

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch


class        _UpperCAmelCase :
  '''simple docstring'''







  def __init__( self	: Optional[int]		,						UpperCamelCase__	: Dict		,						UpperCamelCase__	: Any=14		,						UpperCamelCase__	: Dict=7		,						UpperCamelCase__	: List[str]=True		,						UpperCamelCase__	: List[str]=True		,						UpperCamelCase__	: int=False		,						UpperCamelCase__	: int=True		,						UpperCamelCase__	: str=99		,						UpperCamelCase__	: List[str]=32		,						UpperCamelCase__	: int=5		,						UpperCamelCase__	: Dict=4		,						UpperCamelCase__	: Any=37		,						UpperCamelCase__	: Union[str, Any]="gelu"		,						UpperCamelCase__	: Tuple=0.1		,						UpperCamelCase__	: List[str]=0.1		,						UpperCamelCase__	: Any=512		,						UpperCamelCase__	: Tuple=0.02		,						):
        A    =				parent
        A    =				batch_size
        A    =				seq_length
        A    =				is_training
        A    =				use_input_mask
        A    =				use_token_type_ids
        A    =				use_labels
        A    =				vocab_size
        A    =				hidden_size
        A    =				num_hidden_layers
        A    =				num_attention_heads
        A    =				intermediate_size
        A    =				hidden_act
        A    =				hidden_dropout_prob
        A    =				attention_probs_dropout_prob
        A    =				max_position_embeddings
        A    =				initializer_range
        A    =				None
        A    =				vocab_size - 1
        A    =				vocab_size - 1
        A    =				vocab_size - 1







  def 	UpperCamelCase       ( self	: Any ):
        A    =				ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size )

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

        A    =				GPTaConfig(
            vocab_size=self.vocab_size		,						n_embd=self.hidden_size		,						n_layer=self.num_hidden_layers		,						n_head=self.num_attention_heads		,						n_positions=self.max_position_embeddings		,						use_cache=__snake_case		,						bos_token_id=self.bos_token_id		,						eos_token_id=self.eos_token_id		,						pad_token_id=self.pad_token_id		,						)

        return (config, input_ids, input_mask)







  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				self.prepare_config_and_inputs()
        A ,	A ,	A    =				config_and_inputs
        A    =				{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict







  def 	UpperCamelCase       ( self	: List[str] ):
        A ,	A ,	A    =				self.prepare_config_and_inputs()

        A    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
        A    =				ids_tensor([self.batch_size, self.seq_length]		,						vocab_size=2 )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )







  def 	UpperCamelCase       ( self	: List[Any]		,						UpperCamelCase__	: List[str]		,						UpperCamelCase__	: Optional[int]		,						UpperCamelCase__	: List[str]		,						UpperCamelCase__	: str ):
        A    =				20
        A    =				model_class_name(__snake_case )

        A    =				model.init_cache(input_ids.shape[0]		,						__snake_case )
        A    =				jnp.ones((input_ids.shape[0], max_decoder_length)		,						dtype='i4' )

        A    =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1) )
        A    =				model(
            input_ids[:, :-1]		,						attention_mask=__snake_case		,						past_key_values=__snake_case		,						position_ids=__snake_case		,						)

        A    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype='i4' )
        A    =				model(
            input_ids[:, -1:]		,						attention_mask=__snake_case		,						past_key_values=outputs_cache.past_key_values		,						position_ids=__snake_case		,						)

        A    =				model(__snake_case )

        A    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3		,						msg=f'''Max diff is {diff}''' )







  def 	UpperCamelCase       ( self	: Optional[int]		,						UpperCamelCase__	: int		,						UpperCamelCase__	: Union[str, Any]		,						UpperCamelCase__	: Optional[int]		,						UpperCamelCase__	: List[Any] ):
        A    =				20
        A    =				model_class_name(__snake_case )

        A    =				jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]		,						axis=-1		,						)

        A    =				model.init_cache(input_ids.shape[0]		,						__snake_case )
        A    =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1) )

        A    =				model(
            input_ids[:, :-1]		,						attention_mask=__snake_case		,						past_key_values=__snake_case		,						position_ids=__snake_case		,						)
        A    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype='i4' )
        A    =				model(
            input_ids[:, -1:]		,						past_key_values=outputs_cache.past_key_values		,						attention_mask=__snake_case		,						position_ids=__snake_case		,						)

        A    =				model(__snake_case		,						attention_mask=__snake_case )

        A    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
        self.parent.assertTrue(diff < 1e-3		,						msg=f'''Max diff is {diff}''' )


@require_flax
class        _UpperCAmelCase (   snake_case__     ,				snake_case__     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				str					      =				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE							:				Optional[int]					      =				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()







  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				FlaxGPTaModelTester(self )







  def 	UpperCamelCase       ( self	: int ):
        for model_class_name in self.all_model_classes:
              A ,	A ,	A    =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__snake_case		,						__snake_case		,						__snake_case		,						__snake_case )







  def 	UpperCamelCase       ( self	: List[Any] ):
        for model_class_name in self.all_model_classes:
              A ,	A ,	A    =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __snake_case		,						__snake_case		,						__snake_case		,						__snake_case )







  @slow
  def 	UpperCamelCase       ( self	: str ):
        A    =				GPTaTokenizer.from_pretrained('gpt2'		,						pad_token='</s>'		,						padding_side='left' )
        A    =				tokenizer(['Hello this is a long string', 'Hey']		,						return_tensors='np'		,						padding=__snake_case		,						truncation=__snake_case )

        A    =				FlaxGPTaLMHeadModel.from_pretrained('gpt2' )
        A    =				False
        A    =				model.config.eos_token_id

        A    =				jax.jit(model.generate )

        A    =				jit_generate(inputs['input_ids']		,						attention_mask=inputs['attention_mask'] ).sequences

        A    =				tokenizer.batch_decode(__snake_case		,						skip_special_tokens=__snake_case )

        A    =				[
            'Hello this is a long string of words. I\'m going to start with the first one.\n',
            'Hey, I\'m not sure if I\'m going to be able to do',
        ]

        self.assertListEqual(__snake_case		,						__snake_case )







  @is_pt_flax_cross_test
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A ,	A    =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                    # prepare inputs
                    A    =				self._prepare_for_class(__snake_case		,						__snake_case )
                    A    =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    A    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    A    =				getattr(__snake_case		,						__snake_case )

                    A ,	A    =				pt_inputs['input_ids'].shape
                    A    =				np.random.randint(0		,						seq_length - 1		,						size=(batch_size,) )
                    for batch_idx, start_index in enumerate(__snake_case ):
                          A    =				0
                          A    =				1
                          A    =				0
                          A    =				1
                    A    =				pt_model_class(__snake_case ).eval()
                    A    =				model_class(__snake_case		,						dtype=jnp.floataa )

                    A    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,						__snake_case )
                    A    =				fx_state

                    with torch.no_grad():
                          A    =				pt_model(**__snake_case ).to_tuple()

                    A    =				fx_model(**__snake_case ).to_tuple()
                    self.assertEqual(len(__snake_case )		,						len(__snake_case )		,						'Output lengths differ between Flax and PyTorch' )
                    for fx_output, pt_output in zip(__snake_case		,						__snake_case ):
                          self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4e-2 )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                          pt_model.save_pretrained(__snake_case )
                          A    =				model_class.from_pretrained(__snake_case		,						from_pt=__snake_case )

                    A    =				fx_model_loaded(**__snake_case ).to_tuple()
                    self.assertEqual(
                        len(__snake_case )		,						len(__snake_case )		,						'Output lengths differ between Flax and PyTorch' )
                    for fx_output_loaded, pt_output in zip(__snake_case		,						__snake_case ):
                          self.assert_almost_equals(fx_output_loaded[:, -1]		,						pt_output[:, -1].numpy()		,						4e-2 )







  @is_pt_flax_cross_test
  def 	UpperCamelCase       ( self	: Optional[int] ):
        A ,	A    =				self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                    # prepare inputs
                    A    =				self._prepare_for_class(__snake_case		,						__snake_case )
                    A    =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    A    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    A    =				getattr(__snake_case		,						__snake_case )

                    A    =				pt_model_class(__snake_case ).eval()
                    A    =				model_class(__snake_case		,						dtype=jnp.floataa )

                    A    =				load_flax_weights_in_pytorch_model(__snake_case		,						fx_model.params )
                    A ,	A    =				pt_inputs['input_ids'].shape
                    A    =				np.random.randint(0		,						seq_length - 1		,						size=(batch_size,) )
                    for batch_idx, start_index in enumerate(__snake_case ):
                          A    =				0
                          A    =				1
                          A    =				0
                          A    =				1

                    # make sure weights are tied in PyTorch
                    pt_model.tie_weights()

                    with torch.no_grad():
                          A    =				pt_model(**__snake_case ).to_tuple()

                    A    =				fx_model(**__snake_case ).to_tuple()
                    self.assertEqual(len(__snake_case )		,						len(__snake_case )		,						'Output lengths differ between Flax and PyTorch' )
                    for fx_output, pt_output in zip(__snake_case		,						__snake_case ):
                          self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4e-2 )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                          fx_model.save_pretrained(__snake_case )
                          A    =				pt_model_class.from_pretrained(__snake_case		,						from_flax=__snake_case )

                    with torch.no_grad():
                          A    =				pt_model_loaded(**__snake_case ).to_tuple()

                    self.assertEqual(
                        len(__snake_case )		,						len(__snake_case )		,						'Output lengths differ between Flax and PyTorch' )
                    for fx_output, pt_output in zip(__snake_case		,						__snake_case ):
                          self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4e-2 )







  @slow
  def 	UpperCamelCase       ( self	: Tuple ):
        for model_class_name in self.all_model_classes:
              A    =				model_class_name.from_pretrained('gpt2'		,						from_pt=__snake_case )
              A    =				model(np.ones((1, 1) ) )
              self.assertIsNotNone(__snake_case )






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


_UpperCAmelCase    	=			logging.getLogger(__name__)



@dataclass
class        _UpperCAmelCase :
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				str					      =				field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
  SCREAMING_SNAKE_CASE							:				bool					      =				field(default=__lowercase     ,				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.
  SCREAMING_SNAKE_CASE							:				Optional[str]					      =				field(
      default=__lowercase     ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,				)


def 			__UpperCamelCase    ()     ->  Tuple:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      A    =				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.
            A ,	A ,	A    =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            A ,	A ,	A    =				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',    lowerCAmelCase     )

      # 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.

      A    =				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,    )
      A    =				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=lowerCAmelCase,    )
      A    =				AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path,    from_tf=bool('.ckpt' in model_args.model_name_or_path     ),    config=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )

      # Get datasets
      A    =				hasattr(model.config,    'lang2id'     )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      A    =				(
          SquadDataset(
              lowerCAmelCase,    tokenizer=lowerCAmelCase,    mode='dev',    is_language_sensitive=lowerCAmelCase,    cache_dir=model_args.cache_dir,    )
          if training_args.do_eval
          else None
      )

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

      # Initialize our Trainer
      A    =				Trainer(
          model=lowerCAmelCase,    args=lowerCAmelCase,    train_dataset=lowerCAmelCase,    eval_dataset=lowerCAmelCase,    data_collator=lowerCAmelCase,    )

      # 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 			__UpperCamelCase    (lowerCAmelCase					:  Tuple     )     ->  List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()



