

"""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()
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    lowerCAmelCase__							(		)     -> Optional[Any]:




				'''simple docstring'''





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



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any] )     -> Tuple:




				'''simple docstring'''





				A__            =     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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							bool		,    SCREAMING_SNAKE_CASE_:							str		,    SCREAMING_SNAKE_CASE_:							str )     -> Dict:




				'''simple docstring'''





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

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

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

				if is_folding_model:
								A__            =     esm.esm
				else:
								A__            =     esm

				A__            =     EsmConfig(
				    vocab_size=original_esm_model.embed_tokens.num_embeddings		,    mask_token_id=SCREAMING_SNAKE_CASE_		,    hidden_size=SCREAMING_SNAKE_CASE_		,    num_hidden_layers=SCREAMING_SNAKE_CASE_		,    num_attention_heads=SCREAMING_SNAKE_CASE_		,    intermediate_size=SCREAMING_SNAKE_CASE_		,    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=SCREAMING_SNAKE_CASE_		,    emb_layer_norm_before=SCREAMING_SNAKE_CASE_		,    token_dropout=SCREAMING_SNAKE_CASE_		,    position_embedding_type=SCREAMING_SNAKE_CASE_		,    is_folding_model=SCREAMING_SNAKE_CASE_		,    esmfold_config=SCREAMING_SNAKE_CASE_		,    vocab_list=SCREAMING_SNAKE_CASE_		,    )
				if classification_head:
								A__            =     esm.classification_heads["mnli"].out_proj.weight.shape[0]
				print("Our ESM config:"		,    SCREAMING_SNAKE_CASE_ )

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

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

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

				A__            =     original_esm_model.emb_layer_norm_after.weight
				A__            =     original_esm_model.emb_layer_norm_after.bias

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

								# self attention
								A__            =     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__            =     esm_layer.self_attn.q_proj.weight
								A__            =     esm_layer.self_attn.q_proj.bias
								A__            =     esm_layer.self_attn.k_proj.weight
								A__            =     esm_layer.self_attn.k_proj.bias
								A__            =     esm_layer.self_attn.v_proj.weight
								A__            =     esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn		,    "rot_emb"		,    SCREAMING_SNAKE_CASE_ ) 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__            =     esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

								A__            =     hf_tokenizer([row[1] for row in sample_data]		,    return_tensors="pt"		,    padding=SCREAMING_SNAKE_CASE_ )
								A__            =     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__            =     esm.cuda().infer([row[1] for row in sample_data] )
												A__            =     model.cuda()(
												    input_ids=hf_tokens["input_ids"].cuda()		,    attention_mask=hf_tokens["attention_mask"].cuda() )
								else:
												A__            =     model(**SCREAMING_SNAKE_CASE_		,    output_hidden_states=SCREAMING_SNAKE_CASE_ )
												A__            =     our_output["logits"]
												if classification_head:
																A__            =     esm.model.classification_heads["mnli"](esm.extract_features(SCREAMING_SNAKE_CASE_ ) )
												else:
																A__            =     esm(hf_tokens["input_ids"]		,    repr_layers=list(range(9_9_9 ) ) )
																A__            =     their_output["logits"]

								if is_folding_model:
												A__            =     torch.max(torch.abs(our_output["positions"] - their_output["positions"] ) ).item()
												A__            =     torch.allclose(our_output["positions"]		,    their_output["positions"]		,    atol=1e-5 )
								else:
												A__            =     torch.max(torch.abs(our_output - their_output ) ).item()
												A__            =     torch.allclose(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    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__            =     model.predict_contacts(hf_tokens["input_ids"]		,    hf_tokens["attention_mask"] )
												A__            =     esm.predict_contacts(hf_tokens["input_ids"] )
												A__            =     torch.max(torch.abs(our_output - their_output ) ).item()
												A__            =     torch.allclose(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    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(SCREAMING_SNAKE_CASE_ ).mkdir(parents=SCREAMING_SNAKE_CASE_		,    exist_ok=SCREAMING_SNAKE_CASE_ )
								print(F'Saving model to {pytorch_dump_folder_path}' )
								model.save_pretrained(SCREAMING_SNAKE_CASE_ )

								del esm  # Free up some memory before continuing

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

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


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 unittest

from cer import CER


lowerCAmelCase__						=	CER()

class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["White House"]
							A__            =     ["white house"]
							# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.18_1818_1818    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["were wolf"]
							A__            =     ["werewolf"]
							# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.111_1111    ) < 1e-6    )

							A__            =     ["werewolf"]
							A__            =     ["weae     wolf"]
							# S = 1, D = 1, I = 0, N = 8, CER = 0.25
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

							# consecutive whitespaces case 1
							A__            =     ["were wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

							# consecutive whitespaces case 2
							A__            =     ["were   wolf"]
							A__            =     ["were               wolf"]
							# S = 0, D = 0, I = 0, N = 9, CER = 0
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["weaewolf"]
							# S = 1, D = 0, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereawolf"]
							# S = 0, D = 1, I = 0, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     ["wereolf"]
							# S = 0, D = 0, I = 1, N = 8, CER = 0.125
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["werewolf"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__            =     ["werewolf", "I am your father"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertEqual(lowercase , 0.0    )

							A__            =     ["werewolf", "I am your father", "doge"]
							A__            =     ["werxwolf", "I       am your father", "doge"]
							# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.0357_1428    ) < 1e-6    )


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     ["My hovercraft", "is full of eels"]
							A__            =     ["My hovercraft is full", " of eels"]
							# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
							# whitespace at the front of " of eels" will be strip during preporcessing
							# so need to insert 2 whitespaces
							A__            =     cer.compute(predictions=lowercase , references=lowercase , concatenate_texts=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.07_1428    ) < 1e-6    )


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






							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     [" 能吞虾玻璃而 不霜身体啦"]
							# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.45_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃", "而不伤身体"]
							A__            =     ["我    能 吞 下 玻 璃", "而不伤身体"]
							# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertTrue(abs(char_error_rate - 0.4545_4545_4545    ) < 1e-6    )

							A__            =     ["我能吞下玻璃而不伤身体"]
							A__            =     cer.compute(predictions=lowercase , references=lowercase    )
							self.assertFalse(lowercase , 0.0    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     [""]
							A__            =     ["Hypothesis"]
							with self.assertRaises(lowercase    ):
											cer.compute(predictions=lowercase , references=lowercase    )


if __name__ == "__main__":
	unittest.main()

