

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
      from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor

class    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.config_class(**self.inputs_dict       )
         self.parent.assertTrue(hasattr(snake_case__			,						'''hidden_sizes'''       )       )
         self.parent.assertTrue(hasattr(snake_case__			,						'''neck_hidden_sizes'''       )       )
         self.parent.assertTrue(hasattr(snake_case__			,						'''num_attention_heads'''       )       )



class    _lowercase				:

   def __init__(				self	:    Optional[Any]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Union[str, Any]=1_3			,						lowerCamelCase__	:    List[Any]=3_2			,						lowerCamelCase__	:    Any=2			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    Tuple=6_4_0			,						lowerCamelCase__	:    Dict=4			,						lowerCamelCase__	:    List[str]="silu"			,						lowerCamelCase__	:    Union[str, Any]=3			,						lowerCamelCase__	:    Optional[int]=3_2			,						lowerCamelCase__	:    Optional[Any]=0.1			,						lowerCamelCase__	:    Tuple=0.1			,						lowerCamelCase__	:    Any=0.1			,						lowerCamelCase__	:    Optional[Any]=0.02			,						lowerCamelCase__	:    Optional[int]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Union[str, Any]=1_0			,						lowerCamelCase__	:    List[Any]=None			,						) ->			Tuple:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		image_size
         A_							       =		patch_size
         A_							       =		num_channels
         A_							       =		last_hidden_size
         A_							       =		num_attention_heads
         A_							       =		hidden_act
         A_							       =		conv_kernel_size
         A_							       =		output_stride
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		classifier_dropout_prob
         A_							       =		use_labels
         A_							       =		is_training
         A_							       =		num_labels
         A_							       =		initializer_range
         A_							       =		scope

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         A_							       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         A_							       =		None
         A_							       =		None
         if self.use_labels:
               A_							       =		ids_tensor([self.batch_size]			,						self.num_labels       )
               A_							       =		ids_tensor([self.batch_size, self.image_size, self.image_size]			,						self.num_labels       )

         A_							       =		self.get_config()

         return config, pixel_values, labels, pixel_labels

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         return MobileViTConfig(
             image_size=self.image_size			,						patch_size=self.patch_size			,						num_channels=self.num_channels			,						num_attention_heads=self.num_attention_heads			,						hidden_act=self.hidden_act			,						conv_kernel_size=self.conv_kernel_size			,						output_stride=self.output_stride			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						classifier_dropout_prob=self.classifier_dropout_prob			,						initializer_range=self.initializer_range			,						)

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[int]       ) ->			Dict:
         """simple docstring"""





         A_							       =		MobileViTModel(config=snake_case__       )
         model.to(snake_case__       )
         model.eval()
         A_							       =		model(snake_case__       )
         self.parent.assertEqual(
             result.last_hidden_state.shape			,						(
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			,						)

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Any       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		MobileViTForImageClassification(snake_case__       )
         model.to(snake_case__       )
         model.eval()
         A_							       =		model(snake_case__			,						labels=snake_case__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int       ) ->			int:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		MobileViTForSemanticSegmentation(snake_case__       )
         model.to(snake_case__       )
         model.eval()
         A_							       =		model(snake_case__       )
         self.parent.assertEqual(
             result.logits.shape			,						(
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			,						)
         A_							       =		model(snake_case__			,						labels=snake_case__       )
         self.parent.assertEqual(
             result.logits.shape			,						(
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )			,						)





   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         A_       ,A_       ,A_       ,A_							       =		config_and_inputs
         A_							       =		{'''pixel_values''': pixel_values}
         return config, inputs_dict



@require_torch
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    (
       (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   _lowercase     :       Dict  					=    (
       {
           'feature-extraction': MobileViTModel,
           'image-classification': MobileViTForImageClassification,
           'image-segmentation': MobileViTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _lowercase     :       List[Any]  					=    False
   _lowercase     :       Optional[int]  					=    False
   _lowercase     :       Any  					=    False
   _lowercase     :       Union[str, Any]  					=    False

   def     UpperCamelCase					(				self	:    Dict       ) ->			Dict:
         """simple docstring"""





         A_							       =		MobileViTModelTester(self       )
         A_							       =		MobileViTConfigTester(self			,						config_class=snake_case__			,						has_text_modality=snake_case__       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			Dict:
         """simple docstring"""





         self.config_tester.run_common_tests()

   @unittest.skip(reason='''MobileViT does not use inputs_embeds'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			Union[str, Any]:
         """simple docstring"""





         pass

   @unittest.skip(reason='''MobileViT does not support input and output embeddings'''       )
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         pass

   @unittest.skip(reason='''MobileViT does not output attentions'''       )
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               A_							       =		model_class(snake_case__       )
               A_							       =		inspect.signature(model.forward       )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               A_							       =		[*signature.parameters.keys()]

               A_							       =		['''pixel_values''']
               self.assertListEqual(arg_names[:1]			,						snake_case__       )

   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[Any]:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__       )

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         def check_hidden_states_output(lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Tuple       ):
               A_							       =		model_class(snake_case__       )
               model.to(snake_case__       )
               model.eval()

               with torch.no_grad():
                     A_							       =		model(**self._prepare_for_class(snake_case__			,						snake_case__       )       )

               A_							       =		outputs.hidden_states

               A_							       =		5
               self.assertEqual(len(snake_case__       )			,						snake_case__       )

               # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
               # with the width and height being successively divided by 2.
               A_							       =		2
               for i in range(len(snake_case__       )       ):
                     self.assertListEqual(
                         list(hidden_states[i].shape[-2:]       )			,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,						)
                     divisor *= 2

               self.assertEqual(self.model_tester.output_stride			,						divisor // 2       )

         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               A_							       =		True
               check_hidden_states_output(snake_case__			,						snake_case__			,						snake_case__       )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               A_							       =		True

               check_hidden_states_output(snake_case__			,						snake_case__			,						snake_case__       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__       )

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__       )





   @slow
   def     UpperCamelCase					(				self	:    Dict       ) ->			Optional[int]:
         """simple docstring"""





         for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_							       =		MobileViTModel.from_pretrained(snake_case__       )
               self.assertIsNotNone(snake_case__       )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
      return image





@require_torch
@require_vision
class    _lowercase				(					unittest.TestCase			):

   @cached_property
   def     UpperCamelCase					(				self	:    int       ) ->			Union[str, Any]:
         """simple docstring"""





         return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''       ) if is_vision_available() else None

   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Tuple:
         """simple docstring"""





         A_							       =		MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''       ).to(snake_case__       )

         A_							       =		self.default_image_processor
         A_							       =		prepare_img()
         A_							       =		image_processor(images=snake_case__			,						return_tensors='''pt'''       ).to(snake_case__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(**snake_case__       )

         # verify the logits
         A_							       =		torch.Size((1, 1_0_0_0)       )
         self.assertEqual(outputs.logits.shape			,						snake_case__       )

         A_							       =		torch.tensor([-1.9364, -1.2327, -0.4653]       ).to(snake_case__       )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						snake_case__			,						atol=1e-4       )       )

   @slow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			str:
         """simple docstring"""





         A_							       =		MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )
         A_							       =		model.to(snake_case__       )

         A_							       =		MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )

         A_							       =		prepare_img()
         A_							       =		image_processor(images=snake_case__			,						return_tensors='''pt'''       ).to(snake_case__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(**snake_case__       )
         A_							       =		outputs.logits

         # verify the logits
         A_							       =		torch.Size((1, 2_1, 3_2, 3_2)       )
         self.assertEqual(logits.shape			,						snake_case__       )

         A_							       =		torch.tensor(
             [
                 [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                 [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                 [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
             ]			,						device=snake_case__			,						)

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,						snake_case__			,						atol=1e-4       )       )





   @slow
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )
         A_							       =		model.to(snake_case__       )

         A_							       =		MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''       )

         A_							       =		prepare_img()
         A_							       =		image_processor(images=snake_case__			,						return_tensors='''pt'''       ).to(snake_case__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(**snake_case__       )

         A_							       =		outputs.logits.detach().cpu()

         A_							       =		image_processor.post_process_semantic_segmentation(outputs=snake_case__			,						target_sizes=[(5_0, 6_0)]       )
         A_							       =		torch.Size((5_0, 6_0)       )
         self.assertEqual(segmentation[0].shape			,						snake_case__       )

         A_							       =		image_processor.post_process_semantic_segmentation(outputs=snake_case__       )
         A_							       =		torch.Size((3_2, 3_2)       )
         self.assertEqual(segmentation[0].shape			,						snake_case__       )








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()
a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    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",
}
a_     :    Union[str, Any]     		=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				Optional[int]:
 '''simple docstring'''


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE		    =      hf_model.feature_extractor
 SCREAMING_SNAKE_CASE		    =      hf_model.adapter

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE		    =      False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
   SCREAMING_SNAKE_CASE		    =      True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']						):
   load_adapter(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     SCREAMING_SNAKE_CASE		    =      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
      SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_v'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE		    =      'bias'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE		    =      'weight'
     else:
      SCREAMING_SNAKE_CASE		    =      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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Union[str, Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('adaptor.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(_UpperCamelCase		,				_UpperCamelCase		,				bias=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      WavaVecaConfig.from_pretrained(
     _UpperCamelCase		,				add_adapter=_UpperCamelCase		,				adapter_stride=_UpperCamelCase		,				adapter_kernel_size=_UpperCamelCase		,				use_auth_token=_UpperCamelCase		,				output_hidden_size=_UpperCamelCase		,				)
 SCREAMING_SNAKE_CASE		    =      MBartConfig.from_pretrained(_UpperCamelCase						)

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

 # load feature extractor
 SCREAMING_SNAKE_CASE		    =      WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,				use_auth_token=_UpperCamelCase						)

 # set weights for wav2vec2 encoder
 SCREAMING_SNAKE_CASE		    =      WavaVecaModel(_UpperCamelCase						)

 recursively_load_weights_wavaveca(model.encoder		,				_UpperCamelCase						)

 # load decoder weights
 SCREAMING_SNAKE_CASE		    =      MBartForCausalLM(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      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}"""						)

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      MBartaaTokenizer(_UpperCamelCase						)
 tokenizer.save_pretrained(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      hf_wavavec.config.to_dict()
 SCREAMING_SNAKE_CASE		    =      tokenizer.pad_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.bos_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      'mbart50'
 SCREAMING_SNAKE_CASE		    =      'wav2vec2'

 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      25_00_04
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase						)

 hf_wavavec.save_pretrained(_UpperCamelCase						)
 feature_extractor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[str]     		=   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=25_0004, type=int, help="`decoder_start_token_id` of model config")

      a_     :    Dict     		=   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,
      )

