
"""simple docstring"""




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
_A			:       Optional[Any]   		=       logging.get_logger(__name__)

_A			:       Optional[Any]   		=       {
    """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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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""",
}
_A			:       Any   		=       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       List[str]     , __snake_case				:       Optional[int]     , __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]  ) ->				Union[str, Any]:
       for attribute in key.split("."  ):
              lowercase    :				Tuple       		=							getattr(__snake_case     , __snake_case  )

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

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


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       Union[str, Any]  ) ->				Optional[int]:
       lowercase    :				Optional[int]       		=							[]
       lowercase    :				Optional[int]       		=							fairseq_model.state_dict()

       lowercase    :				Dict       		=							hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase    :				int       		=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         __snake_case     , __snake_case     , __snake_case     , __snake_case     , hf_model.config.feat_extract_norm == "group"     , )
                     lowercase    :				List[Any]       		=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."  )[-1] == name.split("."  )[0]:
                                   lowercase    :				List[str]       		=							True
                                   if "*" in mapped_key:
                                          lowercase    :				Optional[Any]       		=							name.split(__snake_case  )[0].split("."  )[-2]
                                          lowercase    :				Dict       		=							mapped_key.replace("*"     , __snake_case  )
                                   if "weight_g" in name:
                                          lowercase    :				Optional[Any]       		=							"weight_g"
                                   elif "weight_v" in name:
                                          lowercase    :				List[str]       		=							"weight_v"
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          lowercase    :				str       		=							"bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase    :				int       		=							"weight"
                                   else:
                                          lowercase    :				Any       		=							None

                                   set_recursively(__snake_case     , __snake_case     , __snake_case     , __snake_case     , __snake_case  )
                            continue
              if not is_used:
                     unused_weights.append(__snake_case  )

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


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


@torch.no_grad()
def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       List[str]     , __snake_case				:       Any=None  ) ->				Any:
       # load the pre-trained checkpoints
       lowercase    :				Union[str, Any]       		=							torch.load(__snake_case  )
       lowercase    :				Union[str, Any]       		=							WavLMConfigOrig(checkpoint["cfg"]  )
       lowercase    :				List[Any]       		=							WavLMOrig(__snake_case  )
       model.load_state_dict(checkpoint["model"]  )
       model.eval()

       if config_path is not None:
              lowercase    :				List[str]       		=							WavLMConfig.from_pretrained(__snake_case  )
       else:
              lowercase    :				List[str]       		=							WavLMConfig()

       lowercase    :				Union[str, Any]       		=							WavLMModel(__snake_case  )

       recursively_load_weights(__snake_case     , __snake_case  )

       hf_wavlm.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   _A			:       str   		=       parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_A			:       int   		=       """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""

class 		a__						(      unittest.TestCase,			a_						):
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							load_tool("text-question-answering"				)
          self.tool.setup()
          lowercase    :				Dict       		=							load_tool("text-question-answering"	,    remote=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.remote_tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.remote_tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)


