


"""simple docstring"""


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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_											=			logging.getLogger(__name__)
@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   , )





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys())})
		__UpperCamelCase							=	field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase							=	field(
		    default=128   , metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Overwrite the cached training and evaluation sets'})






def    a__       ( __lowercase      )		->		Dict[str, torch.Tensor]:
				_A         =	default_data_collator(__lowercase      )
				_A         =	batch.pop("pairID"   ,	__lowercase      )
				return batch






def    a__       ( )		->		Optional[Any]:
				# 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)      )
				_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"   ,	__lowercase      )

				# Set seed
				set_seed(training_args.seed      )

				try:
								_A         =	hans_tasks_num_labels[data_args.task_name]
				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.

				_A         =	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=__lowercase   ,	finetuning_task=data_args.task_name   ,	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   ,	)
				_A         =	AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path   ,	from_tf=bool(".ckpt" in model_args.model_name_or_path      )   ,	config=__lowercase   ,	cache_dir=model_args.cache_dir   ,	)

				# Get datasets
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	)
				    if training_args.do_train
				    else None
				)
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	evaluate=__lowercase   ,	)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				_A         =	Trainer(
				    model=__lowercase   ,	args=__lowercase   ,	train_dataset=__lowercase   ,	eval_dataset=__lowercase   ,	data_collator=__lowercase   ,	)

				# 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
				if training_args.do_eval:
								logger.info("*** Evaluate ***"      )

								_A         =	trainer.predict(__lowercase      )
								_A         =	output.predictions
								_A         =	np.argmax(__lowercase   ,	axis=1      )

								_A         =	[ex.pairID for ex in eval_dataset]
								_A         =	os.path.join(training_args.output_dir   ,	"hans_predictions.txt"      )
								_A         =	eval_dataset.get_labels()
								if trainer.is_world_master():
												with open(__lowercase   ,	"w"      ) as writer:
																writer.write("pairID,gold_label\n"      )
																for pid, pred in zip(__lowercase   ,	__lowercase      ):
																				writer.write("ex" + str(__lowercase      ) + "," + label_list[int(__lowercase      )] + "\n"      )

								trainer._log(output.metrics      )






def    a__       ( __lowercase      )		->		Dict:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  seq_length
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  use_input_mask
          __UpperCamelCase     						=  use_token_type_ids
          __UpperCamelCase     						=  use_labels
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  hidden_size
          __UpperCamelCase     						=  num_hidden_layers
          __UpperCamelCase     						=  num_attention_heads
          __UpperCamelCase     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, 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     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=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       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







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







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

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

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

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

          __UpperCamelCase     						=  model(snake_case )

          __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

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

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

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

          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case )

          __UpperCamelCase     						=  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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  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 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

                  __UpperCamelCase     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,				snake_case )
                  __UpperCamelCase     						=  fx_state

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

                  __UpperCamelCase     						=  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 )
                      __UpperCamelCase     						=  model_class.from_pretrained(snake_case       ,				from_pt=snake_case )

                  __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

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

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

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

                  __UpperCamelCase     						=  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 )
                      __UpperCamelCase     						=  pt_model_class.from_pretrained(snake_case       ,				from_flax=snake_case )

                  with torch.no_grad():
                      __UpperCamelCase     						=  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 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )






