


'''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__":
   lowercase__					  =		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)

   lowercase__  ,					lowercase__					  =		parser.parse_known_args()

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

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

   # Load dataset
   lowercase__  ,					lowercase__					  =		load_dataset("imdb", split=["train", "test"])
   lowercase__					  =		train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
   lowercase__					  =		test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   lowercase__					  =		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"])

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

   # Preprocess test dataset
   lowercase__					  =		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"])

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

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

   lowercase__					  =		time.time()
   lowercase__					  =		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   lowercase__					  =		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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

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

          return inputs






     def 			a_       (      self		)							->     Any:
          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 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

