
import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      _lowerCamelCase           =_lowerCamelCase           =_lowerCamelCase           =_lowerCamelCase           =None





class 							A__	(			unittest.TestCase):
      def __init__(   self					,						__magic_name__					,						__magic_name__=7					,						__magic_name__=3					,						__magic_name__=3_0					,						__magic_name__=4_0_0					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=1 / 2_5_5					,						__magic_name__=True					,						__magic_name__=FLAVA_IMAGE_MEAN					,						__magic_name__=FLAVA_IMAGE_STD					,						__magic_name__=1_4					,						__magic_name__=7_5					,						__magic_name__=None					,						__magic_name__=1_6					,						__magic_name__=0.3					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=True					,						__magic_name__=FLAVA_CODEBOOK_MEAN					,						__magic_name__=FLAVA_CODEBOOK_STD					,						):
       lowerCamelCase	:			Union[str, Any]           =   size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       lowerCamelCase	:			Optional[int]           =   crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       lowerCamelCase	:			str           =   codebook_size if codebook_size is not None else {"""height""": 1_1_2, """width""": 1_1_2}
       lowerCamelCase	:			List[str]           =   codebook_crop_size if codebook_crop_size is not None else {"""height""": 1_1_2, """width""": 1_1_2}

       lowerCamelCase	:			Union[str, Any]           =   parent
       lowerCamelCase	:			Union[str, Any]           =   batch_size
       lowerCamelCase	:			Tuple           =   num_channels
       lowerCamelCase	:			Optional[int]           =   do_resize
       lowerCamelCase	:			str           =   do_rescale
       lowerCamelCase	:			Any           =   rescale_factor
       lowerCamelCase	:			str           =   min_resolution
       lowerCamelCase	:			int           =   max_resolution
       lowerCamelCase	:			Optional[int]           =   size
       lowerCamelCase	:			Optional[int]           =   resample if resample is not None else PILImageResampling.BICUBIC
       lowerCamelCase	:			Optional[Any]           =   do_normalize
       lowerCamelCase	:			Optional[Any]           =   image_mean
       lowerCamelCase	:			Optional[Any]           =   image_std
       lowerCamelCase	:			Optional[Any]           =   do_center_crop
       lowerCamelCase	:			Dict           =   crop_size

       lowerCamelCase	:			List[str]           =   input_size_patches
       lowerCamelCase	:			Any           =   total_mask_patches
       lowerCamelCase	:			int           =   mask_group_max_patches
       lowerCamelCase	:			str           =   mask_group_min_patches
       lowerCamelCase	:			List[str]           =   mask_group_min_aspect_ratio
       lowerCamelCase	:			int           =   mask_group_max_aspect_ratio

       lowerCamelCase	:			Optional[int]           =   codebook_do_resize
       lowerCamelCase	:			Dict           =   codebook_size
       lowerCamelCase	:			Union[str, Any]           =   codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
       lowerCamelCase	:			Dict           =   codebook_do_center_crop
       lowerCamelCase	:			Optional[Any]           =   codebook_crop_size
       lowerCamelCase	:			str           =   codebook_do_map_pixels
       lowerCamelCase	:			int           =   codebook_do_normalize
       lowerCamelCase	:			Optional[Any]           =   codebook_image_mean
       lowerCamelCase	:			Any           =   codebook_image_std
      def 		UpperCamelCase__							(   self   ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "resample": self.resample,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "input_size_patches": self.input_size_patches,
           "total_mask_patches": self.total_mask_patches,
           "mask_group_max_patches": self.mask_group_max_patches,
           "mask_group_min_patches": self.mask_group_min_patches,
           "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
           "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
           "codebook_do_resize": self.codebook_do_resize,
           "codebook_size": self.codebook_size,
           "codebook_resample": self.codebook_resample,
           "codebook_do_center_crop": self.codebook_do_center_crop,
           "codebook_crop_size": self.codebook_crop_size,
           "codebook_do_map_pixels": self.codebook_do_map_pixels,
           "codebook_do_normalize": self.codebook_do_normalize,
           "codebook_image_mean": self.codebook_image_mean,
           "codebook_image_std": self.codebook_image_std,
       }
      def 		UpperCamelCase__							(   self   ):
       return (self.size["height"], self.size["width"])
      def 		UpperCamelCase__							(   self   ):
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches					,						__magic_name__   )
           else self.input_size_patches
       )






      def 		UpperCamelCase__							(   self   ):
       return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class 							A__	(			__SCREAMING_SNAKE_CASE						,     unittest.TestCase):
      _UpperCAmelCase       :					List[str]					 =    FlavaImageProcessor if is_vision_available() else None
      _UpperCAmelCase       :					Dict					 =    None
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Union[str, Any]           =   FlavaImageProcessingTester(self   )
      @property
      def 		UpperCamelCase__							(   self   ):
       return self.image_processor_tester.prepare_image_processor_dict()
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_mean"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""image_std"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_normalize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_resize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""resample"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""crop_size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""do_rescale"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""rescale_factor"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""masking_generator"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_do_resize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_resample"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_do_center_crop"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_crop_size"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_do_map_pixels"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_do_normalize"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_image_mean"""   )   )
       self.assertTrue(hasattr(__magic_name__					,						"""codebook_image_std"""   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Any           =   self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size					,						{"""height""": 2_2_4, """width""": 2_2_4}   )
       self.assertEqual(image_processor.crop_size					,						{"""height""": 2_2_4, """width""": 2_2_4}   )
       self.assertEqual(image_processor.codebook_size					,						{"""height""": 1_1_2, """width""": 1_1_2}   )
       self.assertEqual(image_processor.codebook_crop_size					,						{"""height""": 1_1_2, """width""": 1_1_2}   )

       lowerCamelCase	:			List[str]           =   self.image_processing_class.from_dict(
           self.image_processor_dict					,						size=4_2					,						crop_size=8_4					,						codebook_size=3_3					,						codebook_crop_size=6_6   )
       self.assertEqual(image_processor.size					,						{"""height""": 4_2, """width""": 4_2}   )
       self.assertEqual(image_processor.crop_size					,						{"""height""": 8_4, """width""": 8_4}   )
       self.assertEqual(image_processor.codebook_size					,						{"""height""": 3_3, """width""": 3_3}   )
       self.assertEqual(image_processor.codebook_crop_size					,						{"""height""": 6_6, """width""": 6_6}   )
      def 		UpperCamelCase__							(   self   ):
       pass
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Tuple           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       lowerCamelCase	:			List[Any]           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						PIL.Image.Image   )

       # Test not batched input
       lowerCamelCase	:			int           =   image_processing(image_inputs[0]					,						return_tensors="""pt"""   )

       # Test no bool masked pos
       self.assertFalse("""bool_masked_pos""" in encoded_images   )

       lowerCamelCase    ,					lowerCamelCase	:			Any           =   self.image_processor_tester.get_expected_image_size()

       self.assertEqual(
           encoded_images.pixel_values.shape					,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,						)

       # Test batched
       lowerCamelCase	:			List[str]           =   image_processing(__magic_name__					,						return_tensors="""pt"""   )
       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self.image_processor_tester.get_expected_image_size()

       # Test no bool masked pos
       self.assertFalse("""bool_masked_pos""" in encoded_images   )

       self.assertEqual(
           encoded_images.pixel_values.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,						)
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       # Initialize image_processing
       lowerCamelCase	:			Optional[int]           =   self.image_processing_class(**self.image_processor_dict   )
       # create random tensors
       lowerCamelCase	:			List[str]           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__					,						**__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						__magic_name__   )

       # Test not batched input
       lowerCamelCase	:			Union[str, Any]           =   image_processing(image_inputs[0]					,						return_tensors="""pt"""   )

       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape					,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,						)

       lowerCamelCase	:			Optional[Any]           =   image_processing(__magic_name__					,						return_image_mask=__magic_name__					,						return_tensors="""pt"""   )

       lowerCamelCase    ,					lowerCamelCase	:			Optional[Any]           =   self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,						)

       lowerCamelCase    ,					lowerCamelCase	:			List[Any]           =   self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape					,						(
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )					,						)

       # Test batched
       lowerCamelCase	:			List[Any]           =   image_processing(__magic_name__					,						return_tensors="""pt"""   ).pixel_values

       lowerCamelCase    ,					lowerCamelCase	:			int           =   self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,						)

       # Test masking
       lowerCamelCase	:			Tuple           =   image_processing(__magic_name__					,						return_image_mask=__magic_name__					,						return_tensors="""pt"""   )

       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,						)

       lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape					,						(
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )					,						)
      def 		UpperCamelCase__							(   self   ):
       self._test_call_framework(np.ndarray					,						prepare_kwargs={"""numpify""": True}   )
      def 		UpperCamelCase__							(   self   ):
       self._test_call_framework(torch.Tensor					,						prepare_kwargs={"""torchify""": True}   )
      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       random.seed(1_2_3_4   )
       lowerCamelCase	:			Dict           =   self.image_processing_class(**self.image_processor_dict   )
       lowerCamelCase	:			List[str]           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__					,						torchify=__magic_name__   )

       # Test not batched input
       lowerCamelCase	:			Tuple           =   image_processing(image_inputs[0]					,						return_image_mask=__magic_name__					,						return_tensors="""pt"""   )
       self.assertEqual(encoded_images.bool_masked_pos.sum().item()					,						7_5   )






      def 		UpperCamelCase__							(   self   ):
       # Initialize image_processing
       lowerCamelCase	:			Tuple           =   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       lowerCamelCase	:			Any           =   prepare_image_inputs(self.image_processor_tester					,						equal_resolution=__magic_name__   )
       for image in image_inputs:
        self.assertIsInstance(__magic_name__					,						PIL.Image.Image   )

       # Test not batched input
       lowerCamelCase	:			Optional[int]           =   image_processing(image_inputs[0]					,						return_codebook_pixels=__magic_name__					,						return_tensors="""pt"""   )
       lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape					,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)					,						)

       # Test batched
       lowerCamelCase	:			Optional[int]           =   image_processing(__magic_name__					,						return_codebook_pixels=__magic_name__					,						return_tensors="""pt"""   )
       lowerCamelCase    ,					lowerCamelCase	:			List[Any]           =   self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape					,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )					,						)






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase           ={
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase	:			List[str]           =   getattr(lowerCamelCase,			lowerCamelCase							)

 if weight_type is not None:
  lowerCamelCase	:			str           =   getattr(lowerCamelCase,			lowerCamelCase							).shape
 else:
  lowerCamelCase	:			Union[str, Any]           =   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	:			Dict           =   value
 elif weight_type == "weight_g":
  lowerCamelCase	:			List[str]           =   value
 elif weight_type == "weight_v":
  lowerCamelCase	:			Any           =   value
 elif weight_type == "bias":
  lowerCamelCase	:			Union[str, Any]           =   value
 else:
  lowerCamelCase	:			int           =   value

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   []
 lowerCamelCase	:			Union[str, Any]           =   fairseq_model.state_dict()

 lowerCamelCase	:			Optional[int]           =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase	:			str           =   False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			hf_model.config.feat_extract_norm == """group""",			)
   lowerCamelCase	:			int           =   True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase	:			str           =   """sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

    if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
     lowerCamelCase	:			int           =   True
     if "*" in mapped_key:
      lowerCamelCase	:			Any           =   name.split(lowerCamelCase							)[0].split("""."""							)[-2]
      if not layer_index.isnumeric():
       continue
      lowerCamelCase	:			Union[str, Any]           =   mapped_key.replace("""*""",			lowerCamelCase							)
     if "weight_g" in name:
      lowerCamelCase	:			Any           =   """weight_g"""
     elif "weight_v" in name:
      lowerCamelCase	:			int           =   """weight_v"""
     elif "weight" in name:
      lowerCamelCase	:			int           =   """weight"""
     elif "bias" in name:
      lowerCamelCase	:			int           =   """bias"""
     else:
      lowerCamelCase	:			Dict           =   None
     set_recursively(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)
    continue
  if not is_used:
   unused_weights.append(lowerCamelCase							)

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   full_name.split("""conv_layers."""							)[-1]
 lowerCamelCase	:			Optional[int]           =   name.split("""."""							)
 lowerCamelCase	:			str           =   int(items[0]							)
 lowerCamelCase	:			Optional[int]           =   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	:			Union[str, Any]           =   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	:			int           =   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	:			Dict           =   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	:			Tuple           =   value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
 else:
  unused_weights.append(lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			List[Any]           =   SEWDConfig()
 if is_finetuned:
  lowerCamelCase	:			int           =   model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase	:			Optional[Any]           =   model.cfg

 lowerCamelCase	:			List[str]           =   fs_config.conv_bias
 lowerCamelCase	:			Tuple           =   eval(fs_config.conv_feature_layers							)
 lowerCamelCase	:			List[str]           =   [x[0] for x in conv_layers]
 lowerCamelCase	:			List[Any]           =   [x[1] for x in conv_layers]
 lowerCamelCase	:			int           =   [x[2] for x in conv_layers]
 lowerCamelCase	:			List[str]           =   """gelu"""
 lowerCamelCase	:			Union[str, Any]           =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
 lowerCamelCase	:			List[str]           =   0.0
 lowerCamelCase	:			Optional[int]           =   fs_config.activation_fn.name
 lowerCamelCase	:			Union[str, Any]           =   fs_config.encoder_embed_dim
 lowerCamelCase	:			Tuple           =   0.0_2
 lowerCamelCase	:			str           =   fs_config.encoder_ffn_embed_dim
 lowerCamelCase	:			Dict           =   1e-5
 lowerCamelCase	:			str           =   fs_config.encoder_layerdrop
 lowerCamelCase	:			Optional[Any]           =   fs_config.encoder_attention_heads
 lowerCamelCase	:			List[Any]           =   fs_config.conv_pos_groups
 lowerCamelCase	:			Optional[int]           =   fs_config.conv_pos
 lowerCamelCase	:			Dict           =   len(lowerCamelCase							)
 lowerCamelCase	:			List[Any]           =   fs_config.encoder_layers
 lowerCamelCase	:			Optional[int]           =   fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 lowerCamelCase	:			Optional[int]           =   fs_config.max_position_embeddings
 lowerCamelCase	:			List[str]           =   fs_config.position_buckets
 lowerCamelCase	:			List[Any]           =   fs_config.share_att_key
 lowerCamelCase	:			Dict           =   fs_config.relative_attention
 lowerCamelCase	:			List[Any]           =   fs_config.position_biased_input
 lowerCamelCase	:			Dict           =   tuple(fs_config.pos_att_type.split("""|"""							)							)
 lowerCamelCase	:			List[str]           =   fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  lowerCamelCase	:			Tuple           =   model.cfg
  lowerCamelCase	:			Any           =   fs_config.final_dropout
  lowerCamelCase	:			Tuple           =   fs_config.layerdrop
 lowerCamelCase	:			Dict           =   fs_config.activation_dropout
 lowerCamelCase	:			Union[str, Any]           =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 lowerCamelCase	:			List[str]           =   fs_config.attention_dropout
 lowerCamelCase	:			Tuple           =   fs_config.dropout_input
 lowerCamelCase	:			Dict           =   fs_config.dropout
 lowerCamelCase	:			Dict           =   fs_config.mask_channel_length
 lowerCamelCase	:			str           =   fs_config.mask_channel_prob
 lowerCamelCase	:			Optional[Any]           =   fs_config.mask_length
 lowerCamelCase	:			List[str]           =   fs_config.mask_prob

 lowerCamelCase	:			Dict           =   """Wav2Vec2FeatureExtractor"""
 lowerCamelCase	:			int           =   """Wav2Vec2CTCTokenizer"""

 return config





@torch.no_grad()
def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=True							):

 if is_finetuned:
  lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			Any           =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path],			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
 else:
  lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			List[str]           =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

 if config_path is not None:
  lowerCamelCase	:			List[str]           =   SEWDConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase	:			Optional[int]           =   convert_config(model[0],			lowerCamelCase							)
 lowerCamelCase	:			Dict           =   model[0].eval()

 lowerCamelCase	:			Dict           =   True if config.feat_extract_norm == """layer""" else False
 lowerCamelCase	:			List[str]           =   WavaVecaFeatureExtractor(
     feature_size=1,			sampling_rate=1_6000,			padding_value=0,			do_normalize=lowerCamelCase,			return_attention_mask=lowerCamelCase,			)

 if is_finetuned:
  if dict_path:
   lowerCamelCase	:			List[Any]           =   Dictionary.load(lowerCamelCase							)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase	:			Union[str, Any]           =   target_dict.pad_index
   lowerCamelCase	:			List[Any]           =   target_dict.bos_index
   lowerCamelCase	:			List[str]           =   target_dict.pad_index
   lowerCamelCase	:			Optional[Any]           =   target_dict.bos_index
   lowerCamelCase	:			int           =   target_dict.eos_index
   lowerCamelCase	:			Optional[int]           =   len(target_dict.symbols							)
   lowerCamelCase	:			str           =   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							)
   with open(lowerCamelCase,			"""w""",			encoding="""utf-8"""							) as vocab_handle:
    json.dump(target_dict.indices,			lowerCamelCase							)
   lowerCamelCase	:			Union[str, Any]           =   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	:			Dict           =   WavaVecaProcessor(feature_extractor=lowerCamelCase,			tokenizer=lowerCamelCase							)
   processor.save_pretrained(lowerCamelCase							)

  lowerCamelCase	:			Union[str, Any]           =   SEWDForCTC(lowerCamelCase							)
 else:
  lowerCamelCase	:			Dict           =   SEWDModel(lowerCamelCase							)
  feature_extractor.save_pretrained(lowerCamelCase							)

 recursively_load_weights(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 hf_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase           =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(
          """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      _lowerCamelCase           =parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )




