"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

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


if is_tf_available():
						import tensorflow as tf

						from transformers.models.gptj.modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    shape_list,
						)
class 							snake_case			:




					"""simple docstring"""

					def __init__(							self  :      int       ,lowerCamelCase__  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				parent
									UpperCAmelCase__			 =				13
									UpperCAmelCase__			 =				7
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				99
									UpperCAmelCase__			 =				32
									UpperCAmelCase__			 =				4
									UpperCAmelCase__			 =				2
									UpperCAmelCase__			 =				4
									UpperCAmelCase__			 =				37
									UpperCAmelCase__			 =				'gelu'
									UpperCAmelCase__			 =				0.1
									UpperCAmelCase__			 =				0.1
									UpperCAmelCase__			 =				512
									UpperCAmelCase__			 =				16
									UpperCAmelCase__			 =				2
									UpperCAmelCase__			 =				0.0_2
									UpperCAmelCase__			 =				3
									UpperCAmelCase__			 =				4
									UpperCAmelCase__			 =				None
									UpperCAmelCase__			 =				self.vocab_size - 1
									UpperCAmelCase__			 =				self.vocab_size - 1
									UpperCAmelCase__			 =				self.vocab_size - 1







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

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

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

									UpperCAmelCase__			 =				None
									if self.use_mc_token_ids:
													UpperCAmelCase__			 =				ids_tensor([self.batch_size, self.num_choices]       ,self.seq_length     )

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

									UpperCAmelCase__			 =				GPTJConfig(
									    vocab_size=self.vocab_size       ,n_embd=self.hidden_size       ,n_layer=self.num_hidden_layers       ,n_head=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       ,n_positions=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,bos_token_id=self.bos_token_id       ,eos_token_id=self.eos_token_id       ,pad_token_id=self.pad_token_id       ,rotary_dim=self.rotary_dim       ,return_dict=_A       ,)

									UpperCAmelCase__			 =				ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,2     )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)







					def 						__lowerCAmelCase		(							self  :      str       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      List[Any]       ,*lowerCamelCase__  :      List[Any]     ):
									UpperCAmelCase__			 =				TFGPTJModel(config=_A     )
									UpperCAmelCase__			 =				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__			 =				model(_A     )

									UpperCAmelCase__			 =				[input_ids, None, input_mask]  # None is the input for 'past'
									UpperCAmelCase__			 =				model(_A     )

									UpperCAmelCase__			 =				model(_A     )

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







					def 						__lowerCAmelCase		(							self  :      int       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      Dict       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      Optional[int]       ,*lowerCamelCase__  :      Optional[Any]     ):
									UpperCAmelCase__			 =				TFGPTJModel(config=_A     )

									# first forward pass
									UpperCAmelCase__			 =				model(_A       ,token_type_ids=_A       ,use_cache=_A     )
									UpperCAmelCase__			 =				model(_A       ,token_type_ids=_A     )
									UpperCAmelCase__			 =				model(_A       ,token_type_ids=_A       ,use_cache=_A     )

									self.parent.assertTrue(len(_A     ) == len(_A     )     )
									self.parent.assertTrue(len(_A     ) == len(_A     ) + 1     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, 1)       ,config.vocab_size     )
									UpperCAmelCase__			 =				ids_tensor([self.batch_size, 1]       ,self.type_vocab_size     )

									# append to next input_ids and token_type_ids
									UpperCAmelCase__			 =				tf.concat([input_ids, next_tokens]       ,axis=-1     )
									UpperCAmelCase__			 =				tf.concat([token_type_ids, next_token_types]       ,axis=-1     )

									UpperCAmelCase__			 =				model(_A       ,token_type_ids=_A     )['last_hidden_state']
									UpperCAmelCase__			 =				model(_A       ,token_type_ids=_A       ,past_key_values=_A     )[
									    'last_hidden_state'
									]

									# select random slice
									UpperCAmelCase__			 =				int(ids_tensor((1,)       ,shape_list(_A     )[-1]     )     )
									UpperCAmelCase__			 =				output_from_no_past[:, -1, random_slice_idx]
									UpperCAmelCase__			 =				output_from_past[:, 0, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_A       ,_A       ,rtol=1e-6     )







					def 						__lowerCAmelCase		(							self  :      Tuple       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      str       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      Optional[Any]       ,*lowerCamelCase__  :      Dict     ):
									UpperCAmelCase__			 =				TFGPTJModel(config=_A     )

									# create attention mask
									UpperCAmelCase__			 =				self.seq_length // 2
									UpperCAmelCase__			 =				tf.ones((self.batch_size, half_seq_length)       ,dtype=tf.intaa     )
									UpperCAmelCase__			 =				tf.zeros((self.batch_size, self.seq_length - half_seq_length)       ,dtype=tf.intaa     )
									UpperCAmelCase__			 =				tf.concat([attn_mask_begin, attn_mask_end]       ,axis=1     )

									# first forward pass
									UpperCAmelCase__ ,      UpperCAmelCase__			 =				model(_A       ,attention_mask=_A     ).to_tuple()

									# create hypothetical next token and extent to next_input_ids
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, 1)       ,config.vocab_size     )

									# change a random masked slice from input_ids
									UpperCAmelCase__			 =				ids_tensor((1,)       ,_A     ).numpy() + 1
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, self.seq_length)       ,config.vocab_size     )
									UpperCAmelCase__			 =				tf.range(self.seq_length     ) == (self.seq_length - random_seq_idx_to_change)
									UpperCAmelCase__			 =				tf.transpose(
									    tf.broadcast_to(tf.expand_dims(_A       ,-1     )       ,(self.seq_length, self.batch_size)     )     )
									UpperCAmelCase__			 =				tf.where(_A       ,_A       ,_A     )

									# append to next input_ids and attn_mask
									UpperCAmelCase__			 =				tf.concat([input_ids, next_tokens]       ,axis=-1     )
									UpperCAmelCase__			 =				tf.concat([attn_mask, tf.ones((shape_list(_A     )[0], 1)       ,dtype=tf.intaa     )]       ,axis=1     )

									# get two different outputs
									UpperCAmelCase__			 =				model(_A       ,attention_mask=_A     )['last_hidden_state']
									UpperCAmelCase__			 =				model(_A       ,past_key_values=_A       ,attention_mask=_A     )[
									    'last_hidden_state'
									]

									# select random slice
									UpperCAmelCase__			 =				int(ids_tensor((1,)       ,shape_list(_A     )[-1]     )     )
									UpperCAmelCase__			 =				output_from_no_past[:, -1, random_slice_idx]
									UpperCAmelCase__			 =				output_from_past[:, 0, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_A       ,_A       ,rtol=1e-12     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      Tuple       ,*lowerCamelCase__  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				TFGPTJModel(config=_A     )

									UpperCAmelCase__			 =				input_ids[:1, :]
									UpperCAmelCase__			 =				input_mask[:1, :]
									UpperCAmelCase__			 =				token_type_ids[:1, :]
									UpperCAmelCase__			 =				1

									# first forward pass
									UpperCAmelCase__			 =				model(_A       ,attention_mask=_A       ,token_type_ids=_A       ,use_cache=_A     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, 3)       ,config.vocab_size     )
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, 3)       ,2     )
									UpperCAmelCase__			 =				ids_tensor((self.batch_size, 3)       ,self.type_vocab_size     )

									# append to next input_ids and token_type_ids
									UpperCAmelCase__			 =				tf.concat([input_ids, next_tokens]       ,axis=-1     )
									UpperCAmelCase__			 =				tf.concat([input_mask, next_attn_mask]       ,axis=-1     )
									UpperCAmelCase__			 =				tf.concat([token_type_ids, next_token_types]       ,axis=-1     )

									UpperCAmelCase__			 =				model(
									    _A       ,token_type_ids=_A       ,attention_mask=_A     )['last_hidden_state']
									UpperCAmelCase__			 =				model(
									    _A       ,token_type_ids=_A       ,attention_mask=_A       ,past_key_values=_A       ,)['last_hidden_state']
									self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]     )

									# select random slice
									UpperCAmelCase__			 =				int(ids_tensor((1,)       ,shape_list(_A     )[-1]     )     )
									UpperCAmelCase__			 =				output_from_no_past[:, -3:, random_slice_idx]
									UpperCAmelCase__			 =				output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_A       ,_A       ,rtol=1e-3     )







					def 						__lowerCAmelCase		(							self  :      Tuple       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      List[Any]       ,*lowerCamelCase__  :      Any     ):
									UpperCAmelCase__			 =				TFGPTJForCausalLM(config=_A     )
									UpperCAmelCase__			 =				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__			 =				model(_A     )
									self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)     )







					def 						__lowerCAmelCase		(							self  :      str     ):
									UpperCAmelCase__			 =				self.prepare_config_and_inputs()

									(
									    (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      (
									    UpperCAmelCase__				
									) ,      				
									)			 =				config_and_inputs

									UpperCAmelCase__			 =				{
									    'input_ids': input_ids,
									    'token_type_ids': token_type_ids,
									    'attention_mask': input_mask,
									}
									return config, inputs_dict






@require_tf
class 							snake_case			(					A_ ,       A_ ,       A_ ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					(
					    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
					    if is_tf_available()
					    else ()
					)

					snake_case__							       =					(TFGPTJForCausalLM,) if is_tf_available() else ()
					snake_case__							       =					(
					    {
					        "feature-extraction": TFGPTJModel,
					        "question-answering": TFGPTJForQuestionAnswering,
					        "text-classification": TFGPTJForSequenceClassification,
					        "text-generation": TFGPTJForCausalLM,
					        "zero-shot": TFGPTJForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					snake_case__							       =					False
					snake_case__							       =					False
					snake_case__							       =					False
					snake_case__							       =					False







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      List[str]     ):
									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('Fast'     )
									):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

									return False







					def 						__lowerCAmelCase		(							self  :      Any     ):
									UpperCAmelCase__			 =				TFGPTJModelTester(self     )
									UpperCAmelCase__			 =				ConfigTester(self       ,config_class=_A       ,n_embd=37     )







					def 						__lowerCAmelCase		(							self  :      Any     ):
									self.config_tester.run_common_tests()







					def 						__lowerCAmelCase		(							self  :      str     ):
									UpperCAmelCase__			 =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model(*_A     )







					def 						__lowerCAmelCase		(							self  :      Dict     ):
									UpperCAmelCase__			 =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past(*_A     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_attention_mask_past(*_A     )







					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									UpperCAmelCase__			 =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past_large_inputs(*_A     )







					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_lm_head_model(*_A     )







					@slow
					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices('GPU'     )     ) > 0       ,'skip testing on GPU for now to avoid GPU OOM.'       ,)
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									UpperCAmelCase__			 =				TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B'       ,from_pt=_A     )
									self.assertIsNotNone(_A     )







					@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'     )
					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									super().test_resize_token_embeddings()






@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 							snake_case			(					unittest.TestCase							):




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      Dict     ):
									UpperCAmelCase__			 =				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'       ,from_pt=_A     )
									UpperCAmelCase__			 =				tf.convert_to_tensor([[464, 3_290]]       ,dtype=tf.intaa     )  # The dog
									# fmt: off
									# The dog is a man's best friend. It is a loyal companion, and it is a friend
									UpperCAmelCase__			 =				[464, 3_290, 318, 257, 582, 338, 1_266, 1_545, 13, 632, 318, 257, 9_112, 15_185, 11, 290, 340, 318, 257, 1_545]
									# fmt: on
									UpperCAmelCase__			 =				model.generate(_A       ,do_sample=_A     )
									self.assertListEqual(output_ids[0].numpy().tolist()       ,_A     )







					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'       ,revision='float16'     )
									UpperCAmelCase__			 =				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'       ,revision='float16'       ,from_pt=_A     )

									UpperCAmelCase__			 =				tokenizer('Today is a nice day and'       ,return_tensors='tf'     )
									# forces the generation to happen on CPU, to avoid GPU-related quirks
									with tf.device(':/CPU:0'     ):
													UpperCAmelCase__			 =				model.generate(**_A       ,do_sample=_A       ,seed=[42, 0]     )
									UpperCAmelCase__			 =				tokenizer.decode(output_ids[0]       ,skip_special_tokens=_A     )

									UpperCAmelCase__			 =				'Today is a nice day and I’m going to go for a walk. I’'
									self.assertEqual(_A       ,_A     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									UpperCAmelCase__			 =				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'       ,revision='float16'       ,from_pt=_A     )
									UpperCAmelCase__			 =				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'       ,revision='float16'     )

									UpperCAmelCase__			 =				'left'

									# Define PAD Token = EOS Token = 50256
									UpperCAmelCase__			 =				tokenizer.eos_token
									UpperCAmelCase__			 =				model.config.eos_token_id

									# use different length sentences to test batching
									UpperCAmelCase__			 =				[
									    'Hello, my dog is a little',
									    'Today, I',
									]
									UpperCAmelCase__			 =				[
									    'Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia',
									    'Today, I’m going to be talking about a topic that’',
									]
									return model, tokenizer, sentences, expected_output_sentences







					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									# Confirms that we get the expected results with left-padded beam search
									UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				self._get_beam_search_test_objects()

									UpperCAmelCase__			 =				tokenizer(_A       ,return_tensors='tf'       ,padding=_A     )
									UpperCAmelCase__			 =				model.generate(**_A       ,do_sample=_A       ,num_beams=2     )
									UpperCAmelCase__			 =				tokenizer.batch_decode(_A       ,skip_special_tokens=_A     )
									self.assertListEqual(_A       ,_A     )







					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									# Confirms that left-padding is working properly
									UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				self._get_beam_search_test_objects()

									UpperCAmelCase__			 =				tokenizer(_A       ,return_tensors='tf'       ,padding=_A     )
									UpperCAmelCase__			 =				tokenizer(sentences[0]       ,return_tensors='tf'     )
									UpperCAmelCase__			 =				model.generate(**_A       ,do_sample=_A       ,num_beams=2     )
									UpperCAmelCase__			 =				(
									    shape_list(inputs_non_padded['input_ids']     )[-1]
									    - tf.reduce_sum(tf.cast(inputs['attention_mask'][-1]       ,tf.intaa     )     ).numpy()
									)
									UpperCAmelCase__			 =				tokenizer(sentences[1]       ,return_tensors='tf'     )
									UpperCAmelCase__			 =				model.generate(
									    **_A       ,do_sample=_A       ,num_beams=2       ,max_length=model.config.max_length - num_paddings     )
									UpperCAmelCase__			 =				tokenizer.decode(output_non_padded[0]       ,skip_special_tokens=_A     )
									UpperCAmelCase__			 =				tokenizer.decode(output_padded[0]       ,skip_special_tokens=_A     )
									self.assertListEqual(_A       ,[non_padded_sentence, padded_sentence]     )







					def 						__lowerCAmelCase		(							self  :      Dict     ):
									# Confirms that XLA is working properly
									UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__ ,      UpperCAmelCase__			 =				self._get_beam_search_test_objects()

									UpperCAmelCase__			 =				tokenizer(_A       ,return_tensors='tf'       ,padding=_A     )
									UpperCAmelCase__			 =				tf.function(model.generate       ,jit_compile=_A     )
									UpperCAmelCase__			 =				xla_generate(**_A       ,do_sample=_A       ,num_beams=2     )
									UpperCAmelCase__			 =				tokenizer.batch_decode(_A       ,skip_special_tokens=_A     )
									self.assertListEqual(_A       ,_A     )





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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        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__						)

    # Prepare Question-Answering task
    # 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					,						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					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.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						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # 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__					,						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						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

