

"""simple docstring"""





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()
_a					 =				logging.get_logger(__name__)

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

_a					 =				{
    '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,
}

_a					 =				list('ARNDCQEGHILKMFPSTWYV')

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




def     _A    (    )      ->		List[str]:
   '''simple docstring'''


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




def     _A    (    UpperCamelCase_	:			Any,     UpperCamelCase_	:			str)      ->		List[str]:
   '''simple docstring'''


   __lowercase								=		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_	:			str,     UpperCamelCase_	:			str,     UpperCamelCase_	:			bool,     UpperCamelCase_	:			str,     UpperCamelCase_	:			str)      ->		Tuple:
   '''simple docstring'''


   if model.startswith("esmfold"):
      __lowercase								=		MODEL_MAPPING[model]()
   else:
      __lowercase						,__lowercase								=		MODEL_MAPPING[model]()
   esm.eval()  # disable dropout

   if model.startswith("esmfold"):
      __lowercase								=		esm.esm.embed_dim
      __lowercase								=		esm.esm.num_layers
      __lowercase								=		esm.esm.attention_heads
      __lowercase								=		4 * embed_dim
      __lowercase								=		esm.esm.token_dropout
      __lowercase								=		False  # This code path does not exist in ESM-2
      __lowercase								=		"rotary"
      __lowercase								=		True
      __lowercase								=		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
      __lowercase								=		esm.args.embed_dim
      __lowercase								=		esm.args.layers
      __lowercase								=		esm.args.attention_heads
      __lowercase								=		esm.args.ffn_embed_dim
      __lowercase								=		esm.args.token_dropout
      __lowercase								=		True if esm.emb_layer_norm_before else False
      __lowercase								=		"absolute"
      __lowercase								=		False
      __lowercase								=		None
   else:
      # Indicates an ESM-2 model
      __lowercase								=		esm.embed_dim
      __lowercase								=		esm.num_layers
      __lowercase								=		esm.attention_heads
      __lowercase								=		4 * embed_dim  # This is hardcoded in ESM-2
      __lowercase								=		esm.token_dropout
      __lowercase								=		False  # This code path does not exist in ESM-2
      __lowercase								=		"rotary"
      __lowercase								=		False
      __lowercase								=		None

   if is_folding_model:
      __lowercase								=		esm.esm.alphabet
   __lowercase								=		tuple(alphabet.all_toks)
   __lowercase								=		alphabet.mask_idx
   __lowercase								=		alphabet.padding_idx

   if is_folding_model:
      __lowercase								=		esm.esm
   else:
      __lowercase								=		esm

   __lowercase								=		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=1026,     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:
      __lowercase								=		esm.classification_heads["mnli"].out_proj.weight.shape[0]
   print("Our ESM config:",     UpperCamelCase_)

   if model.startswith("esmfold"):
      __lowercase								=		EsmForProteinFolding
   elif classification_head:
      __lowercase								=		EsmForSequenceClassification
   else:
      __lowercase								=		EsmForMaskedLM
   __lowercase								=		model_class(UpperCamelCase_)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   __lowercase								=		original_esm_model.embed_tokens.weight
   if position_embedding_type == "absolute":
      __lowercase								=		original_esm_model.embed_positions.weight

   if config.emb_layer_norm_before:
      __lowercase								=		original_esm_model.emb_layer_norm_before.weight
      __lowercase								=		original_esm_model.emb_layer_norm_before.bias

   __lowercase								=		original_esm_model.emb_layer_norm_after.weight
   __lowercase								=		original_esm_model.emb_layer_norm_after.bias

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

      # self attention
      __lowercase								=		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))
      )

      __lowercase								=		esm_layer.self_attn.q_proj.weight
      __lowercase								=		esm_layer.self_attn.q_proj.bias
      __lowercase								=		esm_layer.self_attn.k_proj.weight
      __lowercase								=		esm_layer.self_attn.k_proj.bias
      __lowercase								=		esm_layer.self_attn.v_proj.weight
      __lowercase								=		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.
         __lowercase								=		esm_layer.self_attn.rot_emb.inv_freq

      # LayerNorm changes for pre-activation
      __lowercase								=		esm_layer.self_attn_layer_norm.weight
      __lowercase								=		esm_layer.self_attn_layer_norm.bias
      __lowercase								=		esm_layer.final_layer_norm.weight
      __lowercase								=		esm_layer.final_layer_norm.bias

      # self-attention output
      __lowercase								=		layer.attention.output
      assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
      __lowercase								=		esm_layer.self_attn.out_proj.weight
      __lowercase								=		esm_layer.self_attn.out_proj.bias

      # intermediate
      __lowercase								=		layer.intermediate
      assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
      __lowercase								=		esm_layer.fca.weight
      __lowercase								=		esm_layer.fca.bias

      # output
      __lowercase								=		layer.output
      assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
      __lowercase								=		esm_layer.fca.weight
      __lowercase								=		esm_layer.fca.bias
      # end of layer

   if is_folding_model:
      __lowercase								=		esm.esm_s_combine.data
      __lowercase								=		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:
      __lowercase								=		esm.esm.classification_heads["mnli"].dense.weight
      __lowercase								=		esm.classification_heads["mnli"].dense.bias
      __lowercase								=		esm.classification_heads["mnli"].out_proj.weight
      __lowercase								=		esm.classification_heads["mnli"].out_proj.bias
   else:
      # LM Head
      __lowercase								=		esm.lm_head.dense.weight
      __lowercase								=		esm.lm_head.dense.bias
      __lowercase								=		esm.lm_head.layer_norm.weight
      __lowercase								=		esm.lm_head.layer_norm.bias
      __lowercase								=		esm.lm_head.weight
      __lowercase								=		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.
      __lowercase								=		SAMPLE_DATA[:2]
   else:
      __lowercase								=		SAMPLE_DATA

   if is_folding_model:
      __lowercase								=		get_esmfold_tokenizer()
      __lowercase								=		hf_tokenizer(
          [row[1] for row in sample_data],     return_tensors="pt",     padding=UpperCamelCase_,     add_special_tokens=UpperCamelCase_)
      __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase								=		esmfold_encode_sequences([row[1] for row in sample_data])
      __lowercase								=		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.
      __lowercase								=		alphabet.get_batch_converter()
      __lowercase						,__lowercase						,__lowercase								=		batch_converter(UpperCamelCase_)
      # Prepare tokenizer and make sure it matches
      with TemporaryDirectory() as tempdir:
         __lowercase								=		"\n".join(alphabet.all_toks)
         __lowercase								=		Path(UpperCamelCase_) / "vocab.txt"
         vocab_file.write_text(UpperCamelCase_)
         __lowercase								=		EsmTokenizer(vocab_file=str(UpperCamelCase_))

      __lowercase								=		hf_tokenizer([row[1] for row in sample_data],     return_tensors="pt",     padding=UpperCamelCase_)
      __lowercase								=		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.
         __lowercase								=		esm.cuda().infer([row[1] for row in sample_data])
         __lowercase								=		model.cuda()(
             input_ids=hf_tokens["input_ids"].cuda(),     attention_mask=hf_tokens["attention_mask"].cuda())
      else:
         __lowercase								=		model(**UpperCamelCase_,     output_hidden_states=UpperCamelCase_)
         __lowercase								=		our_output["logits"]
         if classification_head:
            __lowercase								=		esm.model.classification_heads["mnli"](esm.extract_features(UpperCamelCase_))
         else:
            __lowercase								=		esm(hf_tokens["input_ids"],     repr_layers=list(range(999)))
            __lowercase								=		their_output["logits"]

      if is_folding_model:
         __lowercase								=		torch.max(torch.abs(our_output["positions"] - their_output["positions"])).item()
         __lowercase								=		torch.allclose(our_output["positions"],     their_output["positions"],     atol=1E-5)
      else:
         __lowercase								=		torch.max(torch.abs(our_output - their_output)).item()
         __lowercase								=		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
         __lowercase								=		model.predict_contacts(hf_tokens["input_ids"],     hf_tokens["attention_mask"])
         __lowercase								=		esm.predict_contacts(hf_tokens["input_ids"])
         __lowercase								=		torch.max(torch.abs(our_output - their_output)).item()
         __lowercase								=		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__":
   _a					 =				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.')
   _a					 =				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
   )







"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

_a					 =				'\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

_a					 =				'\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'




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







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    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    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		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"      )
    __lowercase								=		report.pop("micro avg"      )

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

    return scores




