




'''simple docstring'''




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      :       Tuple							, _lowerCamelCase      :       int							, _lowerCamelCase      :       Union[str, Any]=14							, _lowerCamelCase      :       int=7							, _lowerCamelCase      :       Optional[Any]=True							, _lowerCamelCase      :       str=True							, _lowerCamelCase      :       Any=False							, _lowerCamelCase      :       str=True							, _lowerCamelCase      :       Optional[Any]=99							, _lowerCamelCase      :       Dict=32							, _lowerCamelCase      :       List[str]=5							, _lowerCamelCase      :       Tuple=4							, _lowerCamelCase      :       str=37							, _lowerCamelCase      :       Any="gelu"							, _lowerCamelCase      :       Any=0.1							, _lowerCamelCase      :       int=0.1							, _lowerCamelCase      :       List[str]=512							, _lowerCamelCase      :       Dict=0.02							, ):





											"""simple docstring"""







											A_ :     List[Any] =							parent
											A_ :     Dict =							batch_size
											A_ :     int =							seq_length
											A_ :     Tuple =							is_training
											A_ :     List[str] =							use_input_mask
											A_ :     Optional[Any] =							use_token_type_ids
											A_ :     Any =							use_labels
											A_ :     Tuple =							vocab_size
											A_ :     List[str] =							hidden_size
											A_ :     Dict =							num_hidden_layers
											A_ :     Tuple =							num_attention_heads
											A_ :     List[str] =							intermediate_size
											A_ :     Dict =							hidden_act
											A_ :     str =							hidden_dropout_prob
											A_ :     Optional[Any] =							attention_probs_dropout_prob
											A_ :     Optional[Any] =							max_position_embeddings
											A_ :     List[Any] =							initializer_range
											A_ :     List[Any] =							None
											A_ :     Optional[Any] =							vocab_size - 1
											A_ :     Union[str, Any] =							vocab_size - 1
											A_ :     int =							vocab_size - 1







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size    )

											A_ :     List[str] =							None
											if self.use_input_mask:
																A_ :     Optional[Any] =							random_attention_mask([self.batch_size, self.seq_length]    )

											A_ :     str =							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=_lowerCamelCase							, 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       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     str =							self.prepare_config_and_inputs()
											A_ :     int =							config_and_inputs
											A_ :     List[str] =							{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     Union[str, Any] =							self.prepare_config_and_inputs()

											A_ :     List[str] =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
											A_ :     int =							ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2    )

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







						def       _a    (     self      :       Tuple							, _lowerCamelCase      :       int							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       List[Any]    ):





											"""simple docstring"""







											A_ :     Tuple =							20
											A_ :     Tuple =							model_class_name(_lowerCamelCase    )

											A_ :     Any =							model.init_cache(input_ids.shape[0]							, _lowerCamelCase    )
											A_ :     List[str] =							jnp.ones((input_ids.shape[0], max_decoder_length)							, dtype='''i4'''    )

											A_ :     str =							jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1    )[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)    )
											A_ :     Union[str, Any] =							model(
											    input_ids[:, :-1]							, attention_mask=_lowerCamelCase							, past_key_values=_lowerCamelCase							, position_ids=_lowerCamelCase							, )

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

											A_ :     Tuple =							model(_lowerCamelCase    )

											A_ :     int =							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       _a    (     self      :       List[Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       str    ):





											"""simple docstring"""







											A_ :     int =							20
											A_ :     List[Any] =							model_class_name(_lowerCamelCase    )

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

											A_ :     Optional[int] =							model.init_cache(input_ids.shape[0]							, _lowerCamelCase    )
											A_ :     Optional[int] =							jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1    )[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)    )

											A_ :     Tuple =							model(
											    input_ids[:, :-1]							, attention_mask=_lowerCamelCase							, past_key_values=_lowerCamelCase							, position_ids=_lowerCamelCase							, )
											A_ :     int =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							, dtype='''i4'''    )
											A_ :     Optional[Any] =							model(
											    input_ids[:, -1:]							, past_key_values=outputs_cache.past_key_values							, attention_mask=_lowerCamelCase							, position_ids=_lowerCamelCase							, )

											A_ :     int =							model(_lowerCamelCase							, attention_mask=_lowerCamelCase    )

											A_ :     int =							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_     (a__,      a__,      unittest.TestCase     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_lowerCAmelCase    						=					(FlaxGPTaLMHeadModel,) if is_flax_available() else ()







						def       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							FlaxGPTaModelTester(self    )







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																A_ :     Dict =							self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																A_ :     Any =							self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )







						@slow
						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Tuple =							GPTaTokenizer.from_pretrained('''gpt2'''							, pad_token='''</s>'''							, padding_side='''left'''    )
											A_ :     Any =							tokenizer(['''Hello this is a long string''', '''Hey''']							, return_tensors='''np'''							, padding=_lowerCamelCase							, truncation=_lowerCamelCase    )

											A_ :     str =							FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''    )
											A_ :     Optional[int] =							False
											A_ :     List[str] =							model.config.eos_token_id

											A_ :     List[str] =							jax.jit(model.generate    )

											A_ :     List[Any] =							jit_generate(inputs['''input_ids''']							, attention_mask=inputs['''attention_mask''']    ).sequences

											A_ :     List[Any] =							tokenizer.batch_decode(_lowerCamelCase							, skip_special_tokens=_lowerCamelCase    )

											A_ :     Tuple =							[
											    '''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(_lowerCamelCase							, _lowerCamelCase    )







						@is_pt_flax_cross_test
						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     List[Any] =							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_ :     str =							self._prepare_for_class(_lowerCamelCase							, _lowerCamelCase    )
																					A_ :     Optional[int] =							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					A_ :     Optional[Any] =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					A_ :     str =							getattr(_lowerCamelCase							, _lowerCamelCase    )

																					A_ :     Any =							pt_inputs['''input_ids'''].shape
																					A_ :     Optional[Any] =							np.random.randint(0							, seq_length - 1							, size=(batch_size,)    )
																					for batch_idx, start_index in enumerate(_lowerCamelCase    ):
																										A_ :     Tuple =							0
																										A_ :     int =							1
																										A_ :     Tuple =							0
																										A_ :     List[str] =							1
																					A_ :     Tuple =							pt_model_class(_lowerCamelCase    ).eval()
																					A_ :     str =							model_class(_lowerCamelCase							, dtype=jnp.floataa    )

																					A_ :     Any =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()							, _lowerCamelCase    )
																					A_ :     int =							fx_state

																					with torch.no_grad():
																										A_ :     Tuple =							pt_model(**_lowerCamelCase    ).to_tuple()

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																										pt_model.save_pretrained(_lowerCamelCase    )
																										A_ :     int =							model_class.from_pretrained(_lowerCamelCase							, from_pt=_lowerCamelCase    )

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







						@is_pt_flax_cross_test
						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Tuple =							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_ :     Any =							self._prepare_for_class(_lowerCamelCase							, _lowerCamelCase    )
																					A_ :     Dict =							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					A_ :     List[str] =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					A_ :     Optional[int] =							getattr(_lowerCamelCase							, _lowerCamelCase    )

																					A_ :     int =							pt_model_class(_lowerCamelCase    ).eval()
																					A_ :     List[Any] =							model_class(_lowerCamelCase							, dtype=jnp.floataa    )

																					A_ :     Optional[int] =							load_flax_weights_in_pytorch_model(_lowerCamelCase							, fx_model.params    )
																					A_ :     Union[str, Any] =							pt_inputs['''input_ids'''].shape
																					A_ :     Optional[Any] =							np.random.randint(0							, seq_length - 1							, size=(batch_size,)    )
																					for batch_idx, start_index in enumerate(_lowerCamelCase    ):
																										A_ :     Optional[int] =							0
																										A_ :     Optional[Any] =							1
																										A_ :     Optional[Any] =							0
																										A_ :     Dict =							1

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

																					with torch.no_grad():
																										A_ :     Union[str, Any] =							pt_model(**_lowerCamelCase    ).to_tuple()

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																										fx_model.save_pretrained(_lowerCamelCase    )
																										A_ :     Optional[Any] =							pt_model_class.from_pretrained(_lowerCamelCase							, from_flax=_lowerCamelCase    )

																					with torch.no_grad():
																										A_ :     str =							pt_model_loaded(**_lowerCamelCase    ).to_tuple()

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







						@slow
						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																A_ :     int =							model_class_name.from_pretrained('''gpt2'''							, from_pt=_lowerCamelCase    )
																A_ :     Tuple =							model(np.ones((1, 1)    )    )
																self.assertIsNotNone(_lowerCamelCase    )









'''simple docstring'''




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__":
							snake_case__          =						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)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

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

							# Set up logging
							snake_case__          =						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
							snake_case__          =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							snake_case__          =						AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						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"""])

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

							# Preprocess test dataset
							snake_case__          =						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"""])

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

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

							snake_case__          =						time.time()
							snake_case__          =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							snake_case__          =						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}')



