


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
				import torch

				from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class 							_UpperCamelCase :

			'''simple docstring'''







			def __init__(  self  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Any=1_3       ,  _lowerCamelCase  : List[str]=7       ,  _lowerCamelCase  : int=True       ,  _lowerCamelCase  : Union[str, Any]=True       ,  _lowerCamelCase  : Dict=False       ,  _lowerCamelCase  : List[Any]=True       ,  _lowerCamelCase  : Optional[Any]=9_9       ,  _lowerCamelCase  : List[Any]=3_2       ,  _lowerCamelCase  : Union[str, Any]=5       ,  _lowerCamelCase  : Union[str, Any]=4       ,  _lowerCamelCase  : str=3_7       ,  _lowerCamelCase  : str="gelu"       ,  _lowerCamelCase  : Optional[Any]=0.1       ,  _lowerCamelCase  : int=0.1       ,  _lowerCamelCase  : str=5_1_2       ,  _lowerCamelCase  : Tuple=1_6       ,  _lowerCamelCase  : int=2       ,  _lowerCamelCase  : Optional[Any]=0.02       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Union[str, Any]=4       ,  _lowerCamelCase  : List[str]=None       ,  ):






									'''simple docstring'''



									__lowerCamelCase     :      int       						=						parent
									__lowerCamelCase     :      Optional[Any]       						=						batch_size
									__lowerCamelCase     :      str       						=						seq_length
									__lowerCamelCase     :      List[str]       						=						is_training
									__lowerCamelCase     :      Dict       						=						use_input_mask
									__lowerCamelCase     :      Optional[Any]       						=						use_token_type_ids
									__lowerCamelCase     :      int       						=						use_labels
									__lowerCamelCase     :      int       						=						vocab_size
									__lowerCamelCase     :      Union[str, Any]       						=						hidden_size
									__lowerCamelCase     :      int       						=						num_hidden_layers
									__lowerCamelCase     :      Any       						=						num_attention_heads
									__lowerCamelCase     :      Any       						=						intermediate_size
									__lowerCamelCase     :      str       						=						hidden_act
									__lowerCamelCase     :      str       						=						hidden_dropout_prob
									__lowerCamelCase     :      List[Any]       						=						attention_probs_dropout_prob
									__lowerCamelCase     :      Dict       						=						max_position_embeddings
									__lowerCamelCase     :      int       						=						type_vocab_size
									__lowerCamelCase     :      Optional[int]       						=						type_sequence_label_size
									__lowerCamelCase     :      Dict       						=						initializer_range
									__lowerCamelCase     :      List[str]       						=						num_labels
									__lowerCamelCase     :      List[str]       						=						num_choices
									__lowerCamelCase     :      Optional[int]       						=						scope

			def   _snake_case    (  self  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase     :      int       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

									__lowerCamelCase     :      Optional[int]       						=						None
									if self.use_input_mask:
															__lowerCamelCase     :      Dict       						=						random_attention_mask([self.batch_size, self.seq_length]							)

									__lowerCamelCase     :      int       						=						None
									if self.use_token_type_ids:
															__lowerCamelCase     :      List[str]       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size							)

									__lowerCamelCase     :      Dict       						=						None
									__lowerCamelCase     :      Dict       						=						None
									__lowerCamelCase     :      Dict       						=						None
									if self.use_labels:
															__lowerCamelCase     :      List[str]       						=						ids_tensor([self.batch_size]       ,  self.type_sequence_label_size							)
															__lowerCamelCase     :      Union[str, Any]       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels							)
															__lowerCamelCase     :      Dict       						=						ids_tensor([self.batch_size]       ,  self.num_choices							)

									__lowerCamelCase     :      List[Any]       						=						self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def   _snake_case    (  self  : Union[str, Any]							):






									'''simple docstring'''



									return OpenLlamaConfig(
									    vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  type_vocab_size=self.type_vocab_size       ,  is_decoder=_lowerCamelCase       ,  initializer_range=self.initializer_range       ,  use_stable_embedding=_lowerCamelCase       ,  )

			def   _snake_case    (  self  : str       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : str							):






									'''simple docstring'''



									__lowerCamelCase     :      Union[str, Any]       						=						OpenLlamaModel(config=_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      Dict       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase							)
									__lowerCamelCase     :      Dict       						=						model(_lowerCamelCase							)
									self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)

			def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Optional[Any]       ,  ):






									'''simple docstring'''



									__lowerCamelCase     :      int       						=						True
									__lowerCamelCase     :      Any       						=						OpenLlamaModel(_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      List[str]       						=						model(
									    _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  )
									__lowerCamelCase     :      Any       						=						model(
									    _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  )
									__lowerCamelCase     :      List[str]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase							)
									self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)

			def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Tuple       ,  ):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[Any]       						=						OpenLlamaForCausalLM(config=_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      Tuple       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  labels=_lowerCamelCase							)
									self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)

			def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  ):






									'''simple docstring'''



									__lowerCamelCase     :      Dict       						=						True
									__lowerCamelCase     :      List[str]       						=						True
									__lowerCamelCase     :      List[Any]       						=						OpenLlamaForCausalLM(config=_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()

									# first forward pass
									__lowerCamelCase     :      List[str]       						=						model(
									    _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  use_cache=_lowerCamelCase       ,  )
									__lowerCamelCase     :      int       						=						outputs.past_key_values

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

									# append to next input_ids and
									__lowerCamelCase     :      Tuple       						=						torch.cat([input_ids, next_tokens]       ,  dim=-1							)
									__lowerCamelCase     :      Optional[int]       						=						torch.cat([input_mask, next_mask]       ,  dim=-1							)

									__lowerCamelCase     :      Optional[int]       						=						model(
									    _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  output_hidden_states=_lowerCamelCase       ,  )["""hidden_states"""][0]
									__lowerCamelCase     :      Optional[int]       						=						model(
									    _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  past_key_values=_lowerCamelCase       ,  output_hidden_states=_lowerCamelCase       ,  )["""hidden_states"""][0]

									# select random slice
									__lowerCamelCase     :      List[str]       						=						ids_tensor((1,)       ,  output_from_past.shape[-1]							).item()
									__lowerCamelCase     :      str       						=						output_from_no_past[:, -3:, random_slice_idx].detach()
									__lowerCamelCase     :      List[Any]       						=						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(_lowerCamelCase       ,  _lowerCamelCase       ,  atol=1E-3							)							)


			def   _snake_case    (  self  : Any							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[Any]       						=						self.prepare_config_and_inputs()
									(
									    __lowerCamelCase 
									)     :      str       						=						config_and_inputs
									__lowerCamelCase     :      str       						=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict



@require_torch
class 							_UpperCamelCase (						A,A,A,unittest.TestCase						):

			'''simple docstring'''







			a_				:							Dict    = (
			    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
			)
			a_				:							Any    = (OpenLlamaForCausalLM,) if is_torch_available() else ()
			a_				:							Optional[int]    = (
			    {
			        "feature-extraction": OpenLlamaModel,
			        "text-classification": OpenLlamaForSequenceClassification,
			        "text-generation": OpenLlamaForCausalLM,
			        "zero-shot": OpenLlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			a_				:							Any    = False
			a_				:							Dict    = False

			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[int]       						=						OpenLlamaModelTester(self							)
									__lowerCamelCase     :      int       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  hidden_size=3_7							)

			def   _snake_case    (  self  : List[str]							):






									'''simple docstring'''



									self.config_tester.run_common_tests()

			def   _snake_case    (  self  : str							):






									'''simple docstring'''



									__lowerCamelCase     :      List[str]       						=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowerCamelCase							)

			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									__lowerCamelCase     :      Union[str, Any]       						=						self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															__lowerCamelCase     :      Optional[int]       						=						type
															self.model_tester.create_and_check_model(*_lowerCamelCase							)

			def   _snake_case    (  self  : Dict							):






									'''simple docstring'''



									__lowerCamelCase     :      List[Any]       						=						self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase     :      Tuple       						=						3
									__lowerCamelCase     :      int       						=						input_dict["""input_ids"""]
									__lowerCamelCase     :      List[str]       						=						input_ids.ne(1							).to(_lowerCamelCase							)
									__lowerCamelCase     :      Any       						=						ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size							)
									__lowerCamelCase     :      Tuple       						=						OpenLlamaForSequenceClassification(_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      Optional[int]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  labels=_lowerCamelCase							)
									self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)							)

			def   _snake_case    (  self  : Any							):






									'''simple docstring'''



									__lowerCamelCase     :      int       						=						self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase     :      Optional[int]       						=						3
									__lowerCamelCase     :      Optional[Any]       						=						"""single_label_classification"""
									__lowerCamelCase     :      Any       						=						input_dict["""input_ids"""]
									__lowerCamelCase     :      Tuple       						=						input_ids.ne(1							).to(_lowerCamelCase							)
									__lowerCamelCase     :      int       						=						ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size							)
									__lowerCamelCase     :      List[str]       						=						OpenLlamaForSequenceClassification(_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      str       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  labels=_lowerCamelCase							)
									self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)							)

			def   _snake_case    (  self  : Any							):






									'''simple docstring'''



									__lowerCamelCase     :      Tuple       						=						self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase     :      Any       						=						3
									__lowerCamelCase     :      List[str]       						=						"""multi_label_classification"""
									__lowerCamelCase     :      Union[str, Any]       						=						input_dict["""input_ids"""]
									__lowerCamelCase     :      Optional[Any]       						=						input_ids.ne(1							).to(_lowerCamelCase							)
									__lowerCamelCase     :      Tuple       						=						ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size							).to(torch.float							)
									__lowerCamelCase     :      int       						=						OpenLlamaForSequenceClassification(_lowerCamelCase							)
									model.to(_lowerCamelCase							)
									model.eval()
									__lowerCamelCase     :      List[str]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  labels=_lowerCamelCase							)
									self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels)							)

			@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""							)
			def   _snake_case    (  self  : Dict							):






									'''simple docstring'''



									pass


			@parameterized.expand([("""linear""",), ("""dynamic""",)]							)
			def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase     :      List[Any]       						=						self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase     :      Optional[Any]       						=						ids_tensor([1, 1_0]       ,  config.vocab_size							)
									__lowerCamelCase     :      Dict       						=						ids_tensor([1, int(config.max_position_embeddings * 1.5							)]       ,  config.vocab_size							)

									set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
									__lowerCamelCase     :      str       						=						OpenLlamaModel(_lowerCamelCase							)
									original_model.to(_lowerCamelCase							)
									original_model.eval()
									__lowerCamelCase     :      Tuple       						=						original_model(_lowerCamelCase							).last_hidden_state
									__lowerCamelCase     :      Union[str, Any]       						=						original_model(_lowerCamelCase							).last_hidden_state

									set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
									__lowerCamelCase     :      Dict       						=						{"""type""": scaling_type, """factor""": 10.0}
									__lowerCamelCase     :      int       						=						OpenLlamaModel(_lowerCamelCase							)
									scaled_model.to(_lowerCamelCase							)
									scaled_model.eval()
									__lowerCamelCase     :      List[Any]       						=						scaled_model(_lowerCamelCase							).last_hidden_state
									__lowerCamelCase     :      Union[str, Any]       						=						scaled_model(_lowerCamelCase							).last_hidden_state

									# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
									# maximum sequence length, so the outputs for the short input should match.
									if scaling_type == "dynamic":
															self.assertTrue(torch.allclose(_lowerCamelCase       ,  _lowerCamelCase       ,  atol=1E-5							)							)
									else:
															self.assertFalse(torch.allclose(_lowerCamelCase       ,  _lowerCamelCase       ,  atol=1E-5							)							)

									# The output should be different for long inputs
									self.assertFalse(torch.allclose(_lowerCamelCase       ,  _lowerCamelCase       ,  atol=1E-5							)							)







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    :						Optional[Any]     =							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    :						Dict     =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Any       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      List[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      Optional[int]       						=						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     :      List[Any]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Tuple       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[int]       						=						value
      else:
            __lowerCamelCase     :      Dict       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						[]
      __lowerCamelCase     :      Union[str, Any]       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      Optional[Any]       						=						hf_model.feature_extractor
      __lowerCamelCase     :      List[str]       						=						hf_model.adapter

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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						full_name.split("""adaptor."""      )[-1]
      __lowerCamelCase     :      Any       						=						name.split("""."""      )

      if items[1].isdigit():
            __lowerCamelCase     :      Dict       						=						int(items[1]      )
      else:
            __lowerCamelCase     :      List[str]       						=						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     :      str       						=						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     :      str       						=						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     :      Optional[int]       						=						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     :      List[str]       						=						value
                        logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""      )
      elif isinstance(UpperCAmelCase						,	UpperCAmelCase      ):
            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     :      int       						=						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     :      Tuple       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						emb.weight.shape
      __lowerCamelCase     :      Any       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						emb.weight.data
      return lin_layer





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Optional[Any]						,	):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						WavaVecaConfig.from_pretrained(
          UpperCAmelCase						,	add_adapter=UpperCAmelCase						,	adapter_stride=UpperCAmelCase						,	adapter_kernel_size=UpperCAmelCase						,	use_auth_token=UpperCAmelCase						,	output_hidden_size=UpperCAmelCase						,	)
      __lowerCamelCase     :      Optional[int]       						=						MBartConfig.from_pretrained(UpperCAmelCase      )

      # load model
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						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     :      Union[str, Any]       						=						model[0].eval()

      # load feature extractor
      __lowerCamelCase     :      Any       						=						WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase						,	use_auth_token=UpperCAmelCase      )

      # set weights for wav2vec2 encoder
      __lowerCamelCase     :      Tuple       						=						WavaVecaModel(UpperCAmelCase      )

      recursively_load_weights_wavaveca(model.encoder						,	UpperCAmelCase      )

      # load decoder weights
      __lowerCamelCase     :      Dict       						=						MBartForCausalLM(UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      int       						=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,	strict=UpperCAmelCase      )
      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     :      List[Any]       						=						SpeechEncoderDecoderModel(encoder=UpperCAmelCase						,	decoder=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						False

      __lowerCamelCase     :      List[str]       						=						MBartaaTokenizer(UpperCAmelCase      )
      tokenizer.save_pretrained(UpperCAmelCase      )

      __lowerCamelCase     :      int       						=						hf_wavavec.config.to_dict()
      __lowerCamelCase     :      str       						=						tokenizer.pad_token_id
      __lowerCamelCase     :      Optional[Any]       						=						tokenizer.bos_token_id
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Tuple       						=						"""mbart50"""
      __lowerCamelCase     :      List[str]       						=						"""wav2vec2"""

      __lowerCamelCase     :      List[str]       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Optional[int]       						=						250_004
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id

      __lowerCamelCase     :      Optional[Any]       						=						SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase      )

      hf_wavavec.save_pretrained(UpperCAmelCase      )
      feature_extractor.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Dict     =							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=1024, type=int, help='encoder output dim')
    parser.add_argument('--start_token_id', default=250004, 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,
    )



