


"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__magic_name__						: Dict    =				logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def       a_ (	lowercase__ :Union[str, Any]     ):
			if "token" in model_name_or_path:
						return "rag_token"
			if "sequence" in model_name_or_path:
						return "rag_sequence"
			if "bart" in model_name_or_path:
						return "bart"
			return None





def       a_ (	lowercase__ :Dict,			lowercase__ :Optional[Any],			lowercase__ :str     ):
			return max(metric_fn(lowercase__,			lowercase__     ) for gt in ground_truths     )





def       a_ (	lowercase__ :Optional[int],			lowercase__ :List[str],			lowercase__ :Dict     ):
			__lowerCamelCase          =      [line.strip() for line in open(lowercase__,			"""r"""     ).readlines()]
			__lowerCamelCase          =      []

			if args.gold_data_mode == "qa":
						__lowerCamelCase          =      pd.read_csv(lowercase__,			sep="""\t""",			header=lowercase__     )
						for answer_list in data[1]:
									__lowerCamelCase          =      ast.literal_eval(lowercase__     )
									answers.append(lowercase__     )
			else:
						__lowerCamelCase          =      [line.strip() for line in open(lowercase__,			"""r"""     ).readlines()]
						__lowerCamelCase          =      [[reference] for reference in references]

			__lowerCamelCase          =      __lowerCamelCase          =      __lowerCamelCase          =      0
			for prediction, ground_truths in zip(lowercase__,			lowercase__     ):
						total += 1
						em += metric_max_over_ground_truths(lowercase__,			lowercase__,			lowercase__     )
						fa += metric_max_over_ground_truths(lowercase__,			lowercase__,			lowercase__     )

			__lowerCamelCase          =      100.0 * em / total
			__lowerCamelCase          =      100.0 * fa / total

			logger.info(f'F1: {fa:.2f}'     )
			logger.info(f'EM: {em:.2f}'     )





def       a_ (	lowercase__ :List[str],			lowercase__ :Optional[int],			lowercase__ :str     ):
			__lowerCamelCase          =      args.k
			__lowerCamelCase          =      [line.strip() for line in open(lowercase__,			"""r"""     ).readlines()]
			__lowerCamelCase          =      [line.strip() for line in open(lowercase__,			"""r"""     ).readlines()]

			__lowerCamelCase          =      __lowerCamelCase          =      0
			for hypo, reference in zip(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      set(hypo.split("""\t"""     )[:k]     )
						__lowerCamelCase          =      set(reference.split("""\t"""     )     )
						total += 1
						em += len(hypo_provenance & ref_provenance     ) / k

			__lowerCamelCase          =      100.0 * em / total
			logger.info(f'Precision@{k}: {em: .2f}'     )





def       a_ (	lowercase__ :Optional[int],			lowercase__ :Optional[int],			lowercase__ :Optional[int]     ):
			def strip_title(lowercase__ :int     ):
						if title.startswith("""\""""     ):
									__lowerCamelCase          =      title[1:]
						if title.endswith("""\""""     ):
									__lowerCamelCase          =      title[:-1]
						return title

			__lowerCamelCase          =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    lowercase__,			return_tensors="""pt""",			padding=lowercase__,			truncation=lowercase__,			)["""input_ids"""].to(args.device     )

			__lowerCamelCase          =      rag_model.rag.question_encoder(lowercase__     )
			__lowerCamelCase          =      question_enc_outputs[0]

			__lowerCamelCase          =      rag_model.retriever(
			    lowercase__,			question_enc_pool_output.cpu().detach().to(torch.floataa     ).numpy(),			prefix=rag_model.rag.generator.config.prefix,			n_docs=rag_model.config.n_docs,			return_tensors="""pt""",			)
			__lowerCamelCase          =      rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
			__lowerCamelCase          =      []
			for docs in all_docs:
						__lowerCamelCase          =      [strip_title(lowercase__     ) for title in docs["""title"""]]
						provenance_strings.append("""\t""".join(lowercase__     )     )
			return provenance_strings





def       a_ (	lowercase__ :str,			lowercase__ :Optional[Any],			lowercase__ :str     ):
			with torch.no_grad():
						__lowerCamelCase          =      rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowercase__,			return_tensors="""pt""",			padding=lowercase__,			truncation=lowercase__     )

						__lowerCamelCase          =      inputs_dict.input_ids.to(args.device     )
						__lowerCamelCase          =      inputs_dict.attention_mask.to(args.device     )
						__lowerCamelCase          =      rag_model.generate(  # rag_model overwrites generate
						    lowercase__,			attention_mask=lowercase__,			num_beams=args.num_beams,			min_length=args.min_length,			max_length=args.max_length,			early_stopping=lowercase__,			num_return_sequences=1,			bad_words_ids=[[0, 0]],			)
						__lowerCamelCase          =      rag_model.retriever.generator_tokenizer.batch_decode(lowercase__,			skip_special_tokens=lowercase__     )

						if args.print_predictions:
									for q, a in zip(lowercase__,			lowercase__     ):
												logger.info("""Q: {} - A: {}""".format(lowercase__,			lowercase__     )     )

						return answers





def       a_ (	):
			__lowerCamelCase          =      argparse.ArgumentParser()
			parser.add_argument(
			    """--model_type""",			choices=["""rag_sequence""", """rag_token""", """bart"""],			type=lowercase__,			help=(
			        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
			        """ model_name_or_path"""
			    ),			)
			parser.add_argument(
			    """--index_name""",			default=lowercase__,			choices=["""exact""", """compressed""", """legacy"""],			type=lowercase__,			help="""RAG model retriever type""",			)
			parser.add_argument(
			    """--index_path""",			default=lowercase__,			type=lowercase__,			help="""Path to the retrieval index""",			)
			parser.add_argument("""--n_docs""",			default=5,			type=lowercase__,			help="""Number of retrieved docs"""     )
			parser.add_argument(
			    """--model_name_or_path""",			default=lowercase__,			type=lowercase__,			required=lowercase__,			help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""",			)
			parser.add_argument(
			    """--eval_mode""",			choices=["""e2e""", """retrieval"""],			default="""e2e""",			type=lowercase__,			help=(
			        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
			        """ precision@k."""
			    ),			)
			parser.add_argument("""--k""",			default=1,			type=lowercase__,			help="""k for the precision@k calculation"""     )
			parser.add_argument(
			    """--evaluation_set""",			default=lowercase__,			type=lowercase__,			required=lowercase__,			help="""Path to a file containing evaluation samples""",			)
			parser.add_argument(
			    """--gold_data_path""",			default=lowercase__,			type=lowercase__,			required=lowercase__,			help="""Path to a tab-separated file with gold samples""",			)
			parser.add_argument(
			    """--gold_data_mode""",			default="""qa""",			type=lowercase__,			choices=["""qa""", """ans"""],			help=(
			        """Format of the gold data file"""
			        """qa - a single line in the following format: question [tab] answer_list"""
			        """ans - a single line of the gold file contains the expected answer string"""
			    ),			)
			parser.add_argument(
			    """--predictions_path""",			type=lowercase__,			default="""predictions.txt""",			help="""Name of the predictions file, to be stored in the checkpoints directory""",			)
			parser.add_argument(
			    """--eval_all_checkpoints""",			action="""store_true""",			help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""",			)
			parser.add_argument(
			    """--eval_batch_size""",			default=8,			type=lowercase__,			help="""Batch size per GPU/CPU for evaluation.""",			)
			parser.add_argument(
			    """--recalculate""",			help="""Recalculate predictions even if the prediction file exists""",			action="""store_true""",			)
			parser.add_argument(
			    """--num_beams""",			default=4,			type=lowercase__,			help="""Number of beams to be used when generating answers""",			)
			parser.add_argument("""--min_length""",			default=1,			type=lowercase__,			help="""Min length of the generated answers"""     )
			parser.add_argument("""--max_length""",			default=50,			type=lowercase__,			help="""Max length of the generated answers"""     )

			parser.add_argument(
			    """--print_predictions""",			action="""store_true""",			help="""If True, prints predictions while evaluating.""",			)
			parser.add_argument(
			    """--print_docs""",			action="""store_true""",			help="""If True, prints docs retried while generating.""",			)
			__lowerCamelCase          =      parser.parse_args()
			__lowerCamelCase          =      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )
			return args





def       a_ (	lowercase__ :Any     ):
			__lowerCamelCase          =      {}
			if args.model_type is None:
						__lowerCamelCase          =      infer_model_type(args.model_name_or_path     )
						assert args.model_type is not None
			if args.model_type.startswith("""rag"""     ):
						__lowerCamelCase          =      RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
						__lowerCamelCase          =      args.n_docs
						if args.index_name is not None:
									__lowerCamelCase          =      args.index_name
						if args.index_path is not None:
									__lowerCamelCase          =      args.index_path
			else:
						__lowerCamelCase          =      BartForConditionalGeneration

			__lowerCamelCase          =      (
			    [f.path for f in os.scandir(args.model_name_or_path     ) if f.is_dir()]
			    if args.eval_all_checkpoints
			    else [args.model_name_or_path]
			)

			logger.info("""Evaluate the following checkpoints: %s""",			lowercase__     )

			__lowerCamelCase          =      get_scores if args.eval_mode == """e2e""" else get_precision_at_k
			__lowerCamelCase          =      evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

			for checkpoint in checkpoints:
						if os.path.exists(args.predictions_path     ) and (not args.recalculate):
									logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path     )     )
									score_fn(lowercase__,			args.predictions_path,			args.gold_data_path     )
									continue

						logger.info("""***** Running evaluation for {} *****""".format(lowercase__     )     )
						logger.info("""  Batch size = %d""",			args.eval_batch_size     )
						logger.info("""  Predictions will be stored under {}""".format(args.predictions_path     )     )

						if args.model_type.startswith("""rag"""     ):
									__lowerCamelCase          =      RagRetriever.from_pretrained(lowercase__,			**lowercase__     )
									__lowerCamelCase          =      model_class.from_pretrained(lowercase__,			retriever=lowercase__,			**lowercase__     )
									model.retriever.init_retrieval()
						else:
									__lowerCamelCase          =      model_class.from_pretrained(lowercase__,			**lowercase__     )
						model.to(args.device     )

						with open(args.evaluation_set,			"""r"""     ) as eval_file, open(args.predictions_path,			"""w"""     ) as preds_file:
									__lowerCamelCase          =      []
									for line in tqdm(lowercase__     ):
												questions.append(line.strip()     )
												if len(lowercase__     ) == args.eval_batch_size:
															__lowerCamelCase          =      evaluate_batch_fn(lowercase__,			lowercase__,			lowercase__     )
															preds_file.write("""\n""".join(lowercase__     ) + """\n"""     )
															preds_file.flush()
															__lowerCamelCase          =      []
									if len(lowercase__     ) > 0:
												__lowerCamelCase          =      evaluate_batch_fn(lowercase__,			lowercase__,			lowercase__     )
												preds_file.write("""\n""".join(lowercase__     )     )
												preds_file.flush()

									score_fn(lowercase__,			args.predictions_path,			args.gold_data_path     )


if __name__ == "__main__":
					__magic_name__						: Dict    =				get_args()
					main(args)








"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)




class 			__snake_case   :




							def __init__(     self:    Any     ,							A_:    str     ,							A_:    str=13     ,							A_:    Dict=7     ,							A_:    Optional[int]=True     ,							A_:    int=True     ,							A_:    List[str]=True     ,							A_:    Any=True     ,							A_:    List[Any]=99     ,							A_:    Tuple=32     ,							A_:    Union[str, Any]=2     ,							A_:    Dict=4     ,							A_:    Tuple=37     ,							A_:    Dict="gelu"     ,							A_:    List[Any]=0.1     ,							A_:    int=0.1     ,							A_:    Union[str, Any]=5_12     ,							A_:    List[Any]=16     ,							A_:    Optional[Any]=2     ,							A_:    int=0.02     ,							A_:    List[str]=3     ,							A_:    Optional[int]=4     ,							A_:    List[str]=None     ,							):
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      13
										__lowerCamelCase          =      7
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      True
										__lowerCamelCase          =      99
										__lowerCamelCase          =      32
										__lowerCamelCase          =      2
										__lowerCamelCase          =      4
										__lowerCamelCase          =      37
										__lowerCamelCase          =      """gelu"""
										__lowerCamelCase          =      0.1
										__lowerCamelCase          =      0.1
										__lowerCamelCase          =      5_12
										__lowerCamelCase          =      16
										__lowerCamelCase          =      2
										__lowerCamelCase          =      0.02
										__lowerCamelCase          =      3
										__lowerCamelCase          =      4
										__lowerCamelCase          =      None




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size   )

										__lowerCamelCase          =      None
										if self.use_input_mask:
													__lowerCamelCase          =      random_attention_mask([self.batch_size, self.seq_length]   )

										__lowerCamelCase          =      None
										if self.use_token_type_ids:
													__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size   )

										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										__lowerCamelCase          =      None
										if self.use_labels:
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.type_sequence_label_size   )
													__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels   )
													__lowerCamelCase          =      ids_tensor([self.batch_size]     ,							self.num_choices   )

										__lowerCamelCase          =      RoFormerConfig(
										    vocab_size=self.vocab_size     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							return_dict=A_     ,							)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def   __a   (     self:    Union[str, Any]     ,							A_:    str     ,							A_:    Dict     ,							A_:    Union[str, Any]     ,							A_:    Optional[int]     ,							A_:    Any     ,							A_:    Dict     ,							A_:    int   ):
										__lowerCamelCase          =      TFRoFormerModel(config=A_   )
										__lowerCamelCase          =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										__lowerCamelCase          =      [input_ids, input_mask]
										__lowerCamelCase          =      model(A_   )

										__lowerCamelCase          =      model(A_   )

										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)   )




							def   __a   (     self:    List[str]     ,							A_:    int     ,							A_:    str     ,							A_:    Any     ,							A_:    int     ,							A_:    str     ,							A_:    str     ,							A_:    Union[str, Any]   ):
										__lowerCamelCase          =      True
										__lowerCamelCase          =      TFRoFormerForCausalLM(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )["""logits"""]
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape   )     ,							[self.batch_size, self.seq_length, self.vocab_size]   )




							def   __a   (     self:    Any     ,							A_:    Optional[Any]     ,							A_:    Optional[Any]     ,							A_:    Union[str, Any]     ,							A_:    str     ,							A_:    str     ,							A_:    Optional[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      TFRoFormerForMaskedLM(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)   )




							def   __a   (     self:    Optional[Any]     ,							A_:    Dict     ,							A_:    Union[str, Any]     ,							A_:    List[Any]     ,							A_:    str     ,							A_:    int     ,							A_:    Optional[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      TFRoFormerForSequenceClassification(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)   )




							def   __a   (     self:    List[str]     ,							A_:    Tuple     ,							A_:    List[str]     ,							A_:    Union[str, Any]     ,							A_:    Optional[Any]     ,							A_:    List[str]     ,							A_:    Optional[int]     ,							A_:    List[Any]   ):
										__lowerCamelCase          =      self.num_choices
										__lowerCamelCase          =      TFRoFormerForMultipleChoice(config=A_   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      tf.tile(tf.expand_dims(A_     ,							1   )     ,							(1, self.num_choices, 1)   )
										__lowerCamelCase          =      {
										    """input_ids""": multiple_choice_inputs_ids,
										    """attention_mask""": multiple_choice_input_mask,
										    """token_type_ids""": multiple_choice_token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_choices)   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    Dict     ,							A_:    List[Any]     ,							A_:    List[Any]     ,							A_:    Union[str, Any]     ,							A_:    str     ,							A_:    Union[str, Any]     ,							A_:    List[str]   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      TFRoFormerForTokenClassification(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)   )




							def   __a   (     self:    int     ,							A_:    Union[str, Any]     ,							A_:    Any     ,							A_:    Any     ,							A_:    Dict     ,							A_:    str     ,							A_:    int     ,							A_:    Tuple   ):
										__lowerCamelCase          =      TFRoFormerForQuestionAnswering(config=A_   )
										__lowerCamelCase          =      {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.start_logits.shape     ,							(self.batch_size, self.seq_length)   )
										self.parent.assertEqual(result.end_logits.shape     ,							(self.batch_size, self.seq_length)   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										(
										    (
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,(
										    __lowerCamelCase
										)     ,
										)          =      config_and_inputs
										__lowerCamelCase          =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict




@require_tf
class 			__snake_case   (lowerCamelCase			,		lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							__a					 =       (
							    {
							        '''feature-extraction''': TFRoFormerModel,
							        '''fill-mask''': TFRoFormerForMaskedLM,
							        '''question-answering''': TFRoFormerForQuestionAnswering,
							        '''text-classification''': TFRoFormerForSequenceClassification,
							        '''text-generation''': TFRoFormerForCausalLM,
							        '''token-classification''': TFRoFormerForTokenClassification,
							        '''zero-shot''': TFRoFormerForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							__a					 =       False
							__a					 =       False




							def   __a   (     self:    List[str]     ,							A_:    List[Any]     ,							A_:    List[str]     ,							A_:    str     ,							A_:    Union[str, Any]     ,							A_:    Dict   ):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
													return True

										return False




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      TFRoFormerModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=A_     ,							hidden_size=37   )




							def   __a   (     self:    int   ):
										self.config_tester.run_common_tests()




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*A_   )




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*A_   )




							@slow
							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""   )
										self.assertIsNotNone(A_   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):




							@slow
							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
										__lowerCamelCase          =      tf.constant([[0, 1, 2, 3, 4, 5]]   )
										__lowerCamelCase          =      model(A_   )[0]

										# TODO Replace vocab size
										__lowerCamelCase          =      5_00_00

										__lowerCamelCase          =      [1, 6, vocab_size]
										self.assertEqual(output.shape     ,							A_   )

										print(output[:, :3, :3]   )

										# TODO Replace values below with what was printed above.
										__lowerCamelCase          =      tf.constant(
										    [
										        [
										            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
										            [-1.5_133_783, 0.197_433, 0.15_190_607],
										            [-5.0_135_403, -3.900_256, -0.84_038_764],
										        ]
										    ]   )
										tf.debugging.assert_near(output[:, :3, :3]     ,							A_     ,							atol=1E-4   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):
							__a					 =       1e-4




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      tf.constant([[4, 10]]   )
										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,							embedding_dim=6   )

										__lowerCamelCase          =      emba(input_ids.shape   )
										__lowerCamelCase          =      tf.constant(
										    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]   )

										tf.debugging.assert_near(A_     ,							A_     ,							atol=self.tolerance   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
										        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
										        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
										    ]   )
										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12     ,							embedding_dim=5_12   )
										emba([2, 16, 5_12]   )
										__lowerCamelCase          =      emba.weight[:3, :5]

										tf.debugging.assert_near(A_     ,							A_     ,							atol=self.tolerance   )




@require_tf
class 			__snake_case   (unittest.TestCase  ):
							__a					 =       1e-4




							def   __a   (     self:    str   ):
										# 2,12,16,64
										__lowerCamelCase          =      tf.reshape(tf.range(2 * 12 * 16 * 64     ,							dtype=tf.floataa   )     ,							shape=(2, 12, 16, 64)   ) / 1_00

										__lowerCamelCase          =      -tf.reshape(tf.range(2 * 12 * 16 * 64     ,							dtype=tf.floataa   )     ,							shape=(2, 12, 16, 64)   ) / 1_00

										__lowerCamelCase          =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,							embedding_dim=64   )
										__lowerCamelCase          =      embed_positions([2, 16, 7_68]   )[None, None, :, :]

										__lowerCamelCase     ,__lowerCamelCase          =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    A_     ,							A_     ,							A_   )

										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
										        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
										        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
										        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
										        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
										        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
										    ]   )
										__lowerCamelCase          =      tf.constant(
										    [
										        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
										        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
										        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
										        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
										        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
										        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
										    ]   )

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,							A_     ,							atol=self.tolerance   )
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,							A_     ,							atol=self.tolerance   )




