




'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image


class   __UpperCamelCase       :






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase=3,		lowerCAmelCase=32,		lowerCAmelCase=3,		lowerCAmelCase=10,		lowerCAmelCase=[8, 16, 32, 64],		lowerCAmelCase=[1, 1, 2, 1],		lowerCAmelCase=True,		lowerCAmelCase=True,		lowerCAmelCase="relu",		lowerCAmelCase=3,		lowerCAmelCase=None,		lowerCAmelCase=["stage2", "stage3", "stage4"],		lowerCAmelCase=[2, 3, 4],		lowerCAmelCase=1,		):



              """simple docstring"""







              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=image_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=embeddings_size
              lowerCamelCase_							=hidden_sizes
              lowerCamelCase_							=depths
              lowerCamelCase_							=is_training
              lowerCamelCase_							=use_labels
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=num_labels
              lowerCamelCase_							=scope
              lowerCamelCase_							=len(lowerCAmelCase  )
              lowerCamelCase_							=out_features
              lowerCamelCase_							=out_indices
              lowerCamelCase_							=num_groups






       def      lowercase__     (							self  ):



              """simple docstring"""







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

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

              lowerCamelCase_							=self.get_config()

              return config, pixel_values, labels






       def      lowercase__     (							self  ):



              """simple docstring"""







              return BitConfig(
                  num_channels=self.num_channels,		embeddings_size=self.embeddings_size,		hidden_sizes=self.hidden_sizes,		depths=self.depths,		hidden_act=self.hidden_act,		num_labels=self.num_labels,		out_features=self.out_features,		out_indices=self.out_indices,		num_groups=self.num_groups,		)






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=BitModel(config=lowerCAmelCase  )
              model.to(lowerCAmelCase  )
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase  )
              self.parent.assertEqual(
                  result.last_hidden_state.shape,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),		)






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=self.num_labels
              lowerCamelCase_							=BitForImageClassification(lowerCAmelCase  )
              model.to(lowerCAmelCase  )
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase,		labels=lowerCAmelCase  )
              self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.num_labels)  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=BitBackbone(config=lowerCAmelCase  )
              model.to(lowerCAmelCase  )
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase  )

              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps  ),		len(config.out_features  )  )
              self.parent.assertListEqual(list(result.feature_maps[0].shape  ),		[self.batch_size, self.hidden_sizes[1], 4, 4]  )

              # verify channels
              self.parent.assertEqual(len(model.channels  ),		len(config.out_features  )  )
              self.parent.assertListEqual(model.channels,		config.hidden_sizes[1:]  )

              # verify backbone works with out_features=None
              lowerCamelCase_							=None
              lowerCamelCase_							=BitBackbone(config=lowerCAmelCase  )
              model.to(lowerCAmelCase  )
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase  )

              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps  ),		1  )
              self.parent.assertListEqual(list(result.feature_maps[0].shape  ),		[self.batch_size, self.hidden_sizes[-1], 1, 1]  )

              # verify channels
              self.parent.assertEqual(len(model.channels  ),		1  )
              self.parent.assertListEqual(model.channels,		[config.hidden_sizes[-1]]  )






       def      lowercase__     (							self  ):



              """simple docstring"""







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





@require_torch
class   __UpperCamelCase       ( lowerCamelCase__			,					lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    List[str]      						=(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
       lowercase	:    Tuple      						=(
           {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase	:    Dict      						=False
       lowercase	:    Any      						=False
       lowercase	:    Any      						=False
       lowercase	:    List[Any]      						=False
       lowercase	:    Dict      						=False






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=BitModelTester(self  )
              lowerCamelCase_							=ConfigTester(self,		config_class=lowerCAmelCase,		has_text_modality=lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              self.create_and_test_config_common_properties()
              self.config_tester.create_and_test_config_to_json_string()
              self.config_tester.create_and_test_config_to_json_file()
              self.config_tester.create_and_test_config_from_and_save_pretrained()
              self.config_tester.create_and_test_config_with_num_labels()
              self.config_tester.check_config_can_be_init_without_params()
              self.config_tester.check_config_arguments_init()






       def      lowercase__     (							self  ):



              """simple docstring"""







              return






       @unittest.skip(reason='''Bit does not output attentions'''  )
       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       @unittest.skip(reason='''Bit does not use inputs_embeds'''  )
       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       @unittest.skip(reason='''Bit does not support input and output embeddings'''  )
       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

                     lowerCamelCase_							=['''pixel_values''']
                     self.assertListEqual(arg_names[:1],		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=model_class(config=lowerCAmelCase  )
                     for name, module in model.named_modules():
                            if isinstance(lowerCAmelCase,		(nn.BatchNormad, nn.GroupNorm)  ):
                                   self.assertTrue(
                                       torch.all(module.weight == 1  ),		msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',		)
                                   self.assertTrue(
                                       torch.all(module.bias == 0  ),		msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',		)






       def      lowercase__     (							self  ):



              """simple docstring"""







              def check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):
                     lowerCamelCase_							=model_class(lowerCAmelCase  )
                     model.to(lowerCAmelCase  )
                     model.eval()

                     with torch.no_grad():
                            lowerCamelCase_							=model(**self._prepare_for_class(lowerCAmelCase,		lowerCAmelCase  )  )

                     lowerCamelCase_							=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     lowerCamelCase_							=self.model_tester.num_stages
                     self.assertEqual(len(lowerCAmelCase  ),		expected_num_stages + 1  )

                     # Bit's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]  ),		[self.model_tester.image_size // 4, self.model_tester.image_size // 4],		)

              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()
              lowerCamelCase_							=['''preactivation''', '''bottleneck''']
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            lowerCamelCase_							=layer_type
                            lowerCamelCase_							=True
                            check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )

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

                            check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )






       @unittest.skip(reason='''Bit does not use feedforward chunking'''  )
       def      lowercase__     (							self  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase  )






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCamelCase_							=BitModel.from_pretrained(lowerCAmelCase  )
                     self.assertIsNotNone(lowerCAmelCase  )





def      a_							(      )							->						List[Any]:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       return image


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






       @cached_property
       def      lowercase__     (							self  ):



              """simple docstring"""







              return (
                  BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ) if is_vision_available() else None
              )






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(lowerCAmelCase  )

              lowerCamelCase_							=self.default_image_processor
              lowerCamelCase_							=prepare_img()
              lowerCamelCase_							=image_processor(images=lowerCAmelCase,		return_tensors='''pt'''  ).to(lowerCAmelCase  )

              # forward pass
              with torch.no_grad():
                     lowerCamelCase_							=model(**lowerCAmelCase  )

              # verify the logits
              lowerCamelCase_							=torch.Size((1, 1_000)  )
              self.assertEqual(outputs.logits.shape,		lowerCAmelCase  )

              lowerCamelCase_							=torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]  ).to(lowerCAmelCase  )

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





@require_torch
class   __UpperCamelCase       ( lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    Tuple      						=(BitBackbone,) if is_torch_available() else ()
       lowercase	:    Optional[int]      						=BitConfig

       lowercase	:    Any      						=False






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=BitModelTester(self  )










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        =    logging.get_logger(__name__)

__A        =    {
    "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        =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[int]      )     ->       Tuple:





				"""simple docstring"""


				for attribute in key.split('.'      ):
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )

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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Tuple      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    fairseq_model.state_dict()

				__lowerCamelCase		       =    hf_model.feature_extractor
				__lowerCamelCase		       =    hf_model.adapter

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





				"""simple docstring"""


				__lowerCamelCase		       =    full_name.split('conv_layers.'      )[-1]
				__lowerCamelCase		       =    name.split('.'      )
				__lowerCamelCase		       =    int(items[0]      )
				__lowerCamelCase		       =    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		       =    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		       =    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		       =    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		       =    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__				:      Tuple ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int      )     ->       Union[str, Any]:





				"""simple docstring"""


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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple      )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    emb.weight.shape
				__lowerCamelCase		       =    nn.Linear(UpperCamelCase__ ,     UpperCamelCase__ ,     bias=UpperCamelCase__      )
				__lowerCamelCase		       =    emb.weight.data
				return lin_layer







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[Any] ,     )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    WavaVecaConfig.from_pretrained(
				    UpperCamelCase__ ,     add_adapter=UpperCamelCase__ ,     adapter_stride=UpperCamelCase__ ,     adapter_kernel_size=UpperCamelCase__ ,     use_auth_token=UpperCamelCase__ ,     output_hidden_size=UpperCamelCase__ ,     )
				__lowerCamelCase		       =    MBartConfig.from_pretrained(UpperCamelCase__      )

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

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

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

				recursively_load_weights_wavaveca(model.encoder ,     UpperCamelCase__      )

				# load decoder weights
				__lowerCamelCase		       =    MBartForCausalLM(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    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		       =    SpeechEncoderDecoderModel(encoder=UpperCamelCase__ ,     decoder=UpperCamelCase__      )
				__lowerCamelCase		       =    False

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

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

				__lowerCamelCase		       =    tokenizer.eos_token_id
				__lowerCamelCase		       =    25_0004
				__lowerCamelCase		       =    tokenizer.eos_token_id

				__lowerCamelCase		       =    SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__      )

				hf_wavavec.save_pretrained(UpperCamelCase__      )
				feature_extractor.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    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=10_24, type=int, help="encoder output dim")
					parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

					__A        =    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,
					)


