

import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
       lowerCamelCase 	=   argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument('''--epochs''', type=int, default=1)
       parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
       parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
       parser.add_argument('''--model_name_or_path''', type=str)
       parser.add_argument('''--learning_rate''', type=str, default=5e-5)
       parser.add_argument('''--do_train''', type=bool, default=True)
       parser.add_argument('''--do_eval''', type=bool, default=True)
       parser.add_argument('''--output_dir''', type=str)

       lowerCamelCase 	=   parser.parse_known_args()

       # overwrite batch size until we have tf_glue.py
       lowerCamelCase 	=   16
       lowerCamelCase 	=   16

       # Set up logging
       lowerCamelCase 	=   logging.getLogger(__name__)

       logging.basicConfig(
           level=logging.getLevelName('''INFO'''),
           handlers=[logging.StreamHandler(sys.stdout)],
           format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
       )

       # Load model and tokenizer
       lowerCamelCase 	=   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       lowerCamelCase 	=   AutoTokenizer.from_pretrained(args.model_name_or_path)

       # Load dataset
       lowerCamelCase 	=   load_dataset('''imdb''', split=['''train''', '''test'''])
       lowerCamelCase 	=   train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
       lowerCamelCase 	=   test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

       # Preprocess train dataset
       lowerCamelCase 	=   train_dataset.map(
           lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
       )
       train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

       lowerCamelCase 	=   {
           x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
           for x in ['input_ids', 'attention_mask']
       }
       lowerCamelCase 	=   tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
           args.per_device_train_batch_size
       )

       # Preprocess test dataset
       lowerCamelCase 	=   test_dataset.map(
           lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
       )
       test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

       lowerCamelCase 	=   {
           x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
           for x in ['input_ids', 'attention_mask']
       }
       lowerCamelCase 	=   tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
           args.per_device_eval_batch_size
       )

       # fine optimizer and loss
       lowerCamelCase 	=   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       lowerCamelCase 	=   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       lowerCamelCase 	=   [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       lowerCamelCase 	=   time.time()
       lowerCamelCase 	=   model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       lowerCamelCase 	=   time.time() - start_train_time

       logger.info('''*** Train ***''')
       logger.info(f"""train_runtime = {end_train_time}""")
       for key, value in train_results.history.items():
              logger.info(f"""  {key} = {value}""")

'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	)						->	Any:
           A_      =  parent
           A_      =  13
           A_      =  7
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  99
           A_      =  32
           A_      =  2
           A_      =  4
           A_      =  37
           A_      =  '''gelu'''
           A_      =  0.1
           A_      =  0.1
           A_      =  512
           A_      =  16
           A_      =  2
           A_      =  0.02
           A_      =  3
           A_      =  4
           A_      =  None
           A_      =  self.vocab_size - 1

       def    __A			( self					)						->	Union[str, 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_      =  None
           if self.use_token_type_ids:
               A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size					)

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

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

           A_      =  OpenAIGPTConfig(
               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       ,	pad_token_id=self.pad_token_id       ,	)

           A_      =  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    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_      =  TFOpenAIGPTModel(config=_SCREAMING_SNAKE_CASE					)
           A_      =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           A_      =  model(_SCREAMING_SNAKE_CASE					)

           A_      =  [input_ids, input_mask]
           A_      =  model(_SCREAMING_SNAKE_CASE					)

           A_      =  model(_SCREAMING_SNAKE_CASE					)

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

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  TFOpenAIGPTLMHeadModel(config=_SCREAMING_SNAKE_CASE					)
           A_      =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  TFOpenAIGPTDoubleHeadsModel(config=_SCREAMING_SNAKE_CASE					)

           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)
           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)
           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)

           A_      =  {
               '''input_ids''': multiple_choice_inputs_ids,
               '''mc_token_ids''': mc_token_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(
               result.logits.shape       ,	(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)					)
           self.parent.assertEqual(result.mc_logits.shape       ,	(self.batch_size, self.num_choices)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  self.num_labels
           A_      =  ids_tensor([self.batch_size]       ,	self.type_sequence_label_size					)
           A_      =  {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
               '''labels''': sequence_labels,
           }
           A_      =  TFOpenAIGPTForSequenceClassification(_SCREAMING_SNAKE_CASE					)
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)					)

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  self.prepare_config_and_inputs()

           (
               (
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,
           )      =  config_and_inputs

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




@require_tf
class       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase		, unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   Dict  					=							(
           (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
           if is_tf_available()
           else ()
       )
       __lowercase   :   List[str]  					=							(
           (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       __lowercase   :   List[str]  					=							(
           {
               'feature-extraction': TFOpenAIGPTModel,
               'text-classification': TFOpenAIGPTForSequenceClassification,
               'text-generation': TFOpenAIGPTLMHeadModel,
               'zero-shot': TFOpenAIGPTForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowercase   :   Dict  					=							False
       __lowercase   :   Optional[int]  					=							False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Union[str, Any]:
           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
               # tiny config could not be created.
               return True

           return False

       def    __A			( self					)						->	Any:
           A_      =  TFOpenAIGPTModelTester(self					)
           A_      =  ConfigTester(self       ,	config_class=_SCREAMING_SNAKE_CASE       ,	n_embd=37					)

       def    __A			( self					)						->	List[Any]:
           self.config_tester.run_common_tests()

       def    __A			( self					)						->	Dict:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Any:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_lm_head(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Tuple:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_double_head(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[str]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_SCREAMING_SNAKE_CASE					)

       @slow
       def    __A			( self					)						->	Union[str, Any]:
           for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_      =  TFOpenAIGPTModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)




@require_tf
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       @slow
       def    __A			( self					)						->	List[str]:
           A_      =  TFOpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''					)
           A_      =  tf.convert_to_tensor([[481, 4735, 544]]       ,	dtype=tf.intaa					)  # the president is
           A_      =  [
               481,
               4735,
               544,
               246,
               963,
               870,
               762,
               239,
               244,
               4_0477,
               244,
               249,
               719,
               881,
               487,
               544,
               240,
               244,
               603,
               481,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           A_      =  model.generate(_SCREAMING_SNAKE_CASE       ,	do_sample=_SCREAMING_SNAKE_CASE					)
           self.assertListEqual(output_ids[0].numpy().tolist()       ,	_SCREAMING_SNAKE_CASE					)






