





'''simple docstring'''






import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


A_		:  Optional[Any]		 =						"\\n\n"

A_		:  int		 =						"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

A_		:  Any		 =						"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,				_KWARGS_DESCRIPTION							)
class 					__snake_case  (					datasets.Metric							):






   '''simple docstring'''





   def 		__UpperCamelCase						(							self       ):
    return datasets.MetricInfo(
        description=_DESCRIPTION	,		citation=_CITATION	,		inputs_description=_KWARGS_DESCRIPTION	,		features=datasets.Features(
            {
                """input_texts""": datasets.Value("""string"""       ),
            }       )	,		reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""]	,		)


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE = 1_6	,		__SCREAMING_SNAKE_CASE = True	,		__SCREAMING_SNAKE_CASE=None       ):
    if device is not None:
     assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
     if device == "gpu":
      snake_case__     :					int    =  """cuda"""
    else:
     snake_case__     :					Any    =  """cuda""" if torch.cuda.is_available() else """cpu"""

    snake_case__     :					int    =  AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  model.to(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[Any]    =  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )

    # if batch_size > 1 (which generally leads to padding being required), and
    # if there is not an already assigned pad_token, assign an existing
    # special token to also be the padding token
    if tokenizer.pad_token is None and batch_size > 1:
     snake_case__     :					List[Any]    =  list(tokenizer.special_tokens_map_extended.values()       )
     # check that the model already has at least one special token defined
     assert (
         len(__SCREAMING_SNAKE_CASE       ) > 0
     ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
     # assign one of the special tokens to also be the pad token
     tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]}       )

    if add_start_token:
     # leave room for <BOS> token to be added:
     assert (
         tokenizer.bos_token is not None
     ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
     snake_case__     :					Dict    =  model.config.max_length - 1
    else:
     snake_case__     :					List[str]    =  model.config.max_length

    snake_case__     :					str    =  tokenizer(
        __SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE	,		padding=__SCREAMING_SNAKE_CASE	,		truncation=__SCREAMING_SNAKE_CASE	,		max_length=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""	,		return_attention_mask=__SCREAMING_SNAKE_CASE	,		).to(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  encodings["""input_ids"""]
    snake_case__     :					Union[str, Any]    =  encodings["""attention_mask"""]

    # check that each input is long enough:
    if add_start_token:
     assert torch.all(torch.ge(attn_masks.sum(1       )	,		1       )       ), "Each input text must be at least one token long."
    else:
     assert torch.all(
         torch.ge(attn_masks.sum(1       )	,		2       )       ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

    snake_case__     :					int    =  []
    snake_case__     :					Optional[Any]    =  CrossEntropyLoss(reduction="""none"""       )

    for start_index in logging.tqdm(range(0	,		len(__SCREAMING_SNAKE_CASE       )	,		__SCREAMING_SNAKE_CASE       )       ):
     snake_case__     :					Optional[int]    =  min(start_index + batch_size	,		len(__SCREAMING_SNAKE_CASE       )       )
     snake_case__     :					int    =  encoded_texts[start_index:end_index]
     snake_case__     :					Optional[Any]    =  attn_masks[start_index:end_index]

     if add_start_token:
      snake_case__     :					Any    =  torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(__SCREAMING_SNAKE_CASE       )
      snake_case__     :					int    =  torch.cat([bos_tokens_tensor, encoded_batch]	,		dim=1       )
      snake_case__     :					Optional[Any]    =  torch.cat(
          [torch.ones(bos_tokens_tensor.size()	,		dtype=torch.intaa       ).to(__SCREAMING_SNAKE_CASE       ), attn_mask]	,		dim=1       )

     snake_case__     :					Union[str, Any]    =  encoded_batch

     with torch.no_grad():
      snake_case__     :					Any    =  model(__SCREAMING_SNAKE_CASE	,		attention_mask=__SCREAMING_SNAKE_CASE       ).logits

     snake_case__     :					Optional[Any]    =  out_logits[..., :-1, :].contiguous()
     snake_case__     :					Tuple    =  labels[..., 1:].contiguous()
     snake_case__     :					Tuple    =  attn_mask[..., 1:].contiguous()

     snake_case__     :					Optional[Any]    =  torch.expa(
         (loss_fct(shift_logits.transpose(1	,		2       )	,		__SCREAMING_SNAKE_CASE       ) * shift_attention_mask_batch).sum(1       )
         / shift_attention_mask_batch.sum(1       )       )

     ppls += perplexity_batch.tolist()

    return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE       )}











'''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()
lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : int         =       {
    '''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''',
}
lowercase__       : Dict         =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Any					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					int						=			getattr(__snake_case					,				__snake_case     )

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

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






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					List[str]     )	->	List[Any]:
     __A		:					Optional[Any]						=			[]
     __A		:					Any						=			fairseq_model.state_dict()

     __A		:					Union[str, Any]						=			hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __A		:					Union[str, Any]						=			False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case					,				__snake_case					,				__snake_case					,				__snake_case					,				hf_model.config.feat_extract_norm == 'group'					,				)
               __A		:					Optional[Any]						=			True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                         __A		:					int						=			True
                         if "*" in mapped_key:
                              __A		:					Any						=			name.split(__snake_case     )[0].split('.'     )[-2]
                              __A		:					List[Any]						=			mapped_key.replace('*'					,				__snake_case     )
                         if "weight_g" in name:
                              __A		:					Optional[Any]						=			'weight_g'
                         elif "weight_v" in name:
                              __A		:					Union[str, Any]						=			'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __A		:					Optional[Any]						=			'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __A		:					Tuple						=			'weight'
                         else:
                              __A		:					Dict						=			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 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[int]     )	->	int:
     __A		:					int						=			full_name.split('conv_layers.'     )[-1]
     __A		:					List[str]						=			name.split('.'     )
     __A		:					Optional[int]						=			int(items[0]     )
     __A		:					str						=			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		:					Optional[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.'
               )
               __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		:					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.'
               )
               __A		:					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 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Tuple=None     )	->	Any:
     # load the pre-trained checkpoints
     __A		:					List[str]						=			torch.load(__snake_case     )
     __A		:					Dict						=			WavLMConfigOrig(checkpoint['cfg']     )
     __A		:					Optional[int]						=			WavLMOrig(__snake_case     )
     model.load_state_dict(checkpoint['model']     )
     model.eval()

     if config_path is not None:
          __A		:					List[Any]						=			WavLMConfig.from_pretrained(__snake_case     )
     else:
          __A		:					Dict						=			WavLMConfig()

     __A		:					Optional[Any]						=			WavLMModel(__snake_case     )

     recursively_load_weights(__snake_case					,				__snake_case     )

     hf_wavlm.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Tuple         =       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''')
  lowercase__       : Any         =       parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)