




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:List[Any]		,      __magic_name__						:Optional[int]=99		,      __magic_name__						:Any=13		,      __magic_name__						:List[str]=7		,      __magic_name__						:str=9		,      __magic_name__						:Union[str, Any]=True		,      __magic_name__						:Optional[int]=True		,      __magic_name__						:Optional[int]=False		,      __magic_name__						:Union[str, Any]=32		,      __magic_name__						:Union[str, Any]=5		,      __magic_name__						:Any=4		,      __magic_name__						:Optional[Any]=37		,      __magic_name__						:Any=8		,      __magic_name__						:Union[str, Any]=0.1		,      __magic_name__						:List[Any]=0.002		,      __magic_name__						:int=1		,      __magic_name__						:Tuple=0		,      __magic_name__						:List[Any]=0		,      __magic_name__						:List[str]=None		,      __magic_name__						:Optional[int]=None		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							encoder_seq_length
								a										=							decoder_seq_length
								# For common tests
								a										=							self.decoder_seq_length
								a										=							is_training
								a										=							use_attention_mask
								a										=							use_labels
								a										=							vocab_size
								a										=							hidden_size
								a										=							num_hidden_layers
								a										=							num_attention_heads
								a										=							d_ff
								a										=							relative_attention_num_buckets
								a										=							dropout_rate
								a										=							initializer_factor
								a										=							eos_token_id
								a										=							pad_token_id
								a										=							decoder_start_token_id
								a										=							None
								a										=							decoder_layers
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return TaConfig.from_pretrained("""google/umt5-base"""				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Dict		,      __magic_name__						:List[Any]		,      __magic_name__						:int		,      __magic_name__						:int=None		,      __magic_name__						:Dict=None		,      __magic_name__						:Union[str, Any]=None		,      __magic_name__						:List[Any]=None		,      __magic_name__						:Optional[int]=None		,      ):







								'''simple docstring'''







								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.num_hidden_layers		,      config.num_attention_heads		,      device=__magic_name__				)
								if decoder_head_mask is None:
									a										=							torch.ones(config.num_decoder_layers		,      config.num_attention_heads		,      device=__magic_name__				)
								if cross_attn_head_mask is None:
									a										=							torch.ones(
									    config.num_decoder_layers		,      config.num_attention_heads		,      device=__magic_name__				)
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							ids_tensor([self.batch_size, self.encoder_seq_length]		,      self.vocab_size				)
								a										=							ids_tensor([self.batch_size, self.decoder_seq_length]		,      self.vocab_size				)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe 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										=							config.num_attention_heads
								a										=							self.prepare_inputs_dict(__magic_name__		,      __magic_name__		,      __magic_name__				)
								return config, input_dict
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a				, a										=							self.prepare_config_and_inputs()
								return config, inputs_dict
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								return TaConfig(
								    vocab_size=166		,      d_model=self.hidden_size		,      d_ff=self.d_ff		,      d_kv=self.hidden_size // self.num_attention_heads		,      num_layers=self.num_hidden_layers		,      num_decoder_layers=self.decoder_layers		,      num_heads=self.num_attention_heads		,      relative_attention_num_buckets=self.relative_attention_num_buckets		,      dropout_rate=self.dropout_rate		,      initializer_factor=self.initializer_factor		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.pad_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      )
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return TaConfig(
								    vocab_size=self.vocab_size		,      d_model=self.hidden_size		,      d_ff=self.d_ff		,      d_kv=self.hidden_size // self.num_attention_heads		,      num_layers=self.num_hidden_layers		,      num_decoder_layers=self.decoder_layers		,      num_heads=self.num_attention_heads		,      relative_attention_num_buckets=self.relative_attention_num_buckets		,      dropout_rate=self.dropout_rate		,      initializer_factor=self.initializer_factor		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.pad_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      )
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:Optional[int]		,      __magic_name__						:Tuple		,      __magic_name__						:int		,      __magic_name__						:Any		,      __magic_name__						:Optional[Any]		,      ):







								'''simple docstring'''







								a										=							UMTaModel(config=__magic_name__				)
								model.to(__magic_name__				)
								model.eval()
								a										=							model(
								    input_ids=__magic_name__		,      decoder_input_ids=__magic_name__		,      attention_mask=__magic_name__		,      decoder_attention_mask=__magic_name__		,      )
								a										=							model(input_ids=__magic_name__		,      decoder_input_ids=__magic_name__				)
								a										=							result.last_hidden_state
								a										=							result.past_key_values
								a										=							result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()		,      (self.batch_size, self.encoder_seq_length, self.hidden_size)				)
								self.parent.assertEqual(decoder_output.size()		,      (self.batch_size, self.decoder_seq_length, self.hidden_size)				)
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(__magic_name__				)		,      config.num_layers				)
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]				)		,      4				)
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:List[str]		,      __magic_name__						:Optional[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:List[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:Optional[Any]		,      ):







								'''simple docstring'''







								a										=							UMTaModel(config=__magic_name__				).get_decoder().to(__magic_name__				).eval()
								# first forward pass
								a										=							model(__magic_name__		,      use_cache=__magic_name__				)
								a										=							model(__magic_name__				)
								a										=							model(__magic_name__		,      use_cache=__magic_name__				)

								self.parent.assertTrue(len(__magic_name__				) == len(__magic_name__				)				)
								self.parent.assertTrue(len(__magic_name__				) == len(__magic_name__				) + 1				)

								a				, a										=							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a										=							ids_tensor((self.batch_size, 1)		,      config.vocab_size				)

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

								a										=							model(__magic_name__				)["""last_hidden_state"""]
								a										=							model(__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[:, -1, random_slice_idx].detach()
								a										=							output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(__magic_name__		,      __magic_name__		,      atol=1E-3				)				)





							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Any		,      __magic_name__						:Union[str, Any]		,      ):







								'''simple docstring'''







								a										=							UMTaModel(config=__magic_name__				).to(__magic_name__				).half().eval()
								a										=							model(**__magic_name__				)["""last_hidden_state"""]
								self.parent.assertFalse(torch.isnan(__magic_name__				).any().item()				)







@require_torch
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  (
							    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
							)
							UpperCamelCase__					     =  (UMTaForConditionalGeneration,) if is_torch_available() else ()
							UpperCamelCase__					     =  (
							    {
							        '''conversational''': UMTaForConditionalGeneration,
							        '''feature-extraction''': UMTaModel,
							        '''summarization''': UMTaForConditionalGeneration,
							        '''text2text-generation''': UMTaForConditionalGeneration,
							        '''translation''': UMTaForConditionalGeneration,
							        '''question-answering''': UMTaForQuestionAnswering,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  True
							# The small UMT5 model needs higher percentages for CPU/MP tests
							UpperCamelCase__					     =  [0.8, 0.9]
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								a										=							UMTaModelTester(self				)
							@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs()
								a										=							UMTaModel(config_and_inputs[0]				).to(__magic_name__				)
								with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    __magic_name__		,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,      F'{tmpdirname}/t5_test.onnx'		,      export_params=__magic_name__		,      opset_version=9		,      input_names=["""input_ids""", """decoder_input_ids"""]		,      )
							@unittest.skipIf(torch_device == """cpu"""		,      """Cant do half precision"""				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







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







								'''simple docstring'''







								a										=							["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
								a										=							self.model_tester.prepare_config_and_inputs()
								a										=							config_and_inputs[0]
								a										=							UMTaForConditionalGeneration(__magic_name__				).eval()
								model.to(__magic_name__				)

								a										=							{
								    """head_mask""": torch.zeros(config.num_layers		,      config.num_heads		,      device=__magic_name__				),
								    """decoder_head_mask""": torch.zeros(config.num_decoder_layers		,      config.num_heads		,      device=__magic_name__				),
								    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers		,      config.num_heads		,      device=__magic_name__				),
								}

								for attn_name, (name, mask) in zip(__magic_name__		,      head_masking.items()				):
									a										=							{name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
										a										=							torch.ones(
										    config.num_decoder_layers		,      config.num_heads		,      device=__magic_name__				)

									a										=							model.generate(
									    config_and_inputs[1]["""input_ids"""]		,      num_beams=1		,      max_length=3		,      output_attentions=__magic_name__		,      return_dict_in_generate=__magic_name__		,      **__magic_name__		,      )
									# We check the state of decoder_attentions and cross_attentions just from the last step
									a										=							out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
									self.assertEqual(sum([w.sum().item() for w in attn_weights]				)		,      0.0				)





							@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@slow
							@unittest.skip(
							    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""		,      return_dict=__magic_name__				).to(__magic_name__				)
								a										=							AutoTokenizer.from_pretrained("""google/umt5-small"""		,      use_fast=__magic_name__		,      legacy=__magic_name__				)
								a										=							[
								    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
								    """No se como puedo <extra_id_0>.""",
								    """This is the reason why we <extra_id_0> them.""",
								    """The <extra_id_0> walks in <extra_id_1>, seats""",
								    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
								]
								a										=							tokenizer(__magic_name__		,      return_tensors="""pt"""		,      padding=__magic_name__				).input_ids
								# fmt: off
								a										=							torch.tensor(
								    [
								        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
								    ]				)
								# fmt: on
								torch.testing.assert_allclose(__magic_name__		,      __magic_name__				)

								a										=							model.generate(input_ids.to(__magic_name__				)				)
								a										=							[
								    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
								    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
								]
								a										=							tokenizer.batch_decode(__magic_name__				)
								self.assertEqual(__magic_name__		,      __magic_name__				)







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()
__UpperCamelCase					:       str    					=      logging.get_logger(__name__)

__UpperCamelCase					:       List[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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__UpperCamelCase					:       Union[str, Any]    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	if weight_type is not None:
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	else:
		a										=							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":
		a										=							value
	elif weight_type == "weight_g":
		a										=							value
	elif weight_type == "weight_v":
		a										=							value
	elif weight_type == "bias":
		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    )      ->       int:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.feature_extractor
	a										=							hf_model.adapter

	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
		elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
			load_adapter(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			a										=							True
		else:
			for key, mapped_key in MAPPING.items():
				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:
						a										=							"""weight"""
					else:
						a										=							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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple:
	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:
			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.'
			)
			a										=							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.'
			)
			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:
			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."
			)
			a										=							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.'
			)
			a										=							value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Any:
	a										=							full_name.split("""adaptor."""    )[-1]
	a										=							name.split("""."""    )

	if items[1].isdigit():
		a										=							int(items[1]    )
	else:
		a										=							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.'
				a										=							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.'
				a										=							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.'
				a										=							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.'
				a										=							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.'
			a										=							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.'
			a										=							value
			logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





def 		__A		(	__lowerCamelCase    )      ->       Tuple:
	a				, a										=							emb.weight.shape
	a										=							nn.Linear(__lowerCamelCase							,			__lowerCamelCase							,			bias=__lowerCamelCase    )
	a										=							emb.weight.data
	return lin_layer





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			)      ->       Optional[int]:
	a										=							WavaVecaConfig.from_pretrained(
	    __lowerCamelCase							,			add_adapter=__lowerCamelCase							,			adapter_stride=__lowerCamelCase							,			adapter_kernel_size=__lowerCamelCase							,			use_auth_token=__lowerCamelCase							,			output_hidden_size=__lowerCamelCase							,			)
	a										=							MBartConfig.from_pretrained(__lowerCamelCase    )

	# load model
	a				, a				, a										=							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,
	    }							,			)
	a										=							model[0].eval()

	# load feature extractor
	a										=							WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase							,			use_auth_token=__lowerCamelCase    )

	# set weights for wav2vec2 encoder
	a										=							WavaVecaModel(__lowerCamelCase    )

	recursively_load_weights_wavaveca(model.encoder							,			__lowerCamelCase    )

	# load decoder weights
	a										=							MBartForCausalLM(__lowerCamelCase    )
	a				, a										=							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}'    )

	a										=							SpeechEncoderDecoderModel(encoder=__lowerCamelCase							,			decoder=__lowerCamelCase    )
	a										=							False

	a										=							MBartaaTokenizer(__lowerCamelCase    )
	tokenizer.save_pretrained(__lowerCamelCase    )

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

	a										=							tokenizer.eos_token_id
	a										=							25_0004
	a										=							tokenizer.eos_token_id

	a										=							SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )
	feature_extractor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       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_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=1_024, type=int, help="encoder output dim")
		parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

		__UpperCamelCase					:       int    					=      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,
		)

