

import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
       UpperCamelCase__	:   Union[str, Any]								=     True
       UpperCamelCase__	:   str								=     "p3dn.24xlarge"
       import smdistributed.dataparallel.tensorflow as sdp
else:
       UpperCamelCase__	:   Optional[int]								=     False


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		str			, _SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		List[str]			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		Dict			, _SCREAMING_SNAKE_CASE       :		str=None							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    tqdm(lowercase__							)
    for i, batch in enumerate(lowercase__							):
        with tf.GradientTape() as tape:
            SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    batch
            SCREAMING_SNAKE_CASE_						=    model(lowercase__							)
            SCREAMING_SNAKE_CASE_						=    loss(lowercase__			, outputs.logits							)

        if SDP_ENABLED:
            SCREAMING_SNAKE_CASE_						=    sdp.DistributedGradientTape(lowercase__			, sparse_as_dense=lowercase__							)

        SCREAMING_SNAKE_CASE_						=    tape.gradient(lowercase__			, model.trainable_variables							)
        opt.apply_gradients(zip(lowercase__			, model.trainable_variables							)							)

        pbar.set_description(f"""Loss: {loss_value:.4f}"""							)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables			, root_rank=0							)
            sdp.broadcast_variables(opt.variables()			, root_rank=0							)

        if max_steps and i >= max_steps:
            break

    SCREAMING_SNAKE_CASE_						=    {'loss': loss_value.numpy()}
    return train_results


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		str			, _SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		Optional[Any]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    load_dataset('imdb'			, split=['train', 'test']							)

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

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

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

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

    if SDP_ENABLED:
        SCREAMING_SNAKE_CASE_						=    tf_train_dataset.shard(sdp.size()			, sdp.rank()							)
        SCREAMING_SNAKE_CASE_						=    tf_test_dataset.shard(sdp.size()			, sdp.rank()							)
    SCREAMING_SNAKE_CASE_						=    tf_train_dataset.batch(lowercase__			, drop_remainder=lowercase__							)
    SCREAMING_SNAKE_CASE_						=    tf_test_dataset.batch(lowercase__			, drop_remainder=lowercase__							)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       UpperCamelCase__	:   Tuple								=     argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument("--epochs", type=int, default=3)
       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)
       parser.add_argument("--max_steps", type=int, default=None)

       # Data, model, and output directories
       parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
       parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
       parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

       UpperCamelCase__						,			UpperCamelCase__	:   Tuple								=     parser.parse_known_args()

       # Set up logging
       UpperCamelCase__	:   Optional[int]								=     logging.getLogger(__name__)

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

       if SDP_ENABLED:
              sdp.init()

              UpperCamelCase__	:   Dict								=     tf.config.experimental.list_physical_devices("GPU")
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
       UpperCamelCase__	:   List[str]								=     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       UpperCamelCase__	:   Tuple								=     AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       UpperCamelCase__						,			UpperCamelCase__	:   List[Any]								=     get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

       # fine optimizer and loss
       UpperCamelCase__	:   Optional[Any]								=     tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       UpperCamelCase__	:   Any								=     tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       UpperCamelCase__	:   Union[str, Any]								=     [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              UpperCamelCase__	:   Dict								=     time.time()
              UpperCamelCase__	:   Tuple								=     fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              UpperCamelCase__	:   Dict								=     time.time() - start_train_time
              logger.info("*** Train ***")
              logger.info(F'train_runtime = {end_train_time}')

              UpperCamelCase__	:   Any								=     os.path.join(args.output_dir, "train_results.txt")

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, "w") as writer:
                            logger.info("***** Train results *****")
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(F'  {key} = {value}')
                                   writer.write(F'{key} = {value}\n')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              UpperCamelCase__	:   List[str]								=     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info("*** Evaluate ***")

              UpperCamelCase__	:   Optional[Any]								=     os.path.join(args.output_dir, "eval_results.txt")

              with open(output_eval_file, "w") as writer:
                     logger.info("***** Eval results *****")
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(F'  {key} = {value}')
                            writer.write(F'{key} = {value}\n')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__			=    {
    "agents": ["Agent", "AzureOpenAiAgent", "HfAgent", "LocalAgent", "OpenAiAgent"],
    "base": ["PipelineTool", "RemoteTool", "Tool", "launch_gradio_demo", "load_tool"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__			=    ["DocumentQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageCaptioningTool"]
			UpperCamelCase__			=    ["ImageQuestionAnsweringTool"]
			UpperCamelCase__			=    ["ImageSegmentationTool"]
			UpperCamelCase__			=    ["SpeechToTextTool"]
			UpperCamelCase__			=    ["TextClassificationTool"]
			UpperCamelCase__			=    ["TextQuestionAnsweringTool"]
			UpperCamelCase__			=    ["TextSummarizationTool"]
			UpperCamelCase__			=    ["TextToSpeechTool"]
			UpperCamelCase__			=    ["TranslationTool"]

if TYPE_CHECKING:
			from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
			from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .document_question_answering import DocumentQuestionAnsweringTool
						from .image_captioning import ImageCaptioningTool
						from .image_question_answering import ImageQuestionAnsweringTool
						from .image_segmentation import ImageSegmentationTool
						from .speech_to_text import SpeechToTextTool
						from .text_classification import TextClassificationTool
						from .text_question_answering import TextQuestionAnsweringTool
						from .text_summarization import TextSummarizationTool
						from .text_to_speech import TextToSpeechTool
						from .translation import TranslationTool
else:
			import sys

			UpperCamelCase__			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





