


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


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

__lowerCamelCase   	=  [
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

__lowerCamelCase   	=  {
    '''esm1b_t33_650M_UR50S''': esm_module.pretrained.esm1b_t33_650M_UR50S,
    '''esm1v_t33_650M_UR90S_1''': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    '''esm1v_t33_650M_UR90S_2''': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    '''esm1v_t33_650M_UR90S_3''': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    '''esm1v_t33_650M_UR90S_4''': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    '''esm1v_t33_650M_UR90S_5''': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    '''esm2_t48_15B_UR50D''': esm_module.pretrained.esm2_t48_15B_UR50D,
    '''esm2_t36_3B_UR50D''': esm_module.pretrained.esm2_t36_3B_UR50D,
    '''esm2_t33_650M_UR50D''': esm_module.pretrained.esm2_t33_650M_UR50D,
    '''esm2_t30_150M_UR50D''': esm_module.pretrained.esm2_t30_150M_UR50D,
    '''esm2_t12_35M_UR50D''': esm_module.pretrained.esm2_t12_35M_UR50D,
    '''esm2_t6_8M_UR50D''': esm_module.pretrained.esm2_t6_8M_UR50D,
    '''esmfold_v1''': esmfold_va,
}

__lowerCamelCase   	=  list('''ARNDCQEGHILKMFPSTWYV''')

__lowerCamelCase   	=  restypes + ['''X''']
__lowerCamelCase   	=  restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']



def 			_a      (    ):
     with TemporaryDirectory() as tempdir:
          a_     :  int				      =     """\n""".join(__UpperCamelCase			)
          a_     :  Tuple				      =     Path(__UpperCamelCase			) / """vocab.txt"""
          vocab_file.write_text(__UpperCamelCase			)
          a_     :  int				      =     EsmTokenizer(vocab_file=str(__UpperCamelCase			)			)
     a_     :  Any				      =     0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[str]				      =     our_module.load_state_dict(original_module.state_dict()			)
     if status.missing_keys:
          raise ValueError(F'''Missing keys: {status.missing_keys}'''			)
     if status.unexpected_keys:
          raise ValueError(F'''Unexpected keys: {status.unexpected_keys}'''			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     if model.startswith("""esmfold"""			):
          a_     :  Optional[Any]				      =     MODEL_MAPPING[model]()
     else:
          a_      ,      a_     :  Optional[int]				      =     MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith("""esmfold"""			):
          a_     :  List[str]				      =     esm.esm.embed_dim
          a_     :  Any				      =     esm.esm.num_layers
          a_     :  Any				      =     esm.esm.attention_heads
          a_     :  int				      =     4 * embed_dim
          a_     :  Any				      =     esm.esm.token_dropout
          a_     :  Union[str, Any]				      =     False  # This code path does not exist in ESM-2
          a_     :  Union[str, Any]				      =     """rotary"""
          a_     :  List[Any]				      =     True
          a_     :  str				      =     EsmFoldConfig()
          for key, val in esm.cfg.items():
               if hasattr(__UpperCamelCase	,	__UpperCamelCase			) and key != "trunk":
                    setattr(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk	,	__UpperCamelCase			) and key != "structure_module":
                    setattr(esmfold_config.trunk	,	__UpperCamelCase	,	__UpperCamelCase			)
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module	,	__UpperCamelCase			):
                    setattr(esmfold_config.trunk.structure_module	,	__UpperCamelCase	,	__UpperCamelCase			)
     elif hasattr(__UpperCamelCase	,	"""args"""			):
          # Indicates an ESM-1b or ESM-1v model
          a_     :  Union[str, Any]				      =     esm.args.embed_dim
          a_     :  Optional[Any]				      =     esm.args.layers
          a_     :  Dict				      =     esm.args.attention_heads
          a_     :  Dict				      =     esm.args.ffn_embed_dim
          a_     :  Optional[Any]				      =     esm.args.token_dropout
          a_     :  Optional[int]				      =     True if esm.emb_layer_norm_before else False
          a_     :  Dict				      =     """absolute"""
          a_     :  Dict				      =     False
          a_     :  Tuple				      =     None
     else:
          # Indicates an ESM-2 model
          a_     :  Union[str, Any]				      =     esm.embed_dim
          a_     :  Optional[Any]				      =     esm.num_layers
          a_     :  int				      =     esm.attention_heads
          a_     :  Any				      =     4 * embed_dim  # This is hardcoded in ESM-2
          a_     :  Optional[Any]				      =     esm.token_dropout
          a_     :  Optional[int]				      =     False  # This code path does not exist in ESM-2
          a_     :  Union[str, Any]				      =     """rotary"""
          a_     :  List[Any]				      =     False
          a_     :  Union[str, Any]				      =     None

     if is_folding_model:
          a_     :  Any				      =     esm.esm.alphabet
     a_     :  int				      =     tuple(alphabet.all_toks			)
     a_     :  Tuple				      =     alphabet.mask_idx
     a_     :  int				      =     alphabet.padding_idx

     if is_folding_model:
          a_     :  Any				      =     esm.esm
     else:
          a_     :  Union[str, Any]				      =     esm

     a_     :  Dict				      =     EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings	,	mask_token_id=__UpperCamelCase	,	hidden_size=__UpperCamelCase	,	num_hidden_layers=__UpperCamelCase	,	num_attention_heads=__UpperCamelCase	,	intermediate_size=__UpperCamelCase	,	max_position_embeddings=1_0_2_6	,	layer_norm_eps=1E-5	,	attention_probs_dropout_prob=0.0	,	hidden_dropout_prob=0.0	,	pad_token_id=__UpperCamelCase	,	emb_layer_norm_before=__UpperCamelCase	,	token_dropout=__UpperCamelCase	,	position_embedding_type=__UpperCamelCase	,	is_folding_model=__UpperCamelCase	,	esmfold_config=__UpperCamelCase	,	vocab_list=__UpperCamelCase	,	)
     if classification_head:
          a_     :  Any				      =     esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our ESM config:"""	,	__UpperCamelCase			)

     if model.startswith("""esmfold"""			):
          a_     :  Any				      =     EsmForProteinFolding
     elif classification_head:
          a_     :  Tuple				      =     EsmForSequenceClassification
     else:
          a_     :  Optional[int]				      =     EsmForMaskedLM
     a_     :  Optional[Any]				      =     model_class(__UpperCamelCase			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     a_     :  Union[str, Any]				      =     original_esm_model.embed_tokens.weight
     if position_embedding_type == "absolute":
          a_     :  str				      =     original_esm_model.embed_positions.weight

     if config.emb_layer_norm_before:
          a_     :  List[Any]				      =     original_esm_model.emb_layer_norm_before.weight
          a_     :  Union[str, Any]				      =     original_esm_model.emb_layer_norm_before.bias

     a_     :  Optional[int]				      =     original_esm_model.emb_layer_norm_after.weight
     a_     :  Optional[int]				      =     original_esm_model.emb_layer_norm_after.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          a_     :  EsmLayer				      =     model.esm.encoder.layer[i]
          # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
          a_     :  Dict				      =     original_esm_model.layers[i]

          # self attention
          a_     :  EsmSelfAttention				      =     layer.attention.self
          assert (
              esm_layer.self_attn.k_proj.weight.data.shape
              == esm_layer.self_attn.q_proj.weight.data.shape
              == esm_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)			)
          )

          a_     :  Union[str, Any]				      =     esm_layer.self_attn.q_proj.weight
          a_     :  int				      =     esm_layer.self_attn.q_proj.bias
          a_     :  List[str]				      =     esm_layer.self_attn.k_proj.weight
          a_     :  List[Any]				      =     esm_layer.self_attn.k_proj.bias
          a_     :  Union[str, Any]				      =     esm_layer.self_attn.v_proj.weight
          a_     :  Dict				      =     esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn	,	"""rot_emb"""	,	__UpperCamelCase			) is not None:
               # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
               # During the training of ESM-2 the model was converted to float16 precision, which also converts
               # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
               # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
               # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
               # we make sure the new model copies the data from the old inv_freq.
               a_     :  int				      =     esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          a_     :  str				      =     esm_layer.self_attn_layer_norm.weight
          a_     :  str				      =     esm_layer.self_attn_layer_norm.bias
          a_     :  Optional[Any]				      =     esm_layer.final_layer_norm.weight
          a_     :  Tuple				      =     esm_layer.final_layer_norm.bias

          # self-attention output
          a_     :  EsmSelfOutput				      =     layer.attention.output
          assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
          a_     :  Any				      =     esm_layer.self_attn.out_proj.weight
          a_     :  str				      =     esm_layer.self_attn.out_proj.bias

          # intermediate
          a_     :  EsmIntermediate				      =     layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          a_     :  int				      =     esm_layer.fca.weight
          a_     :  Tuple				      =     esm_layer.fca.bias

          # output
          a_     :  EsmOutput				      =     layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          a_     :  List[str]				      =     esm_layer.fca.weight
          a_     :  Any				      =     esm_layer.fca.bias
          # end of layer

     if is_folding_model:
          a_     :  Union[str, Any]				      =     esm.esm_s_combine.data
          a_     :  int				      =     esm.afa_to_esm.data
          transfer_and_check_weights(esm.embedding	,	model.embedding			)
          transfer_and_check_weights(esm.esm_s_mlp	,	model.esm_s_mlp			)
          transfer_and_check_weights(esm.trunk	,	model.trunk			)
          transfer_and_check_weights(esm.distogram_head	,	model.distogram_head			)
          transfer_and_check_weights(esm.ptm_head	,	model.ptm_head			)
          transfer_and_check_weights(esm.lm_head	,	model.lm_head			)
          transfer_and_check_weights(esm.lddt_head	,	model.lddt_head			)

     elif classification_head:
          a_     :  Tuple				      =     esm.esm.classification_heads["""mnli"""].dense.weight
          a_     :  Dict				      =     esm.classification_heads["""mnli"""].dense.bias
          a_     :  Dict				      =     esm.classification_heads["""mnli"""].out_proj.weight
          a_     :  Optional[Any]				      =     esm.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          a_     :  str				      =     esm.lm_head.dense.weight
          a_     :  List[str]				      =     esm.lm_head.dense.bias
          a_     :  Dict				      =     esm.lm_head.layer_norm.weight
          a_     :  List[Any]				      =     esm.lm_head.layer_norm.bias
          a_     :  Tuple				      =     esm.lm_head.weight
          a_     :  Optional[Any]				      =     esm.lm_head.bias

     # Contact prediction head
     transfer_and_check_weights(esm.contact_head	,	model.esm.contact_head			)

     # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
     if is_folding_model:
          # Folding models aren't trained on masked inputs and don't like mask tokens.
          a_     :  List[str]				      =     SAMPLE_DATA[:2]
     else:
          a_     :  Tuple				      =     SAMPLE_DATA

     if is_folding_model:
          a_     :  str				      =     get_esmfold_tokenizer()
          a_     :  Tuple				      =     hf_tokenizer(
              [row[1] for row in sample_data]	,	return_tensors="""pt"""	,	padding=__UpperCamelCase	,	add_special_tokens=__UpperCamelCase			)
          a_      ,      a_      ,      a_      ,      a_      ,      a_     :  List[Any]				      =     esmfold_encode_sequences([row[1] for row in sample_data]			)
          a_     :  Dict				      =     torch.all(hf_tokens["""input_ids"""] == esmfold_aas			) and torch.all(
              hf_tokens["""attention_mask"""] == esmfold_mask			)
     else:
          # Let's check that we get the same results.
          a_     :  List[str]				      =     alphabet.get_batch_converter()
          a_      ,      a_      ,      a_     :  Dict				      =     batch_converter(__UpperCamelCase			)
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               a_     :  Union[str, Any]				      =     """\n""".join(alphabet.all_toks			)
               a_     :  str				      =     Path(__UpperCamelCase			) / """vocab.txt"""
               vocab_file.write_text(__UpperCamelCase			)
               a_     :  int				      =     EsmTokenizer(vocab_file=str(__UpperCamelCase			)			)

          a_     :  List[str]				      =     hf_tokenizer([row[1] for row in sample_data]	,	return_tensors="""pt"""	,	padding=__UpperCamelCase			)
          a_     :  Optional[Any]				      =     torch.all(hf_tokens["""input_ids"""] == batch_tokens			)

     print("""Do both models tokenizers output the same tokens?"""	,	"""🔥""" if success else """💩"""			)
     if not success:
          raise Exception("""Tokenization does not match!"""			)

     with torch.no_grad():
          if is_folding_model:
               # Let's test the model in parts
               # ESMFold always converts the ESM stem to float16, which requires float16 ops
               # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
               # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
               # original and the converted model on the GPU at the same time.
               a_     :  Dict				      =     esm.cuda().infer([row[1] for row in sample_data]			)
               a_     :  Union[str, Any]				      =     model.cuda()(
                   input_ids=hf_tokens["""input_ids"""].cuda()	,	attention_mask=hf_tokens["""attention_mask"""].cuda()			)
          else:
               a_     :  Dict				      =     model(**__UpperCamelCase	,	output_hidden_states=__UpperCamelCase			)
               a_     :  Tuple				      =     our_output["""logits"""]
               if classification_head:
                    a_     :  Optional[int]				      =     esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCamelCase			)			)
               else:
                    a_     :  Union[str, Any]				      =     esm(hf_tokens["""input_ids"""]	,	repr_layers=list(range(9_9_9			)			)			)
                    a_     :  Optional[Any]				      =     their_output["""logits"""]

          if is_folding_model:
               a_     :  List[str]				      =     torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(our_output["""positions"""]	,	their_output["""positions"""]	,	atol=1E-5			)
          else:
               a_     :  Optional[int]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  str				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	atol=1E-5			)

          print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-5
          print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩"""			)

          if not success:
               raise Exception("""Something went wRoNg"""			)

          if not is_folding_model:
               # Let's check contact prediction too
               a_     :  Any				      =     model.predict_contacts(hf_tokens["""input_ids"""]	,	hf_tokens["""attention_mask"""]			)
               a_     :  List[Any]				      =     esm.predict_contacts(hf_tokens["""input_ids"""]			)
               a_     :  Optional[Any]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	atol=1E-5			)

               print("""Contact prediction testing:"""			)
               print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-5
               print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩"""			)

               if not success:
                    raise Exception("""Something went wRoNg"""			)

          pathlib.Path(__UpperCamelCase			).mkdir(parents=__UpperCamelCase	,	exist_ok=__UpperCamelCase			)
          print(F'''Saving model to {pytorch_dump_folder_path}'''			)
          model.save_pretrained(__UpperCamelCase			)

          del esm  # Free up some memory before continuing

     print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
     hf_tokenizer.save_pretrained(__UpperCamelCase			)

     if push_to_repo:
          model.push_to_hub(repo_id=__UpperCamelCase	,	use_auth_token=__UpperCamelCase			)
          hf_tokenizer.push_to_hub(repo_id=__UpperCamelCase	,	use_auth_token=__UpperCamelCase			)


if __name__ == "__main__":
       __lowerCamelCase   	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_dump_folder_path''', type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
       )
       parser.add_argument('''--model''', default=None, type=str, required=True, help='''Name of model to convert.''')
       parser.add_argument('''--push_to_repo''', type=str, help='''Repo to upload to (including username!).''')
       parser.add_argument('''--auth_token''', type=str, help='''HuggingFace auth token.''')
       __lowerCamelCase   	=  parser.parse_args()
       convert_esm_checkpoint_to_pytorch(
           args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
       )











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__snake_case     :     Optional[int]       =  """\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

__snake_case     :     Any       =  """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

__snake_case     :     Optional[int]       =  """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION)
class      __SCREAMING_SNAKE_CASE       ( datasets.Metric):






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,    citation=_CITATION  ,    homepage='https://github.com/chakki-works/seqeval'  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												            'references': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												        } )  ,    codebase_urls=['https://github.com/chakki-works/seqeval']  ,    reference_urls=['https://github.com/chakki-works/seqeval']  ,    )







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = "warn"  ,    ):






												"""simple docstring"""
												if scheme is not None:
																		try:
																								lowerCAmelCase__         =    importlib.import_module('seqeval.scheme' )
																								lowerCAmelCase__         =    getattr(_UpperCamelCase  ,    _UpperCamelCase )
																		except AttributeError:
																								raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}" )
												lowerCAmelCase__         =    classification_report(
												    y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase  ,    suffix=_UpperCamelCase  ,    output_dict=_UpperCamelCase  ,    scheme=_UpperCamelCase  ,    mode=_UpperCamelCase  ,    sample_weight=_UpperCamelCase  ,    zero_division=_UpperCamelCase  ,    )
												report.pop('macro avg' )
												report.pop('weighted avg' )
												lowerCAmelCase__         =    report.pop('micro avg' )

												lowerCAmelCase__         =    {
												    type_name: {
												        'precision': score['precision'],
												        'recall': score['recall'],
												        'f1': score['f1-score'],
												        'number': score['support'],
												    }
												    for type_name, score in report.items()
												}
												lowerCAmelCase__         =    overall_score['precision']
												lowerCAmelCase__         =    overall_score['recall']
												lowerCAmelCase__         =    overall_score['f1-score']
												lowerCAmelCase__         =    accuracy_score(y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase )

												return scores





