




import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
     from transformers.models.mobilevitva.modeling_mobilevitva import (
         MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
         make_divisible,
     )


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         lowerCAmelCase__          =			self.config_class(**self.inputs_dict       )
         self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "width_multiplier"       )       )






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any=13       ,     SCREAMING_SNAKE_CASE__					:					Any=64       ,     SCREAMING_SNAKE_CASE__					:					int=2       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=3       ,     SCREAMING_SNAKE_CASE__					:					Any="swish"       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=3       ,     SCREAMING_SNAKE_CASE__					:					str=32       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=0.02       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=10       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					int=0.25       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.0       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.0       ,     )				->	Optional[int]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			image_size
         lowerCAmelCase__          =			patch_size
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			make_divisible(512 * width_multiplier       ,     divisor=8       )
         lowerCAmelCase__          =			hidden_act
         lowerCAmelCase__          =			conv_kernel_size
         lowerCAmelCase__          =			output_stride
         lowerCAmelCase__          =			classifier_dropout_prob
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			num_labels
         lowerCAmelCase__          =			initializer_range
         lowerCAmelCase__          =			scope
         lowerCAmelCase__          =			width_multiplier
         lowerCAmelCase__          =			ffn_dropout
         lowerCAmelCase__          =			attn_dropout


    def        a				(	self					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

         lowerCAmelCase__          =			self.get_config()

         return config, pixel_values, labels, pixel_labels


    def        a				(	self					:					Optional[int]       )				->	Optional[Any]:
         return MobileViTVaConfig(
             image_size=self.image_size       ,     patch_size=self.patch_size       ,     num_channels=self.num_channels       ,     hidden_act=self.hidden_act       ,     conv_kernel_size=self.conv_kernel_size       ,     output_stride=self.output_stride       ,     classifier_dropout_prob=self.classifier_dropout_prob       ,     initializer_range=self.initializer_range       ,     width_multiplier=self.width_multiplier       ,     ffn_dropout=self.ffn_dropout_prob       ,     attn_dropout=self.attn_dropout_prob       ,     )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	int:
         lowerCAmelCase__          =			MobileViTVaModel(config=SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_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        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	str:
         lowerCAmelCase__          =			self.num_labels
         lowerCAmelCase__          =			MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     labels=SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	str:
         lowerCAmelCase__          =			self.num_labels
         lowerCAmelCase__          =			MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__       )
         model.to(SCREAMING_SNAKE_CASE__       )
         model.eval()
         lowerCAmelCase__          =			model(SCREAMING_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,
             )       ,     )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     labels=SCREAMING_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        a				(	self					:					str       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			config_and_inputs
         lowerCAmelCase__          =			{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(
        (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
        if is_torch_available()
        else ()
    )

    snake_case__ =				(
        {
            "feature-extraction": MobileViTVaModel,
            "image-classification": MobileViTVaForImageClassification,
            "image-segmentation": MobileViTVaForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					str       )				->	str:
         lowerCAmelCase__          =			MobileViTVaModelTester(self       )
         lowerCAmelCase__          =			MobileViTVaConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     has_text_modality=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       )				->	Any:
         self.config_tester.run_common_tests()


    @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"       )
    def        a				(	self					:					Optional[Any]       )				->	str:
         pass


    @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"       )
    def        a				(	self					:					List[Any]       )				->	List[str]:
         pass


    @unittest.skip(reason="MobileViTV2 does not output attentions"       )
    def        a				(	self					:					str       )				->	Optional[Any]:
         pass


    @require_torch_multi_gpu
    @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."       )
    def        a				(	self					:					Any       )				->	str:
         pass


    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
    def        a				(	self					:					int       )				->	Union[str, Any]:
         pass


    def        a				(	self					:					Dict       )				->	Union[str, Any]:
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              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]       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Tuple       )				->	Optional[Any]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Dict:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ):
              lowerCAmelCase__          =			model_class(SCREAMING_SNAKE_CASE__       )
              model.to(SCREAMING_SNAKE_CASE__       )
              model.eval()

              with torch.no_grad():
                   lowerCAmelCase__          =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )       )

              lowerCAmelCase__          =			outputs.hidden_states

              lowerCAmelCase__          =			5
              self.assertEqual(len(SCREAMING_SNAKE_CASE__       )       ,     SCREAMING_SNAKE_CASE__       )

              # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              lowerCAmelCase__          =			2
              for i in range(len(SCREAMING_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       )

         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__          =			True
              check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

              check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       )				->	Any:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					int       )				->	Any:
         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			MobileViTVaModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )






def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






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




    """simple docstring"""

    @cached_property
    def        a				(	self					:					Optional[int]       )				->	Tuple:
         return (
             MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"       )
             if is_vision_available()
             else None
         )


    @slow
    def        a				(	self					:					Any       )				->	Dict:
         lowerCAmelCase__          =			MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"       ).to(
             SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			self.default_image_processor
         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1]       ).to(SCREAMING_SNAKE_CASE__       )

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


    @slow
    def        a				(	self					:					List[Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"       )
         lowerCAmelCase__          =			model.to(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"       )

         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			outputs.logits

         # verify the logits
         lowerCAmelCase__          =			torch.Size((1, 21, 32, 32)       )
         self.assertEqual(logits.shape       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.tensor(
             [
                 [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                 [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                 [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
             ]       ,     device=SCREAMING_SNAKE_CASE__       ,     )

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


    @slow
    def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"       )
         lowerCAmelCase__          =			model.to(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"       )

         lowerCAmelCase__          =			prepare_img()
         lowerCAmelCase__          =			image_processor(images=SCREAMING_SNAKE_CASE__       ,     return_tensors="pt"       ).to(SCREAMING_SNAKE_CASE__       )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__          =			model(**SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__       ,     target_sizes=[(50, 60)]       )
         lowerCAmelCase__          =			torch.Size((50, 60)       )
         self.assertEqual(segmentation[0].shape       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			torch.Size((32, 32)       )
         self.assertEqual(segmentation[0].shape       ,     SCREAMING_SNAKE_CASE__       )






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCamelCase								=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			{}
     with open(lowerCAmelCase_       ,    "r"      ) as file:
          for line_number, line in enumerate(lowerCAmelCase_      ):
               lowerCAmelCase__          =			line.strip()
               if line:
                    lowerCAmelCase__          =			line.split()
                    lowerCAmelCase__          =			line_number
                    lowerCAmelCase__          =			words[0]
                    lowerCAmelCase__          =			value
     return result



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     for attribute in key.split("."      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      ).shape
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			hf_pointer
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			shape_pointer.shape

          # let's reduce dimension
          lowerCAmelCase__          =			value[0]
     else:
          lowerCAmelCase__          =			hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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
     elif weight_type == "param":
          for attribute in hf_param_name.split("."      ):
               lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			value
     else:
          lowerCAmelCase__          =			value

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



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			PARAM_MAPPING[full_name.split("."      )[-1]]
               lowerCAmelCase__          =			"param"

     if weight_type is not None and weight_type != "param":
          lowerCAmelCase__          =			".".join([key, weight_type]      )
     elif weight_type is not None and weight_type == "param":
          lowerCAmelCase__          =			".".join([key, hf_param_name]      )
     else:
          lowerCAmelCase__          =			key

     lowerCAmelCase__          =			value if "lm_head" in full_key else value[0]


UpperCamelCase								=					{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     lowerCAmelCase__          =			False
     for key, mapped_key in MAPPING.items():
          lowerCAmelCase__          =			"wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
               lowerCAmelCase__          =			True
               if "*" in mapped_key:
                    lowerCAmelCase__          =			name.split(lowerCAmelCase_      )[0].split("."      )[-2]
                    lowerCAmelCase__          =			mapped_key.replace("*"       ,    lowerCAmelCase_      )
               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:
                    # TODO: don't match quantizer.weight_proj
                    lowerCAmelCase__          =			"weight"
               else:
                    lowerCAmelCase__          =			None
               if hf_dict is not None:
                    rename_dict(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               else:
                    set_recursively(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
               return is_used
     return is_used



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			fairseq_model.state_dict()

     lowerCAmelCase__          =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          lowerCAmelCase__          =			False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    hf_model.config.feat_extract_norm == "group"       ,    )
               lowerCAmelCase__          =			True
          else:
               lowerCAmelCase__          =			load_wavaveca_layer(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
          if not is_used:
               unused_weights.append(lowerCAmelCase_      )

     logger.warning(F'Unused weights: {unused_weights}'      )



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Optional[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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[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(lowerCAmelCase_      )



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Optional[int]=True       ,    lowerCAmelCase_					: List[str]=False      ):
     """simple docstring"""

     if config_path is not None:
          lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaConfig()

     if is_seq_class:
          lowerCAmelCase__          =			read_txt_into_dict(lowerCAmelCase_      )
          lowerCAmelCase__          =			idalabel
          lowerCAmelCase__          =			WavaVecaForSequenceClassification(lowerCAmelCase_      )
          lowerCAmelCase__          =			WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase_      )

     elif is_finetuned:
          if dict_path:
               lowerCAmelCase__          =			Dictionary.load(lowerCAmelCase_      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowerCAmelCase__          =			target_dict.pad_index
               lowerCAmelCase__          =			target_dict.bos_index
               lowerCAmelCase__          =			target_dict.eos_index
               lowerCAmelCase__          =			len(target_dict.symbols      )
               lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    "vocab.json"      )
               if not os.path.isdir(lowerCAmelCase_      ):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase_      )      )
                    return
               os.makedirs(lowerCAmelCase_       ,    exist_ok=lowerCAmelCase_      )
               lowerCAmelCase__          =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               lowerCAmelCase__          =			0
               lowerCAmelCase__          =			1
               with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"      ) as vocab_handle:
                    json.dump(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			WavaVecaCTCTokenizer(
                   lowerCAmelCase_       ,    unk_token=target_dict.unk_word       ,    pad_token=target_dict.pad_word       ,    bos_token=target_dict.bos_word       ,    eos_token=target_dict.eos_word       ,    word_delimiter_token="|"       ,    do_lower_case=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			True if config.feat_extract_norm == "layer" else False
               lowerCAmelCase__          =			WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    )
               lowerCAmelCase__          =			WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,    tokenizer=lowerCAmelCase_      )
               processor.save_pretrained(lowerCAmelCase_      )

          lowerCAmelCase__          =			WavaVecaForCTC(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			WavaVecaForPreTraining(lowerCAmelCase_      )

     if is_finetuned or is_seq_class:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]       ,    arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     else:
          lowerCAmelCase__          =			argparse.Namespace(task="audio_pretraining"      )
          lowerCAmelCase__          =			fairseq.tasks.setup_task(lowerCAmelCase_      )

          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=lowerCAmelCase_      )

     lowerCAmelCase__          =			model[0].eval()

     recursively_load_weights(lowerCAmelCase_       ,    lowerCAmelCase_       ,    not is_finetuned      )

     hf_wavavec.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     parser.add_argument(
         '--is_seq_class',
         action='store_true',
         help='Whether the model to convert is a fine-tuned sequence classification model or not',
     )
     UpperCamelCase								=					parser.parse_args()

     UpperCamelCase								=					not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )
