


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLModel,
				)



class 							_UpperCamelCase :

			'''simple docstring'''







			def __init__(  self  : int       ,  _lowerCamelCase  : Tuple       ,  ):






									'''simple docstring'''



									__lowerCamelCase     :      List[str]       						=						parent
									__lowerCamelCase     :      Union[str, Any]       						=						1_3
									__lowerCamelCase     :      Union[str, Any]       						=						7
									__lowerCamelCase     :      Any       						=						3_0
									__lowerCamelCase     :      Optional[int]       						=						self.seq_length + self.mem_len
									__lowerCamelCase     :      Dict       						=						1_5
									__lowerCamelCase     :      Union[str, Any]       						=						True
									__lowerCamelCase     :      List[str]       						=						True
									__lowerCamelCase     :      Union[str, Any]       						=						9_9
									__lowerCamelCase     :      Optional[Any]       						=						[1_0, 5_0, 8_0]
									__lowerCamelCase     :      Union[str, Any]       						=						3_2
									__lowerCamelCase     :      Any       						=						3_2
									__lowerCamelCase     :      int       						=						4
									__lowerCamelCase     :      Dict       						=						8
									__lowerCamelCase     :      Any       						=						1_2_8
									__lowerCamelCase     :      Dict       						=						2
									__lowerCamelCase     :      str       						=						2
									__lowerCamelCase     :      str       						=						None
									__lowerCamelCase     :      Tuple       						=						1
									__lowerCamelCase     :      Optional[int]       						=						0
									__lowerCamelCase     :      int       						=						3
									__lowerCamelCase     :      Any       						=						self.vocab_size - 1
									__lowerCamelCase     :      Dict       						=						0.01

			def   _snake_case    (  self  : Optional[int]							):






									'''simple docstring'''



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

									__lowerCamelCase     :      Any       						=						None
									if self.use_labels:
															__lowerCamelCase     :      int       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

									__lowerCamelCase     :      Dict       						=						TransfoXLConfig(
									    vocab_size=self.vocab_size       ,  mem_len=self.mem_len       ,  clamp_len=self.clamp_len       ,  cutoffs=self.cutoffs       ,  d_model=self.hidden_size       ,  d_embed=self.d_embed       ,  n_head=self.num_attention_heads       ,  d_head=self.d_head       ,  d_inner=self.d_inner       ,  div_val=self.div_val       ,  n_layer=self.num_hidden_layers       ,  eos_token_id=self.eos_token_id       ,  pad_token_id=self.vocab_size - 1       ,  init_range=self.init_range       ,  num_labels=self.num_labels       ,  )

									return (config, input_ids_a, input_ids_a, lm_labels)

			def   _snake_case    (  self  : List[Any]							):






									'''simple docstring'''



									random.seed(self.seed							)
									tf.random.set_seed(self.seed							)

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






									'''simple docstring'''



									__lowerCamelCase     :      Any       						=						TFTransfoXLModel(UpperCAmelCase__							)

									__lowerCamelCase     :      Optional[Any]       						=						model(UpperCAmelCase__							).to_tuple()

									__lowerCamelCase     :      Dict       						=						{'''input_ids''': input_ids_a, '''mems''': mems_a}

									__lowerCamelCase     :      Optional[Any]       						=						model(UpperCAmelCase__							).to_tuple()

									self.parent.assertEqual(hidden_states_a.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)
									self.parent.assertEqual(hidden_states_a.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)							)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )

			def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase     :      List[Any]       						=						TFTransfoXLLMHeadModel(UpperCAmelCase__							)

									__lowerCamelCase     :      Union[str, Any]       						=						model(UpperCAmelCase__							).to_tuple()

									__lowerCamelCase     :      List[str]       						=						{'''input_ids''': input_ids_a, '''labels''': lm_labels}
									__lowerCamelCase     :      Optional[int]       						=						model(UpperCAmelCase__							).to_tuple()

									__lowerCamelCase     :      Dict       						=						model([input_ids_a, mems_a]							).to_tuple()

									__lowerCamelCase     :      Optional[int]       						=						{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

									__lowerCamelCase     :      Optional[Any]       						=						model(UpperCAmelCase__							).to_tuple()

									self.parent.assertEqual(lm_logits_a.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )

									self.parent.assertEqual(lm_logits_a.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)
									self.parent.assertListEqual(
									    [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )

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






									'''simple docstring'''



									__lowerCamelCase     :      List[Any]       						=						TFTransfoXLForSequenceClassification(UpperCAmelCase__							)
									__lowerCamelCase     :      Union[str, Any]       						=						model(UpperCAmelCase__							)
									self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)							)


			def   _snake_case    (  self  : str							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[Any]       						=						self.prepare_config_and_inputs()
									(__lowerCamelCase)     :      int       						=						config_and_inputs
									__lowerCamelCase     :      Any       						=						{'''input_ids''': input_ids_a}
									return config, inputs_dict



@require_tf
class 							_UpperCamelCase (						lowercase__,lowercase__,unittest.TestCase						):

			'''simple docstring'''







			a_				:							Optional[int]    = (
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			a_				:							str    = () if is_tf_available() else ()
			a_				:							Dict    = (
			    {
			        "feature-extraction": TFTransfoXLModel,
			        "text-classification": TFTransfoXLForSequenceClassification,
			        "text-generation": TFTransfoXLLMHeadModel,
			        "zero-shot": TFTransfoXLForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
			a_				:							List[Any]    = False
			a_				:							str    = False
			a_				:							Any    = False
			a_				:							Optional[int]    = False

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






									'''simple docstring'''



									if pipeline_test_casse_name == "TextGenerationPipelineTests":
															# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
															# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
															# tokenizer.
															return True

									return False

			def   _snake_case    (  self  : List[Any]							):






									'''simple docstring'''



									__lowerCamelCase     :      Any       						=						TFTransfoXLModelTester(self							)
									__lowerCamelCase     :      Optional[Any]       						=						ConfigTester(self       ,  config_class=UpperCAmelCase__       ,  d_embed=3_7							)

			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									self.config_tester.run_common_tests()

			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									self.model_tester.set_seed()
									__lowerCamelCase     :      Union[str, Any]       						=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__							)

			def   _snake_case    (  self  : Optional[Any]							):






									'''simple docstring'''



									self.model_tester.set_seed()
									__lowerCamelCase     :      Any       						=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__							)

			def   _snake_case    (  self  : Any							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[Any]       						=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__							)

			def   _snake_case    (  self  : Optional[int]							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase     :      List[Any]       						=						[TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
															__lowerCamelCase     :      Optional[int]       						=						model_class(UpperCAmelCase__							)
															assert isinstance(model.get_input_embeddings()       ,  tf.keras.layers.Layer							)
															if model_class in list_other_models_with_output_ebd:
																					__lowerCamelCase     :      str       						=						model.get_output_embeddings()
																					assert isinstance(UpperCAmelCase__       ,  tf.keras.layers.Layer							)
																					__lowerCamelCase     :      Tuple       						=						model.get_bias()
																					assert name is None
															else:
																					__lowerCamelCase     :      List[Any]       						=						model.get_output_embeddings()
																					assert x is None
																					__lowerCamelCase     :      int       						=						model.get_bias()
																					assert name is None

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






									'''simple docstring'''



									pass

			@slow
			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowerCamelCase     :      int       						=						TFTransfoXLModel.from_pretrained(UpperCAmelCase__							)
															self.assertIsNotNone(UpperCAmelCase__							)


			@unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss."""							)
			def   _snake_case    (  self  : List[Any]							):






									'''simple docstring'''



									pass



@require_tf
class 							_UpperCamelCase (						unittest.TestCase						):

			'''simple docstring'''







			@unittest.skip("""Skip test until #12651 is resolved."""							)
			@slow
			def   _snake_case    (  self  : int							):






									'''simple docstring'''



									__lowerCamelCase     :      Optional[int]       						=						TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""							)
									# fmt: off
									__lowerCamelCase     :      Optional[int]       						=						tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]       ,  dtype=tf.intaa							)  # noqa: E231
									# fmt: on
									#  In 1991 , the remains of Russian Tsar Nicholas II and his family
									#  ( except for Alexei and Maria ) are discovered .
									#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
									#  remainder of the story . 1883 Western Siberia ,
									#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
									#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
									#  father initially slaps him for making such an accusation , Rasputin watches as the
									#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
									#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
									#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

									# fmt: off
									__lowerCamelCase     :      Tuple       						=						[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
									# fmt: on
									#  In 1991, the remains of Russian Tsar Nicholas II and his family (
									#  except for Alexei and Maria ) are discovered. The voice of young son,
									#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
									#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
									#  and a group of men to perform magic. Rasputin has a vision and
									#  denounces one of the men as a horse thief. Although his father initially
									#  slaps him for making such an accusation, Rasputin watches as the man
									#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
									#  of the Virgin Mary, prompting him to become a priest.
									#  Rasputin quickly becomes famous, with people, even a bishop, begging for
									#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
									# Nicholas II and his family were discovered. The voice of <unk> young son,
									# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

									__lowerCamelCase     :      Dict       						=						model.generate(UpperCAmelCase__       ,  max_length=2_0_0       ,  do_sample=UpperCAmelCase__							)
									self.assertListEqual(output_ids[0].numpy().tolist()       ,  UpperCAmelCase__							)







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,
    )



