



"""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()
UpperCAmelCase:  Tuple			     =				logging.get_logger(__name__)

UpperCAmelCase:  str			     =				[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

UpperCAmelCase:  Union[str, Any]			     =				{
    """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,
}

UpperCAmelCase:  Tuple			     =				list("""ARNDCQEGHILKMFPSTWYV""")

UpperCAmelCase:  List[str]			     =				restypes + ["""X"""]
UpperCAmelCase:  List[Any]			     =				restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]


def  __SCREAMING_SNAKE_CASE (  ):
		with TemporaryDirectory() as tempdir:
				_lowercase				: List[str]     =   """\n""".join(__UpperCAmelCase      )
				_lowercase				: Any     =   Path(__UpperCAmelCase      ) / """vocab.txt"""
				vocab_file.write_text(__UpperCAmelCase      )
				_lowercase				: Dict     =   EsmTokenizer(vocab_file=str(__UpperCAmelCase      )      )
		_lowercase				: Optional[Any]     =   0  # Overlaps with 'A' but that seems to be what they want
		return hf_tokenizer


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Any     =   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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		if model.startswith("""esmfold"""      ):
				_lowercase				: str     =   MODEL_MAPPING[model]()
		else:
				_lowercase     ,  _lowercase				: int     =   MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

		if model.startswith("""esmfold"""      ):
				_lowercase				: Dict     =   esm.esm.embed_dim
				_lowercase				: Any     =   esm.esm.num_layers
				_lowercase				: Optional[int]     =   esm.esm.attention_heads
				_lowercase				: List[str]     =   4 * embed_dim
				_lowercase				: Union[str, Any]     =   esm.esm.token_dropout
				_lowercase				: Optional[Any]     =   False  # This code path does not exist in ESM-2
				_lowercase				: Optional[int]     =   """rotary"""
				_lowercase				: Dict     =   True
				_lowercase				: Optional[int]     =   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				: Optional[int]     =   esm.args.embed_dim
				_lowercase				: Union[str, Any]     =   esm.args.layers
				_lowercase				: Optional[Any]     =   esm.args.attention_heads
				_lowercase				: Optional[int]     =   esm.args.ffn_embed_dim
				_lowercase				: str     =   esm.args.token_dropout
				_lowercase				: Dict     =   True if esm.emb_layer_norm_before else False
				_lowercase				: List[Any]     =   """absolute"""
				_lowercase				: Optional[int]     =   False
				_lowercase				: Union[str, Any]     =   None
		else:
				# Indicates an ESM-2 model
				_lowercase				: int     =   esm.embed_dim
				_lowercase				: Any     =   esm.num_layers
				_lowercase				: Tuple     =   esm.attention_heads
				_lowercase				: List[Any]     =   4 * embed_dim  # This is hardcoded in ESM-2
				_lowercase				: List[str]     =   esm.token_dropout
				_lowercase				: str     =   False  # This code path does not exist in ESM-2
				_lowercase				: Optional[int]     =   """rotary"""
				_lowercase				: Tuple     =   False
				_lowercase				: List[Any]     =   None

		if is_folding_model:
				_lowercase				: List[Any]     =   esm.esm.alphabet
		_lowercase				: List[Any]     =   tuple(alphabet.all_toks      )
		_lowercase				: Union[str, Any]     =   alphabet.mask_idx
		_lowercase				: Optional[int]     =   alphabet.padding_idx

		if is_folding_model:
				_lowercase				: List[Any]     =   esm.esm
		else:
				_lowercase				: Optional[Any]     =   esm

		_lowercase				: List[Any]     =   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				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
		print("""Our ESM config:"""   ,				__UpperCAmelCase      )

		if model.startswith("""esmfold"""      ):
				_lowercase				: Union[str, Any]     =   EsmForProteinFolding
		elif classification_head:
				_lowercase				: Optional[Any]     =   EsmForSequenceClassification
		else:
				_lowercase				: str     =   EsmForMaskedLM
		_lowercase				: List[str]     =   model_class(__UpperCAmelCase      )
		model.eval()

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

		if config.emb_layer_norm_before:
				_lowercase				: Optional[int]     =   original_esm_model.emb_layer_norm_before.weight
				_lowercase				: int     =   original_esm_model.emb_layer_norm_before.bias

		_lowercase				: List[Any]     =   original_esm_model.emb_layer_norm_after.weight
		_lowercase				: Optional[Any]     =   original_esm_model.emb_layer_norm_after.bias

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

				# self attention
				_lowercase				: 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)      )
				)

				_lowercase				: Any     =   esm_layer.self_attn.q_proj.weight
				_lowercase				: Tuple     =   esm_layer.self_attn.q_proj.bias
				_lowercase				: Tuple     =   esm_layer.self_attn.k_proj.weight
				_lowercase				: Dict     =   esm_layer.self_attn.k_proj.bias
				_lowercase				: Any     =   esm_layer.self_attn.v_proj.weight
				_lowercase				: Optional[int]     =   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				: List[Any]     =   esm_layer.self_attn.rot_emb.inv_freq

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

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

				# intermediate
				_lowercase				: EsmIntermediate     =   layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				_lowercase				: Optional[int]     =   esm_layer.fca.weight
				_lowercase				: str     =   esm_layer.fca.bias

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

		if is_folding_model:
				_lowercase				: List[str]     =   esm.esm_s_combine.data
				_lowercase				: Union[str, Any]     =   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				: Union[str, Any]     =   esm.esm.classification_heads["""mnli"""].dense.weight
				_lowercase				: Dict     =   esm.classification_heads["""mnli"""].dense.bias
				_lowercase				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.weight
				_lowercase				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.bias
		else:
				# LM Head
				_lowercase				: Any     =   esm.lm_head.dense.weight
				_lowercase				: Union[str, Any]     =   esm.lm_head.dense.bias
				_lowercase				: int     =   esm.lm_head.layer_norm.weight
				_lowercase				: str     =   esm.lm_head.layer_norm.bias
				_lowercase				: Dict     =   esm.lm_head.weight
				_lowercase				: Dict     =   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				: Any     =   SAMPLE_DATA[:2]
		else:
				_lowercase				: Tuple     =   SAMPLE_DATA

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

				_lowercase				: Optional[Any]     =   hf_tokenizer([row[1] for row in sample_data]   ,				return_tensors="""pt"""   ,				padding=__UpperCAmelCase      )
				_lowercase				: Union[str, 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.
						_lowercase				: List[str]     =   esm.cuda().infer([row[1] for row in sample_data]      )
						_lowercase				: Tuple     =   model.cuda()(
						    input_ids=hf_tokens["""input_ids"""].cuda()   ,				attention_mask=hf_tokens["""attention_mask"""].cuda()      )
				else:
						_lowercase				: Union[str, Any]     =   model(**__UpperCAmelCase   ,				output_hidden_states=__UpperCAmelCase      )
						_lowercase				: Tuple     =   our_output["""logits"""]
						if classification_head:
								_lowercase				: int     =   esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCAmelCase      )      )
						else:
								_lowercase				: List[str]     =   esm(hf_tokens["""input_ids"""]   ,				repr_layers=list(range(999      )      )      )
								_lowercase				: str     =   their_output["""logits"""]

				if is_folding_model:
						_lowercase				: str     =   torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]      )      ).item()
						_lowercase				: str     =   torch.allclose(our_output["""positions"""]   ,				their_output["""positions"""]   ,				atol=1E-5      )
				else:
						_lowercase				: int     =   torch.max(torch.abs(our_output - their_output      )      ).item()
						_lowercase				: Tuple     =   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				: Tuple     =   model.predict_contacts(hf_tokens["""input_ids"""]   ,				hf_tokens["""attention_mask"""]      )
						_lowercase				: List[str]     =   esm.predict_contacts(hf_tokens["""input_ids"""]      )
						_lowercase				: Union[str, Any]     =   torch.max(torch.abs(our_output - their_output      )      ).item()
						_lowercase				: Dict     =   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__":
		UpperCAmelCase:  Any			     =				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.""")
		UpperCAmelCase:  Tuple			     =				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
		)








from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__A       ="\\n"

__A       ="\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n"

__A       ="\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

__A       =["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__A       =""

try:
				from jiwer import transforms as tr

				__A       =True
except ImportError:
				__A       =False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):

				class 						UpperCAmelCase__     (     tr.AbstractTransform  ):




							'''simple docstring'''






							def __init__(  self				:					Tuple			,			a_				:					str = " "					):
									'''simple docstring'''





									__UpperCAmelCase       :	str						=      sentence_delimiter




							def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					str					):
									'''simple docstring'''





									return list(a_					)




							def 					snake_case__	(  self				:					List[str]			,			a_				:					List[str]					):
									'''simple docstring'''





									__UpperCAmelCase       :	Optional[Any]						=      []
									for sent_idx, sentence in enumerate(a_					):
											chars.extend(self.process_string(a_					)					)
											if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(a_					) - 1:
													chars.append(self.sentence_delimiter					)
									return chars
				__A       =tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				__A       =tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				__A       =None

def 			a		( _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		return float((preds == labels).mean()		)

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





		return {
		    "f1": float(fa_score(y_true=_UpperCAmelCase					,  y_pred=_UpperCAmelCase					,  average='''macro'''		)		),
		    "accuracy": simple_accuracy(_UpperCAmelCase					,  _UpperCAmelCase		),
		}

def 			a		( _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   int="exp"					,  _UpperCAmelCase				:   Optional[int]=None					,  _UpperCAmelCase				:   Dict=False					,  _UpperCAmelCase				:   List[Any]=False					,  _UpperCAmelCase				:   Optional[Any]=None					,  _UpperCAmelCase				:   str=False					,  ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      [[label] for label in labels]
		__UpperCAmelCase       :	Any						=      list(_UpperCAmelCase		)
		try:
				import sacrebleu as scb
		except ImportError:
				raise ValueError(
				    '''sacrebleu has to be installed in order to apply the bleu metric for covost2.'''
				    '''You can install it via `pip install sacrebleu`.'''		)

		if version.parse(scb.__version__		) < version.parse('''1.4.12'''		):
				raise ImportWarning(
				    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
				    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''		)

		__UpperCAmelCase       :	Dict						=      len(labels[0]		)
		if any(len(_UpperCAmelCase		) != references_per_prediction for refs in labels		):
				raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
		__UpperCAmelCase       :	Union[str, Any]						=      [[refs[i] for refs in labels] for i in range(_UpperCAmelCase		)]
		__UpperCAmelCase       :	Tuple						=      scb.corpus_bleu(
		    _UpperCAmelCase					,  _UpperCAmelCase					,  smooth_method=_UpperCAmelCase					,  smooth_value=_UpperCAmelCase					,  force=_UpperCAmelCase					,  lowercase=_UpperCAmelCase					,  use_effective_order=_UpperCAmelCase					,  **({'''tokenize''': tokenize} if tokenize else {})					,  )
		return {"bleu": output.score}

def 			a		( _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		try:
				from jiwer import compute_measures
		except ImportError:
				raise ValueError(
				    f'jiwer has to be installed in order to apply the wer metric for {config_name}.'
				    '''You can install it via `pip install jiwer`.'''		)

		if concatenate_texts:
				__UpperCAmelCase       :	int						=      compute_measures(_UpperCAmelCase					,  _UpperCAmelCase		)['''wer''']

				__UpperCAmelCase       :	List[Any]						=      compute_measures(_UpperCAmelCase					,  _UpperCAmelCase					,  truth_transform=_UpperCAmelCase					,  hypothesis_transform=_UpperCAmelCase		)['''wer''']
				return {"wer": wer, "cer": cer}
		else:

				def compute_score(_UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Tuple="wer"		):
						__UpperCAmelCase       :	List[Any]						=      0
						__UpperCAmelCase       :	Tuple						=      0
						for prediction, reference in zip(_UpperCAmelCase					,  _UpperCAmelCase		):
								if score_type == "wer":
										__UpperCAmelCase       :	Optional[int]						=      compute_measures(_UpperCAmelCase					,  _UpperCAmelCase		)
								elif score_type == "cer":
										__UpperCAmelCase       :	Any						=      compute_measures(
										    _UpperCAmelCase					,  _UpperCAmelCase					,  truth_transform=_UpperCAmelCase					,  hypothesis_transform=_UpperCAmelCase		)
								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
								total += measures["substitutions"] + measures["deletions"] + measures["hits"]
						return incorrect / total

				return {"wer": compute_score(_UpperCAmelCase					,  _UpperCAmelCase					,  '''wer'''		), "cer": compute_score(_UpperCAmelCase					,  _UpperCAmelCase					,  '''cer'''		)}

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




			'''simple docstring'''






			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					if self.config_name not in _CONFIG_NAMES:
							raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'					)

					__UpperCAmelCase       :	Optional[int]						=      '''int64''' if self.config_name in ['''fleurs-lang_id''', '''minds14'''] else '''string'''

					return datasets.MetricInfo(
					    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
					        {'''predictions''': datasets.Value(a_					), '''references''': datasets.Value(a_					)}					)			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy'''			,			)




			def 					snake_case__	(  self				:					Any			,			a_				:					Optional[int]			,			a_				:					str			,			a_				:					str=None			,			a_				:					str=None					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      bleu_kwargs if bleu_kwargs is not None else {}
					__UpperCAmelCase       :	Any						=      wer_kwargs if wer_kwargs is not None else {}

					if self.config_name == "fleurs-lang_id":
							return {"accuracy": simple_accuracy(a_			,			a_					)}
					elif self.config_name == "minds14":
							return fa_and_simple_accuracy(a_			,			a_					)
					elif self.config_name == "covost2":
							__UpperCAmelCase       :	str						=      bleu_kwargs.pop('''smooth_method'''			,			'''exp'''					)
							__UpperCAmelCase       :	Optional[int]						=      bleu_kwargs.pop('''smooth_value'''			,			a_					)
							__UpperCAmelCase       :	Any						=      bleu_kwargs.pop('''force'''			,			a_					)
							__UpperCAmelCase       :	Any						=      bleu_kwargs.pop('''lowercase'''			,			a_					)
							__UpperCAmelCase       :	Any						=      bleu_kwargs.pop('''tokenize'''			,			a_					)
							__UpperCAmelCase       :	Tuple						=      bleu_kwargs.pop('''use_effective_order'''			,			a_					)
							return bleu(
							    preds=a_			,			labels=a_			,			smooth_method=a_			,			smooth_value=a_			,			force=a_			,			lowercase=a_			,			tokenize=a_			,			use_effective_order=a_			,			)
					elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
							__UpperCAmelCase       :	Union[str, Any]						=      wer_kwargs.pop('''concatenate_texts'''			,			a_					)
							return wer_and_cer(a_			,			a_			,			a_			,			self.config_name					)
					else:
							raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'					)





