




'''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__":
				UpperCAmelCase_       				=      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=1_6)
				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)

				UpperCAmelCase_				,		UpperCAmelCase_       				=      parser.parse_known_args()

				# overwrite batch size until we have tf_glue.py
				UpperCAmelCase_       				=      1_6
				UpperCAmelCase_       				=      1_6

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

				# Load dataset
				UpperCAmelCase_				,		UpperCAmelCase_       				=      load_dataset('imdb', split=['train', 'test'])
				UpperCAmelCase_       				=      train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
				UpperCAmelCase_       				=      test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

				# Preprocess train dataset
				UpperCAmelCase_       				=      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'])

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

				# Preprocess test dataset
				UpperCAmelCase_       				=      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'])

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

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

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




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   if str(_UpperCAmelCase ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )

   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

   UpperCAmelCase__										=    {
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }

   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   self._test_save_load_optional_components()


  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()						,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,		)
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   super().test_save_load_floataa(expected_max_diff=1E-1 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   super().test_floataa_inference(expected_max_diff=1E-1 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   self._test_inference_batch_single_identical(
       expected_max_diff=1E-2						,		)
