel,
							        '''summarization''': MaMaaaForConditionalGeneration,
							        '''text2text-generation''': MaMaaaForConditionalGeneration,
							        '''translation''': MaMaaaForConditionalGeneration,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:int		,      __magic_name__						:int		,      __magic_name__						:int		,      __magic_name__						:List[str]		,      __magic_name__						:str				):







								'''simple docstring'''







								if pipeline_test_casse_name == "TranslationPipelineTests":
									# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
									# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
									return True

								return False
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							MaMaaaModelTester(self				)
								a										=							ConfigTester(self		,      config_class=__magic_name__				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									a										=							model_class(__magic_name__				)

									with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__magic_name__				)
										a				, a										=							model_class.from_pretrained(__magic_name__		,      output_loading_info=__magic_name__				)
									self.assertEqual(info["""missing_keys"""]		,      []				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_encoder_decoder_model_standalone(*__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
									a										=							model_class(__magic_name__				)
									model.to(__magic_name__				)
									model.eval()

									a										=							copy.deepcopy(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)

									if not self.is_encoder_decoder:
										a										=							inputs["""input_ids"""]
										del inputs["input_ids"]
									else:
										a										=							inputs["""input_ids"""]
										a										=							inputs.get("""decoder_input_ids"""		,      __magic_name__				)
										del inputs["input_ids"]
										inputs.pop("""decoder_input_ids"""		,      __magic_name__				)

									a										=							model.get_input_embeddings()
									if not self.is_encoder_decoder:
										a										=							wte(__magic_name__				)
									else:
										a										=							wte(__magic_name__				)
										a										=							wte(__magic_name__				)

									with torch.no_grad():
										model(**__magic_name__				)[0]





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs()
								a										=							input_dict["""input_ids"""]
								a										=							input_ids.ne(1				).to(__magic_name__				)
								a										=							MaMaaaForConditionalGeneration(__magic_name__				).eval().to(__magic_name__				)
								if torch_device == "cuda":
									model.half()
								model.generate(__magic_name__		,      attention_mask=__magic_name__				)
								model.generate(num_beams=4		,      do_sample=__magic_name__		,      early_stopping=__magic_name__		,      num_return_sequences=3				)







def 		__A		(	__lowerCamelCase    )      ->       List[Any]:
	return torch.tensor(__lowerCamelCase							,			dtype=torch.long							,			device=__lowerCamelCase    )


__UpperCamelCase					:       Dict    					=      1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@cached_property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""				).to(__magic_name__				)
								a										=							_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]				)
								a										=							_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]				)
								a										=							prepare_mam_aaa_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								with torch.no_grad():
									a										=							model(**__magic_name__				)[0]
								a										=							torch.Size((1, 11, 1024)				)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							torch.tensor(
								    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]		,      device=__magic_name__				)
								self.assertTrue(torch.allclose(output[:, :3, :3]		,      __magic_name__		,      atol=__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""				).to(__magic_name__				)

								# change to intended input
								a										=							_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]				)
								a										=							_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]				)
								a										=							prepare_mam_aaa_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								with torch.no_grad():
									a										=							model(**__magic_name__				)[0]
								a										=							torch.Size((1, 11, model.config.vocab_size)				)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							torch.tensor(
								    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]		,      device=__magic_name__				)
								self.assertTrue(torch.allclose(output[:, :3, :3]		,      __magic_name__		,      atol=__magic_name__				)				)





							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""				).to(__magic_name__				)
								a										=							MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""		,      src_lang="""fr"""		,      tgt_lang="""en"""				)

								a										=							[
								    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
								    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
								    """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
								    """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
								    """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
								]

								# The below article tests that we don't add any hypotheses outside of the top n_beams
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      return_tensors="""pt"""				)

								a										=							model.generate(
								    input_ids=dct["""input_ids"""].to(__magic_name__				)		,      attention_mask=dct["""attention_mask"""].to(__magic_name__				)		,      num_beams=5		,      forced_bos_token_id=tokenizer.get_lang_id("""en"""				)		,      )

								a										=							[
								    """The NSA case highlights the total absence of intelligence debate""",
								    """I think there are two levels of response from the French government.""",
								    """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
								    """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
								    """ communications in France.""",
								]

								a										=							tokenizer.batch_decode(
								    hypotheses_batch.tolist()		,      clean_up_tokenization_spaces=__magic_name__		,      skip_special_tokens=__magic_name__				)
								assert generated == expected_en







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__UpperCamelCase					:       Union[str, Any]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Any    					=      {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__UpperCamelCase					:       Dict    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 		__A		(	__lowerCamelCase    )      ->       List[str]:
	a										=							{}
	with open(__lowerCamelCase							,			"""r"""    ) as file:
		for line_number, line in enumerate(__lowerCamelCase    ):
			a										=							line.strip()
			if line:
				a										=							line.split()
				a										=							line_number
				a										=							words[0]
				a										=							value
	return result





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	elif weight_type is not None and weight_type == "param":
		a										=							hf_pointer
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		a										=							shape_pointer.shape

		# let's reduce dimension
		a										=							value[0]
	else:
		a										=							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":
		a										=							value
	elif weight_type == "weight_g":
		a										=							value
	elif weight_type == "weight_v":
		a										=							value
	elif weight_type == "bias":
		a										=							value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		a										=							value
	else:
		a										=							value

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							""".""".join([key, weight_type]    )
	elif weight_type is not None and weight_type == "param":
		a										=							""".""".join([key, hf_param_name]    )
	else:
		a										=							key

	a										=							value if """lm_head""" in full_key else value[0]


__UpperCamelCase					:       List[Any]    					=      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Optional[Any]:
	a										=							False
	for key, mapped_key in MAPPING.items():
		a										=							"""wav2vec2.""" + 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]:
			a										=							True
			if "*" in mapped_key:
				a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
				a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
			if "weight_g" in name:
				a										=							"""weight_g"""
			elif "weight_v" in name:
				a										=							"""weight_v"""
			elif "bias" in name:
				a										=							"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				a										=							"""weight"""
			else:
				a										=							None
			if hf_dict is not None:
				rename_dict(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			else:
				set_recursively(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			return is_used
	return is_used





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Any]:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		a										=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		else:
			a										=							load_wavaveca_layer(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
		if not is_used:
			unused_weights.append(__lowerCamelCase    )

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Any:
	a										=							full_name.split("""conv_layers."""    )[-1]
	a										=							name.split("""."""    )
	a										=							int(items[0]    )
	a										=							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.'    )
			a										=							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.'    )
			a										=							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.'    )
			a										=							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.'    )
			a										=							value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=True							,			__lowerCamelCase=False    )      ->       List[Any]:
	if config_path is not None:
		a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	else:
		a										=							WavaVecaConfig()

	if is_seq_class:
		a										=							read_txt_into_dict(__lowerCamelCase    )
		a										=							idalabel
		a										=							WavaVecaForSequenceClassification(__lowerCamelCase    )
		a										=							WavaVecaFeatureExtractor(
		    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
		feature_extractor.save_pretrained(__lowerCamelCase    )

	elif is_finetuned:
		if dict_path:
			a										=							Dictionary.load(__lowerCamelCase    )

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

			# fairseq has the <pad> and <s> switched
			a										=							0
			a										=							1
			with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as vocab_handle:
				json.dump(__lowerCamelCase							,			__lowerCamelCase    )
			a										=							WavaVecaCTCTokenizer(
			    __lowerCamelCase							,			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=__lowerCamelCase							,			)
			a										=							True if config.feat_extract_norm == """layer""" else False
			a										=							WavaVecaFeatureExtractor(
			    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
			a										=							WavaVecaProcessor(feature_extractor=__lowerCamelCase							,			tokenizer=__lowerCamelCase    )
			processor.save_pretrained(__lowerCamelCase    )

		a										=							WavaVecaForCTC(__lowerCamelCase    )
	else:
		a										=							WavaVecaForPreTraining(__lowerCamelCase    )

	if is_finetuned or is_seq_class:
		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
	else:
		a										=							argparse.Namespace(task="""audio_pretraining"""    )
		a										=							fairseq.tasks.setup_task(__lowerCamelCase    )

		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,			task=__lowerCamelCase    )

	a										=							model[0].eval()

	recursively_load_weights(__lowerCamelCase							,			__lowerCamelCase							,			not is_finetuned    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       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_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"
		)
		parser.add_argument(
		    "--is_seq_class",
		    action="store_true",
		    help="Whether the model to convert is a fine-tuned sequence classification model or not",
		)
		__UpperCamelCase					:       Union[str, Any]    					=      parser.parse_args()

		__UpperCamelCase					:       Any    					=      not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)

