






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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.models.layoutlm.modeling_tf_layoutlm import (
							    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFLayoutLMForMaskedLM,
							    TFLayoutLMForQuestionAnswering,
							    TFLayoutLMForSequenceClassification,
							    TFLayoutLMForTokenClassification,
							    TFLayoutLMModel,
							)

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Optional[Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Tuple=13  ,     lowerCamelCase__     :Union[str, Any]=7  ,     lowerCamelCase__     :int=True  ,     lowerCamelCase__     :Tuple=True  ,     lowerCamelCase__     :Any=True  ,     lowerCamelCase__     :Optional[int]=True  ,     lowerCamelCase__     :Dict=99  ,     lowerCamelCase__     :Dict=32  ,     lowerCamelCase__     :Union[str, Any]=2  ,     lowerCamelCase__     :Optional[int]=4  ,     lowerCamelCase__     :Any=37  ,     lowerCamelCase__     :Optional[int]="gelu"  ,     lowerCamelCase__     :int=0.1  ,     lowerCamelCase__     :int=0.1  ,     lowerCamelCase__     :Union[str, Any]=5_12  ,     lowerCamelCase__     :List[Any]=16  ,     lowerCamelCase__     :int=2  ,     lowerCamelCase__     :Optional[int]=0.02  ,     lowerCamelCase__     :Tuple=3  ,     lowerCamelCase__     :Optional[Any]=4  ,     lowerCamelCase__     :str=None  ,     lowerCamelCase__     :Tuple=10_00  ,     ):
					UpperCamelCase__ :Dict						=      parent
					UpperCamelCase__ :int						=      batch_size
					UpperCamelCase__ :str						=      seq_length
					UpperCamelCase__ :Dict						=      is_training
					UpperCamelCase__ :Dict						=      use_input_mask
					UpperCamelCase__ :Optional[int]						=      use_token_type_ids
					UpperCamelCase__ :Optional[int]						=      use_labels
					UpperCamelCase__ :Tuple						=      vocab_size
					UpperCamelCase__ :Any						=      hidden_size
					UpperCamelCase__ :Union[str, Any]						=      num_hidden_layers
					UpperCamelCase__ :List[str]						=      num_attention_heads
					UpperCamelCase__ :Any						=      intermediate_size
					UpperCamelCase__ :int						=      hidden_act
					UpperCamelCase__ :Union[str, Any]						=      hidden_dropout_prob
					UpperCamelCase__ :Optional[Any]						=      attention_probs_dropout_prob
					UpperCamelCase__ :List[Any]						=      max_position_embeddings
					UpperCamelCase__ :List[str]						=      type_vocab_size
					UpperCamelCase__ :Optional[int]						=      type_sequence_label_size
					UpperCamelCase__ :Tuple						=      initializer_range
					UpperCamelCase__ :List[Any]						=      num_labels
					UpperCamelCase__ :int						=      num_choices
					UpperCamelCase__ :str						=      scope
					UpperCamelCase__ :Tuple						=      range_bbox







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Dict						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )

					# convert bbox to numpy since TF does not support item assignment
					UpperCamelCase__ :Optional[int]						=      ids_tensor([self.batch_size, self.seq_length, 4]  ,     self.range_bbox     ).numpy()
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]     ):
						for j in range(bbox.shape[1]     ):
							if bbox[i, j, 3] < bbox[i, j, 1]:
								UpperCamelCase__ :Dict						=      bbox[i, j, 3]
								UpperCamelCase__ :Dict						=      bbox[i, j, 1]
								UpperCamelCase__ :Dict						=      t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								UpperCamelCase__ :int						=      bbox[i, j, 2]
								UpperCamelCase__ :Tuple						=      bbox[i, j, 0]
								UpperCamelCase__ :Dict						=      t
					UpperCamelCase__ :int						=      tf.convert_to_tensor(lowerCamelCase__     )

					UpperCamelCase__ :Optional[Any]						=      None
					if self.use_input_mask:
						UpperCamelCase__ :int						=      random_attention_mask([self.batch_size, self.seq_length]     )

					UpperCamelCase__ :Optional[int]						=      None
					if self.use_token_type_ids:
						UpperCamelCase__ :Optional[Any]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size     )

					UpperCamelCase__ :Optional[int]						=      None
					UpperCamelCase__ :Union[str, Any]						=      None
					UpperCamelCase__ :Tuple						=      None
					if self.use_labels:
						UpperCamelCase__ :List[Any]						=      ids_tensor([self.batch_size]  ,     self.type_sequence_label_size     )
						UpperCamelCase__ :Any						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels     )
						UpperCamelCase__ :List[str]						=      ids_tensor([self.batch_size]  ,     self.num_choices     )

					UpperCamelCase__ :int						=      LayoutLMConfig(
					    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 config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :List[Any]     ):
					UpperCamelCase__ :List[str]						=      TFLayoutLMModel(config=lowerCamelCase__     )

					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )
					UpperCamelCase__ :Tuple						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      model(lowerCamelCase__  ,     lowerCamelCase__     )
					self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)     )
					self.parent.assertEqual(result.pooler_output.shape  ,     (self.batch_size, self.hidden_size)     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Optional[int]     ):
					UpperCamelCase__ :Any						=      TFLayoutLMForMaskedLM(config=lowerCamelCase__     )

					UpperCamelCase__ :str						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__  ,     labels=lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)     )







				def 				__a   ( self     :int  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Union[str, Any]     ):
					UpperCamelCase__ :str						=      self.num_labels
					UpperCamelCase__ :int						=      TFLayoutLMForSequenceClassification(config=lowerCamelCase__     )

					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)     )







				def 				__a   ( self     :Any  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :List[Any]     ):
					UpperCamelCase__ :List[Any]						=      self.num_labels
					UpperCamelCase__ :Dict						=      TFLayoutLMForTokenClassification(config=lowerCamelCase__     )

					UpperCamelCase__ :str						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__  ,     labels=lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)     )







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Dict     ):
					UpperCamelCase__ :Union[str, Any]						=      TFLayoutLMForQuestionAnswering(config=lowerCamelCase__     )

					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__  ,     lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )
					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]     ):
					UpperCamelCase__ :List[Any]						=      self.prepare_config_and_inputs()
					(
					    (
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		(
					    UpperCamelCase__
					)				,		
					) :Dict						=      config_and_inputs
					UpperCamelCase__ :Union[str, Any]						=      {
					    """input_ids""": input_ids,
					    """bbox""": bbox,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict

@require_tf
class        lowerCAmelCase_				(      lowercase						,      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   int        =			(
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				_snake_case				:   int        =			(
				    {
				        """feature-extraction""": TFLayoutLMModel,
				        """fill-mask""": TFLayoutLMForMaskedLM,
				        """text-classification""": TFLayoutLMForSequenceClassification,
				        """token-classification""": TFLayoutLMForTokenClassification,
				        """zero-shot""": TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case				:   Tuple        =			False
				_snake_case				:   Dict        =			True
				_snake_case				:   Optional[int]        =			10







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Tuple						=      TFLayoutLMModelTester(self     )
					UpperCamelCase__ :int						=      ConfigTester(self  ,     config_class=lowerCamelCase__  ,     hidden_size=37     )







				def 				__a   ( self     :Optional[Any]     ):
					self.config_tester.run_common_tests()







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :int						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Optional[int]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :int						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__     )







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :int						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :Any						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__     )







				@slow
				def 				__a   ( self     :str     ):
					for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :Optional[Any]						=      TFLayoutLMModel.from_pretrained(lowerCamelCase__     )
						self.assertIsNotNone(lowerCamelCase__     )







				@unittest.skip("""Onnx compliancy broke with TF 2.10"""     )
				def 				__a   ( self     :Dict     ):
					pass




def 				A		(					)		-> Union[str, Any]:
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	UpperCamelCase__ :List[str]						=      tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]  )  # noqa: E231
	UpperCamelCase__ :Union[str, Any]						=      tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]  )  # noqa: E231
	UpperCamelCase__ :Tuple						=      tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]  )  # noqa: E231
	UpperCamelCase__ :List[str]						=      tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]  )  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	UpperCamelCase__ :Dict						=      tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]  )  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				@slow
				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Tuple						=      TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""     )

					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      prepare_layoutlm_batch_inputs()

					# forward pass
					UpperCamelCase__ :int						=      model(input_ids=lowerCamelCase__  ,     bbox=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )

					# test the sequence output on [0, :3, :3]
					UpperCamelCase__ :Optional[int]						=      tf.convert_to_tensor(
					    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]  ,     )

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]  ,     lowerCamelCase__  ,     atol=1e-3     )     )

					# test the pooled output on [1, :3]
					UpperCamelCase__ :Tuple						=      tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]     )

					self.assertTrue(np.allclose(outputs.pooler_output[1, :3]  ,     lowerCamelCase__  ,     atol=1e-3     )     )







				@slow
				def 				__a   ( self     :int     ):
					# initialize model with randomly initialized sequence classification head
					UpperCamelCase__ :Optional[Any]						=      TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""  ,     num_labels=2     )

					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      prepare_layoutlm_batch_inputs()

					# forward pass
					UpperCamelCase__ :List[str]						=      model(
					    input_ids=lowerCamelCase__  ,     bbox=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__  ,     labels=tf.convert_to_tensor([1, 1]     )  ,     )

					# test whether we get a loss as a scalar
					UpperCamelCase__ :Optional[int]						=      outputs.loss
					UpperCamelCase__ :str						=      (2,)
					self.assertEqual(loss.shape  ,     lowerCamelCase__     )

					# test the shape of the logits
					UpperCamelCase__ :Any						=      outputs.logits
					UpperCamelCase__ :str						=      (2, 2)
					self.assertEqual(logits.shape  ,     lowerCamelCase__     )







				@slow
				def 				__a   ( self     :Dict     ):
					# initialize model with randomly initialized token classification head
					UpperCamelCase__ :Tuple						=      TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""  ,     num_labels=13     )

					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      prepare_layoutlm_batch_inputs()

					# forward pass
					UpperCamelCase__ :Optional[int]						=      model(
					    input_ids=lowerCamelCase__  ,     bbox=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__  ,     labels=lowerCamelCase__     )

					# test the shape of the logits
					UpperCamelCase__ :Union[str, Any]						=      outputs.logits
					UpperCamelCase__ :Any						=      tf.convert_to_tensor((2, 25, 13)     )
					self.assertEqual(logits.shape  ,     lowerCamelCase__     )







				@slow
				def 				__a   ( self     :Optional[int]     ):
					# initialize model with randomly initialized token classification head
					UpperCamelCase__ :Tuple						=      TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""     )

					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      prepare_layoutlm_batch_inputs()

					# forward pass
					UpperCamelCase__ :Optional[int]						=      model(input_ids=lowerCamelCase__  ,     bbox=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     token_type_ids=lowerCamelCase__     )

					# test the shape of the logits
					UpperCamelCase__ :int						=      tf.convert_to_tensor((2, 25)     )
					self.assertEqual(outputs.start_logits.shape  ,     lowerCamelCase__     )
					self.assertEqual(outputs.end_logits.shape  ,     lowerCamelCase__     )


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__		 =     logging.getLogger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :str		):


       """simple docstring"""




       return (preds == labels).mean()

@dataclass
class     __snake_case    :
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,	)






@dataclass
class     __snake_case    :
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()  )}  )
       __SCREAMING_SNAKE_CASE				    =							field(metadata={"help": "Should contain the data files for the task."}  )
       __SCREAMING_SNAKE_CASE				    =							field(
           default=1_28			,	metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }			,	)
       __SCREAMING_SNAKE_CASE				    =							field(
           default=snake_case__			,	metadata={"help": "Overwrite the cached training and evaluation sets"}  )







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
       __snake_case	,    __snake_case	,    __snake_case       	=							parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir		)
           and os.listdir(training_args.output_dir		)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
                  """ --overwrite_output_dir to overcome."""		)

       # Setup logging
       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,datefmt="""%m/%d/%Y %H:%M:%S"""	,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,)
       logger.warning(
           """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""	,training_args.local_rank	,training_args.device	,training_args.n_gpu	,bool(training_args.local_rank != -1		)	,training_args.fpaa	,)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank		):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("""Training/evaluation parameters %s"""	,__A		)

       # Set seed
       set_seed(training_args.seed		)

       try:
              __snake_case       	=							processors[data_args.task_name]()
              __snake_case       	=							processor.get_labels()
              __snake_case       	=							len(__A		)
       except KeyError:
              raise ValueError("""Task not found: %s""" % (data_args.task_name)		)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       __snake_case       	=							AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path	,num_labels=__A	,finetuning_task=data_args.task_name	,cache_dir=model_args.cache_dir	,)
       __snake_case       	=							AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,cache_dir=model_args.cache_dir	,)
       __snake_case       	=							AutoModelForMultipleChoice.from_pretrained(
           model_args.model_name_or_path	,from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)	,config=__A	,cache_dir=model_args.cache_dir	,)

       # Get datasets
       __snake_case       	=							(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir	,tokenizer=__A	,task=data_args.task_name	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.train	,)
           if training_args.do_train
           else None
       )
       __snake_case       	=							(
           MultipleChoiceDataset(
               data_dir=data_args.data_dir	,tokenizer=__A	,task=data_args.task_name	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.dev	,)
           if training_args.do_eval
           else None
       )

       def compute_metrics(__A    :EvalPrediction		) -> Dict:
              __snake_case       	=							np.argmax(p.predictions	,axis=1		)
              return {"acc": simple_accuracy(__A	,p.label_ids		)}

       # Data collator
       __snake_case       	=							DataCollatorWithPadding(__A	,pad_to_multiple_of=8		) if training_args.fpaa else None

       # Initialize our Trainer
       __snake_case       	=							Trainer(
           model=__A	,args=__A	,train_dataset=__A	,eval_dataset=__A	,compute_metrics=__A	,data_collator=__A	,)

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
       __snake_case       	=							{}
       if training_args.do_eval:
              logger.info("""*** Evaluate ***"""		)

              __snake_case       	=							trainer.evaluate()

              __snake_case       	=							os.path.join(training_args.output_dir	,"""eval_results.txt"""		)
              if trainer.is_world_master():
                     with open(__A	,"""w"""		) as writer:
                            logger.info("""***** Eval results *****"""		)
                            for key, value in result.items():
                                   logger.info("""  %s = %s"""	,__A	,__A		)
                                   writer.write("""%s = %s\n""" % (key, value)		)

                            results.update(__A		)

       return results







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       main()


if __name__ == "__main__":
    main()

