






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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase   :   int      =    logging.get_logger(__name__)

__UpperCamelCase   :   Optional[int]      =    {
    """post_extract_proj""": """feature_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.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 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	for attribute in key.split("""."""       ):
		__lowercase   							=	getattr(lowerCamelCase					,	lowerCamelCase       )

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

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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[]
	__lowercase   							=	fairseq_model.state_dict()

	__lowercase   							=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__lowercase   							=	False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	hf_model.config.feat_extract_norm == """group"""					,	)
			__lowercase   							=	True
		else:
			for key, mapped_key in MAPPING.items():
				__lowercase   							=	"""sew.""" + 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]:
					__lowercase   							=	True
					if "*" in mapped_key:
						__lowercase   							=	name.split(lowerCamelCase       )[0].split("""."""       )[-2]
						__lowercase   							=	mapped_key.replace("""*"""					,	lowerCamelCase       )
					if "weight_g" in name:
						__lowercase   							=	"""weight_g"""
					elif "weight_v" in name:
						__lowercase   							=	"""weight_v"""
					elif "weight" in name:
						__lowercase   							=	"""weight"""
					elif "bias" in name:
						__lowercase   							=	"""bias"""
					else:
						__lowercase   							=	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 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	SEWConfig()
	if is_finetuned:
		__lowercase   							=	model.wav_encoder.wav_model.cfg
	else:
		__lowercase   							=	model.cfg

	__lowercase   							=	fs_config.conv_bias
	__lowercase   							=	eval(fs_config.conv_feature_layers       )
	__lowercase   							=	[x[0] for x in conv_layers]
	__lowercase   							=	[x[1] for x in conv_layers]
	__lowercase   							=	[x[2] for x in conv_layers]
	__lowercase   							=	"""gelu"""
	__lowercase   							=	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	__lowercase   							=	0.0
	__lowercase   							=	fs_config.activation_fn.name
	__lowercase   							=	fs_config.encoder_embed_dim
	__lowercase   							=	0.02
	__lowercase   							=	fs_config.encoder_ffn_embed_dim
	__lowercase   							=	1e-5
	__lowercase   							=	fs_config.encoder_layerdrop
	__lowercase   							=	fs_config.encoder_attention_heads
	__lowercase   							=	fs_config.conv_pos_groups
	__lowercase   							=	fs_config.conv_pos
	__lowercase   							=	len(lowerCamelCase       )
	__lowercase   							=	fs_config.encoder_layers
	__lowercase   							=	fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		__lowercase   							=	model.cfg
		__lowercase   							=	fs_config.final_dropout
		__lowercase   							=	fs_config.layerdrop
	__lowercase   							=	fs_config.activation_dropout
	__lowercase   							=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	__lowercase   							=	fs_config.attention_dropout
	__lowercase   							=	fs_config.dropout_input
	__lowercase   							=	fs_config.dropout
	__lowercase   							=	fs_config.mask_channel_length
	__lowercase   							=	fs_config.mask_channel_prob
	__lowercase   							=	fs_config.mask_length
	__lowercase   							=	fs_config.mask_prob

	__lowercase   							=	"""Wav2Vec2FeatureExtractor"""
	__lowercase   							=	"""Wav2Vec2CTCTokenizer"""

	return config





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




	'''simple docstring'''







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

	if config_path is not None:
		__lowercase   							=	SEWConfig.from_pretrained(lowerCamelCase       )
	else:
		__lowercase   							=	convert_config(model[0]					,	lowerCamelCase       )
	__lowercase   							=	model[0].eval()

	__lowercase   							=	True if config.feat_extract_norm == """layer""" else False
	__lowercase   							=	WavaVecaFeatureExtractor(
	    feature_size=1					,	sampling_rate=16_000					,	padding_value=0					,	do_normalize=lowerCamelCase					,	return_attention_mask=lowerCamelCase					,	)

	if is_finetuned:
		if dict_path:
			__lowercase   							=	Dictionary.load(lowerCamelCase       )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__lowercase   							=	target_dict.pad_index
			__lowercase   							=	target_dict.bos_index
			__lowercase   							=	target_dict.pad_index
			__lowercase   							=	target_dict.bos_index
			__lowercase   							=	target_dict.eos_index
			__lowercase   							=	len(target_dict.symbols       )
			__lowercase   							=	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       )
			__lowercase   							=	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					,	)
			__lowercase   							=	WavaVecaProcessor(feature_extractor=lowerCamelCase					,	tokenizer=lowerCamelCase       )
			processor.save_pretrained(lowerCamelCase       )

		__lowercase   							=	SEWForCTC(lowerCamelCase       )
	else:
		__lowercase   							=	SEWModel(lowerCamelCase       )
		feature_extractor.save_pretrained(lowerCamelCase       )

	recursively_load_weights(lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       )

	hf_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
						__UpperCamelCase   :   str      =    argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
						parser.add_argument("""--config_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"""
						)
						__UpperCamelCase   :   Union[str, Any]      =    parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)






'''simple docstring'''





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



