
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 	lowerCamelCase						:





     def __init__(self :       int							,       _A :       Union[str, Any]  )		->		int:
         snake_case						=   parent
         snake_case						=   1_3
         snake_case						=   7
         snake_case						=   True
         snake_case						=   True
         snake_case						=   True
         snake_case						=   True
         snake_case						=   True
         snake_case						=   9_9
         snake_case						=   3_2
         snake_case						=   4
         snake_case						=   2
         snake_case						=   4
         snake_case						=   3_7
         snake_case						=   "gelu"
         snake_case						=   0.1
         snake_case						=   0.1
         snake_case						=   5_1_2
         snake_case						=   1_6
         snake_case						=   2
         snake_case						=   0.02
         snake_case						=   3
         snake_case						=   4
         snake_case						=   None
         snake_case						=   self.vocab_size - 1
         snake_case						=   self.vocab_size - 1
         snake_case						=   self.vocab_size - 1





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[int]:
         snake_case						=   ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size  )

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

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

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

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

         snake_case						=   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							,       )

         snake_case						=   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 						UpperCAmelCase(self :       str							,       _A :       Tuple							,       _A :       int							,       _A :       Union[str, Any]							,       _A :       Union[str, Any]							,       _A :       List[Any]							,       *_A :       List[Any]  )		->		Dict:
         snake_case						=   TFGPTJModel(config=_A  )
         snake_case						=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         snake_case						=   model(_A  )

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

         snake_case						=   model(_A  )

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





     def 						UpperCAmelCase(self :       int							,       _A :       Dict							,       _A :       Dict							,       _A :       List[Any]							,       _A :       Optional[int]							,       _A :       Optional[int]							,       *_A :       Optional[Any]  )		->		Optional[Any]:
         snake_case						=   TFGPTJModel(config=_A  )

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

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

         snake_case	,				snake_case						=   outputs.to_tuple()

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

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

         snake_case						=   model(_A							,       token_type_ids=_A  )["last_hidden_state"]
         snake_case						=   model(_A							,       token_type_ids=_A							,       past_key_values=_A  )[
             "last_hidden_state"
         ]

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

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





     def 						UpperCAmelCase(self :       Tuple							,       _A :       int							,       _A :       str							,       _A :       Any							,       _A :       Any							,       _A :       Optional[Any]							,       *_A :       Dict  )		->		List[str]:
         snake_case						=   TFGPTJModel(config=_A  )

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

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

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

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

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

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

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

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





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       Tuple							,       _A :       Any							,       _A :       Union[str, Any]							,       _A :       int							,       _A :       Tuple							,       *_A :       Union[str, Any]  )		->		List[str]:
         snake_case						=   TFGPTJModel(config=_A  )

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

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

         snake_case	,				snake_case						=   outputs.to_tuple()

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

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

         snake_case						=   model(
             _A							,       token_type_ids=_A							,       attention_mask=_A  )["last_hidden_state"]
         snake_case						=   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
         snake_case						=   int(ids_tensor((1,)							,       shape_list(_A  )[-1]  )  )
         snake_case						=   output_from_no_past[:, -3:, random_slice_idx]
         snake_case						=   output_from_past[:, :, random_slice_idx]

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





     def 						UpperCAmelCase(self :       Tuple							,       _A :       Optional[int]							,       _A :       Tuple							,       _A :       int							,       _A :       List[Any]							,       _A :       List[Any]							,       *_A :       Any  )		->		List[Any]:
         snake_case						=   TFGPTJForCausalLM(config=_A  )
         snake_case						=   {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         snake_case						=   model(_A  )
         self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size)  )





     def 						UpperCAmelCase(self :       str  )		->		Optional[Any]:
         snake_case						=   self.prepare_config_and_inputs()

         (
             (
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				(
             snake_case
         )	,				
         )						=   config_and_inputs

         snake_case						=   {
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict







@require_tf
class 	lowerCamelCase						(    A_				,					A_				,					A_				,					unittest.TestCase    ):
     UpperCAmelCase__							:				Dict 	=						(
         (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
         if is_tf_available()
         else ()
     )

     UpperCAmelCase__							:				Tuple 	=						(TFGPTJForCausalLM,) if is_tf_available() else ()
     UpperCAmelCase__							:				Union[str, Any] 	=						(
         {
             "feature-extraction": TFGPTJModel,
             "question-answering": TFGPTJForQuestionAnswering,
             "text-classification": TFGPTJForSequenceClassification,
             "text-generation": TFGPTJForCausalLM,
             "zero-shot": TFGPTJForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__							:				Union[str, Any] 	=						False
     UpperCAmelCase__							:				List[Any] 	=						False
     UpperCAmelCase__							:				int 	=						False
     UpperCAmelCase__							:				Optional[int] 	=						False





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       Tuple							,       _A :       Optional[int]							,       _A :       Tuple							,       _A :       int							,       _A :       List[str]  )		->		Dict:
         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 						UpperCAmelCase(self :       Any  )		->		List[Any]:
         snake_case						=   TFGPTJModelTester(self  )
         snake_case						=   ConfigTester(self							,       config_class=_A							,       n_embd=3_7  )





     def 						UpperCAmelCase(self :       Any  )		->		Any:
         self.config_tester.run_common_tests()





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model(*_A  )





     def 						UpperCAmelCase(self :       Dict  )		->		Optional[int]:
         snake_case						=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past(*_A  )





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		Union[str, Any]:
         snake_case						=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_attention_mask_past(*_A  )





     def 						UpperCAmelCase(self :       Tuple  )		->		List[Any]:
         snake_case						=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past_large_inputs(*_A  )





     def 						UpperCAmelCase(self :       List[str]  )		->		str:
         snake_case						=   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 						UpperCAmelCase(self :       Dict  )		->		Tuple:
         snake_case						=   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 						UpperCAmelCase(self :       List[str]  )		->		Union[str, Any]:
         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 	lowerCamelCase						(    unittest.TestCase    ):





     def 						UpperCAmelCase(self :       Dict  )		->		Union[str, Any]:
         snake_case						=   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,       from_pt=_A  )
         snake_case						=   tf.convert_to_tensor([[4_6_4, 3_2_9_0]]							,       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
         snake_case						=   [4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
         # fmt: on
         snake_case						=   model.generate(_A							,       do_sample=_A  )
         self.assertListEqual(output_ids[0].numpy().tolist()							,       _A  )





     def 						UpperCAmelCase(self :       int  )		->		Union[str, Any]:
         snake_case						=   AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"							,       revision="float16"  )
         snake_case						=   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,       revision="float16"							,       from_pt=_A  )

         snake_case						=   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"  ):
             snake_case						=   model.generate(**_A							,       do_sample=_A							,       seed=[4_2, 0]  )
         snake_case						=   tokenizer.decode(output_ids[0]							,       skip_special_tokens=_A  )

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





     def 						UpperCAmelCase(self :       Optional[int]  )		->		Any:
         snake_case						=   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,       revision="float16"							,       from_pt=_A  )
         snake_case						=   AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"							,       revision="float16"  )

         snake_case						=   "left"

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

         # use different length sentences to test batching
         snake_case						=   [
             "Hello, my dog is a little",
             "Today, I",
         ]
         snake_case						=   [
             "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 						UpperCAmelCase(self :       List[str]  )		->		Optional[Any]:
         # Confirms that we get the expected results with left-padded beam search
         snake_case	,				snake_case	,				snake_case	,				snake_case						=   self._get_beam_search_test_objects()

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





     def 						UpperCAmelCase(self :       Tuple  )		->		List[Any]:
         # Confirms that left-padding is working properly
         snake_case	,				snake_case	,				snake_case	,				snake_case						=   self._get_beam_search_test_objects()

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





     def 						UpperCAmelCase(self :       Dict  )		->		Dict:
         # Confirms that XLA is working properly
         snake_case	,				snake_case	,				snake_case	,				snake_case						=   self._get_beam_search_test_objects()

         snake_case						=   tokenizer(_A							,       return_tensors="tf"							,       padding=_A  )
         snake_case						=   tf.function(model.generate							,       jit_compile=_A  )
         snake_case						=   xla_generate(**_A							,       do_sample=_A							,       num_beams=2  )
         snake_case						=   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()

