


"""simple docstring"""







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()
__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """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      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  for attribute in key.split("."       ):
    a						:		Union[str, Any]      							=			getattr(A_  ,   A_       )

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

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


def      lowercase			(				A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







  a						:		Tuple      							=			[]
  a						:		Any      							=			fairseq_model.state_dict()

  a						:		Optional[int]      							=			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    a						:		Optional[int]      							=			False
    if "conv_layers" in name:
      load_conv_layer(
          A_  ,   A_  ,   A_  ,   A_  ,   hf_model.config.feat_extract_norm == "group"  ,   )
      a						:		List[Any]      							=			True
    else:
      for key, mapped_key in MAPPING.items():
        a						:		Tuple      							=			"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]:
          a						:		List[str]      							=			True
          if "*" in mapped_key:
            a						:		Tuple      							=			name.split(A_       )[0].split("."       )[-2]
            a						:		Optional[Any]      							=			mapped_key.replace("*"  ,   A_       )
          if "weight_g" in name:
            a						:		List[Any]      							=			"weight_g"
          elif "weight_v" in name:
            a						:		Any      							=			"weight_v"
          elif "weight" in name:
            a						:		int      							=			"weight"
          elif "bias" in name:
            a						:		Optional[int]      							=			"bias"
          else:
            a						:		Tuple      							=			None
          set_recursively(A_  ,   A_  ,   A_  ,   A_  ,   A_       )
        continue
    if not is_used:
      unused_weights.append(A_       )

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


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      List[str]:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_       )->      int:
  '''simple docstring'''







  a						:		str      							=			SEWConfig()
  if is_finetuned:
    a						:		int      							=			model.wav_encoder.wav_model.cfg
  else:
    a						:		Optional[Any]      							=			model.cfg

  a						:		Optional[int]      							=			fs_config.conv_bias
  a						:		str      							=			eval(fs_config.conv_feature_layers       )
  a						:		Optional[Any]      							=			[x[0] for x in conv_layers]
  a						:		Optional[int]      							=			[x[1] for x in conv_layers]
  a						:		List[str]      							=			[x[2] for x in conv_layers]
  a						:		Dict      							=			"gelu"
  a						:		Union[str, Any]      							=			"layer" if fs_config.extractor_mode == "layer_norm" else "group"
  a						:		List[str]      							=			0.0
  a						:		Tuple      							=			fs_config.activation_fn.name
  a						:		Dict      							=			fs_config.encoder_embed_dim
  a						:		int      							=			0.0_2
  a						:		Dict      							=			fs_config.encoder_ffn_embed_dim
  a						:		Optional[int]      							=			1e-5
  a						:		Tuple      							=			fs_config.encoder_layerdrop
  a						:		Dict      							=			fs_config.encoder_attention_heads
  a						:		int      							=			fs_config.conv_pos_groups
  a						:		Tuple      							=			fs_config.conv_pos
  a						:		int      							=			len(A_       )
  a						:		Union[str, Any]      							=			fs_config.encoder_layers
  a						:		int      							=			fs_config.squeeze_factor

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

  a						:		Union[str, Any]      							=			"Wav2Vec2FeatureExtractor"
  a						:		Any      							=			"Wav2Vec2CTCTokenizer"

  return config


@torch.no_grad()
def      lowercase			(				A_  ,   A_  ,   A_=None  ,   A_=None  ,   A_=True       )->      List[Any]:
  '''simple docstring'''







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

  if config_path is not None:
    a						:		str      							=			SEWConfig.from_pretrained(A_       )
  else:
    a						:		int      							=			convert_config(model[0]  ,   A_       )
  a						:		List[Any]      							=			model[0].eval()

  a						:		int      							=			True if config.feat_extract_norm == "layer" else False
  a						:		Tuple      							=			WavaVecaFeatureExtractor(
      feature_size=1  ,   sampling_rate=16_000  ,   padding_value=0  ,   do_normalize=A_  ,   return_attention_mask=A_  ,   )

  if is_finetuned:
    if dict_path:
      a						:		Optional[Any]      							=			Dictionary.load(A_       )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a						:		Union[str, Any]      							=			target_dict.pad_index
      a						:		Optional[Any]      							=			target_dict.bos_index
      a						:		Optional[int]      							=			target_dict.pad_index
      a						:		List[str]      							=			target_dict.bos_index
      a						:		int      							=			target_dict.eos_index
      a						:		Optional[Any]      							=			len(target_dict.symbols       )
      a						:		Optional[int]      							=			os.path.join(A_  ,   "vocab.json"       )
      if not os.path.isdir(A_       ):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A_       )       )
        return
      os.makedirs(A_  ,   exist_ok=A_       )
      with open(A_  ,   "w"  ,   encoding="utf-8"       ) as vocab_handle:
        json.dump(target_dict.indices  ,   A_       )
      a						:		Optional[Any]      							=			WavaVecaCTCTokenizer(
          A_  ,   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=A_  ,   )
      a						:		Optional[Any]      							=			WavaVecaProcessor(feature_extractor=A_  ,   tokenizer=A_       )
      processor.save_pretrained(A_       )

    a						:		Optional[Any]      							=			SEWForCTC(A_       )
  else:
    a						:		Union[str, Any]      							=			SEWModel(A_       )
    feature_extractor.save_pretrained(A_       )

  recursively_load_weights(A_  ,   A_  ,   A_       )

  hf_model.save_pretrained(A_       )


if __name__ == "__main__":
 __lowercase    					=      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"""
 )
 __lowercase    					=      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 os
import sys
import unittest


__lowercase    					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase    					=      os.path.join(git_repo_path, """src""", """diffusers""")






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			find_backend("    if not is_torch_available():")
      self.assertEqual(__UpperCAmelCase				, "torch")

      # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
      # self.assertEqual(backend_with_underscore, "tensorflow_text")

      a						:		Dict      							=			find_backend("    if not (is_torch_available() and is_transformers_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers")

      # double_backend_with_underscore = find_backend(
      #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
      # )
      # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

      a						:		int      							=			find_backend(
          "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers_and_onnx")






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers"				, __UpperCAmelCase)
      self.assertIn("flax_and_transformers"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers_and_onnx"				, __UpperCAmelCase)

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("UNet2DModel"				, objects["torch"])
      self.assertIn("FlaxUNet2DConditionModel"				, objects["flax"])
      self.assertIn("StableDiffusionPipeline"				, objects["torch_and_transformers"])
      self.assertIn("FlaxStableDiffusionPipeline"				, objects["flax_and_transformers"])
      self.assertIn("LMSDiscreteScheduler"				, objects["torch_and_scipy"])
      self.assertIn("OnnxStableDiffusionPipeline"				, objects["torch_and_transformers_and_onnx"])






    def 	__snake_case							(					self				:    Tuple):
      a						:		Optional[int]      							=			create_dummy_object("CONSTANT"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, "\nCONSTANT = None\n")

      a						:		Dict      							=			create_dummy_object("function"				, "'torch'")
      self.assertEqual(
          __UpperCAmelCase				, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n")

      a						:		Optional[Any]      							=			"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
      a						:		int      							=			create_dummy_object("FakeClass"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[str]      							=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
      a						:		Tuple      							=			create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
      self.assertEqual(dummy_files["torch"]				, __UpperCAmelCase)






