





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCAmelCase 				=     """src/diffusers"""

# Matches is_xxx_available()
_lowerCAmelCase 				=     re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
_lowerCAmelCase 				=     re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


_lowerCAmelCase 				=     """
{0} = None
"""

_lowerCAmelCase 				=     """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


_lowerCAmelCase 				=     """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
			'''simple docstring'''


			A_     :				Any			      =	_re_backend.findall(_lowerCAmelCase     )
			if len(_lowerCAmelCase     ) == 0:
						return None

			return "_and_".join(_lowerCAmelCase     )



def     _lowerCAmelCase		(  ):
			'''simple docstring'''


			with open(os.path.join(_lowerCAmelCase  ,"""__init__.py"""     )  ,"""r"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
						A_     :				str			      =	f.readlines()

			# Get to the point we do the actual imports for type checking
			A_     :				Optional[int]			      =	0
			A_     :				List[Any]			      =	{}
			# Go through the end of the file
			while line_index < len(_lowerCAmelCase     ):
						# If the line contains is_backend_available, we grab all objects associated with the `else` block
						A_     :				Dict			      =	find_backend(lines[line_index]     )
						if backend is not None:
									while not lines[line_index].startswith("""else:"""     ):
												line_index += 1
									line_index += 1
									A_     :				Any			      =	[]
									# Until we unindent, add backend objects to the list
									while line_index < len(_lowerCAmelCase     ) and len(lines[line_index]     ) > 1:
												A_     :				Union[str, Any]			      =	lines[line_index]
												A_     :				Tuple			      =	_re_single_line_import.search(_lowerCAmelCase     )
												if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
												elif line.startswith(""" """ * 8     ):
															objects.append(line[8:-2]     )
												line_index += 1

									if len(_lowerCAmelCase     ) > 0:
												A_     :				Tuple			      =	objects
						else:
									line_index += 1

			return backend_specific_objects



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
			'''simple docstring'''


			if name.isupper():
						return DUMMY_CONSTANT.format(_lowerCAmelCase     )
			elif name.islower():
						return DUMMY_FUNCTION.format(_lowerCAmelCase  ,_lowerCAmelCase     )
			else:
						return DUMMY_CLASS.format(_lowerCAmelCase  ,_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase=None     ):
			'''simple docstring'''


			if backend_specific_objects is None:
						A_     :				Dict			      =	read_init()
			# For special correspondence backend to module name as used in the function requires_modulename
			A_     :				Optional[Any]			      =	{}

			for backend, objects in backend_specific_objects.items():
						A_     :				int			      =	"""[""" + """, """.join(f"""\"{b}\"""" for b in backend.split("""_and_"""     )     ) + """]"""
						A_     :				Optional[Any]			      =	"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
						dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
						dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase  ,_lowerCAmelCase     ) for o in objects]     )
						A_     :				List[Any]			      =	dummy_file

			return dummy_files



def     _lowerCAmelCase		(  _lowerCAmelCase=False     ):
			'''simple docstring'''


			A_     :				Any			      =	create_dummy_files()
			# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
			A_     :				str			      =	{"""torch""": """pt"""}

			# Locate actual dummy modules and read their content.
			A_     :				Optional[Any]			      =	os.path.join(_lowerCAmelCase  ,"""utils"""     )
			A_     :				int			      =	{
			    backend: os.path.join(_lowerCAmelCase  ,f"""dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py"""     )
			    for backend in dummy_files.keys()
			}

			A_     :				Optional[Any]			      =	{}
			for backend, file_path in dummy_file_paths.items():
						if os.path.isfile(_lowerCAmelCase     ):
									with open(_lowerCAmelCase  ,"""r"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
												A_     :				Optional[int]			      =	f.read()
						else:
									A_     :				Optional[int]			      =	""""""

			for backend in dummy_files.keys():
						if dummy_files[backend] != actual_dummies[backend]:
									if overwrite:
												print(
												    f"""Updating diffusers.utils.dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py as the main """
												    """__init__ has new objects."""     )
												with open(dummy_file_paths[backend]  ,"""w"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
															f.write(dummy_files[backend]     )
									else:
												raise ValueError(
												    """The main __init__ has objects that are not present in """
												    f"""diffusers.utils.dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py. Run `make fix-copies` """
												    """to fix this."""     )


if __name__ == "__main__":
						_lowerCAmelCase 				=     argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_lowerCAmelCase 				=     parser.parse_args()

						check_dummies(args.fix_and_overwrite)













import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

_lowerCAmelCase 				=     {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_lowerCAmelCase 				=     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            A_     :				Tuple			      =	"""lm_head"""

      A_     :				int			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	fairseq_model.state_dict()

   A_     :				Any			      =	hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      A_     :				Any			      =	False
      if "conv_layers" in name:
         load_conv_layer(
             _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,hf_model.config.feat_extract_norm == """group"""  ,)
         A_     :				int			      =	True
      else:
         for key, mapped_key in MAPPING.items():
            A_     :				Any			      =	"""unispeech.""" + 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]:
               A_     :				Dict			      =	True
               if "*" in mapped_key:
                  A_     :				Any			      =	name.split(_lowerCAmelCase     )[0].split("""."""     )[-2]
                  A_     :				Optional[int]			      =	mapped_key.replace("""*"""  ,_lowerCAmelCase     )
               if "weight_g" in name:
                  A_     :				Tuple			      =	"""weight_g"""
               elif "weight_v" in name:
                  A_     :				Optional[int]			      =	"""weight_v"""
               elif "bias" in name:
                  A_     :				List[Any]			      =	"""bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  A_     :				Optional[int]			      =	"""weight"""
               else:
                  A_     :				Dict			      =	None
               set_recursively(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
            continue
      if not is_used:
         unused_weights.append(_lowerCAmelCase     )

   logger.warning(f"""Unused weights: {unused_weights}"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	full_name.split("""conv_layers."""     )[-1]
   A_     :				Union[str, Any]			      =	name.split("""."""     )
   A_     :				List[str]			      =	int(items[0]     )
   A_     :				Optional[Any]			      =	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[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."""
         )
         A_     :				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."
         )
         A_     :				List[str]			      =	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_     :				Tuple			      =	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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None  ,_lowerCAmelCase=True     ):
   '''simple docstring'''


   if config_path is not None:
      A_     :				Optional[int]			      =	UniSpeechConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				List[str]			      =	UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         A_     :				str			      =	Dictionary.load_from_json(_lowerCAmelCase     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         A_     :				List[Any]			      =	target_dict.pad_index
         A_     :				str			      =	target_dict.bos_index
         A_     :				Optional[Any]			      =	target_dict.eos_index
         A_     :				Optional[int]			      =	len(target_dict.symbols     )
         A_     :				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     )
         A_     :				Optional[int]			      =	target_dict.indices

         # fairseq has the <pad> and <s> switched
         A_     :				Dict			      =	4_2
         A_     :				Optional[Any]			      =	4_3
         with open(_lowerCAmelCase  ,"""w"""  ,encoding="""utf-8"""     ) as vocab_handle:
            json.dump(_lowerCAmelCase  ,_lowerCAmelCase     )
         A_     :				Tuple			      =	WavaVecaPhonemeCTCTokenizer(
             _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  ,)
         A_     :				Any			      =	True if config.feat_extract_norm == """layer""" else False
         A_     :				List[str]			      =	WavaVecaFeatureExtractor(
             feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=_lowerCAmelCase  ,return_attention_mask=_lowerCAmelCase  ,)
         A_     :				Optional[int]			      =	WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,tokenizer=_lowerCAmelCase     )
         processor.save_pretrained(_lowerCAmelCase     )

      A_     :				int			      =	UniSpeechForCTC(_lowerCAmelCase     )
   else:
      A_     :				str			      =	UniSpeechForPreTraining(_lowerCAmelCase     )

   if is_finetuned:
      A_      ,				A_      ,				A_     :				Union[str, Any]			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     ), """w2v_path""": checkpoint_path}     )
   else:
      A_      ,				A_      ,				A_     :				Any			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   A_     :				List[str]			      =	model[0].eval()

   recursively_load_weights(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   hf_unispeech.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(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )






