






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase       :       List[Any]        =				16
lowerCamelCase       :       Optional[int]        =				32
def 					snake_case_						(   lowerCAmelCase_				:  Accelerator				,				lowerCAmelCase_				:  int = 16				,				lowerCAmelCase_				:  str = "bert-base-cased"					):
							__lowercase					:							Dict			=   AutoTokenizer.from_pretrained(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   load_dataset("""glue"""				,				"""mrpc"""					)

							def tokenize_function(lowerCAmelCase_				:  List[str]					):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase					:							List[Any]			=   tokenizer(examples["""sentence1"""]				,				examples["""sentence2"""]				,				truncation=lowerCAmelCase_				,				max_length=lowerCAmelCase_					)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							__lowercase					:							str			=   datasets.map(
							    lowerCAmelCase_				,				batched=lowerCAmelCase_				,				remove_columns=["""idx""", """sentence1""", """sentence2"""]				,				load_from_cache_file=lowerCAmelCase_					)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							__lowercase					:							List[str]			=   tokenized_datasets.rename_column("""label"""				,				"""labels"""					)

							def collate_fn(lowerCAmelCase_				:  Dict					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(lowerCAmelCase_				,				padding="""max_length"""				,				max_length=128				,				return_tensors="""pt"""					)
														return tokenizer.pad(lowerCAmelCase_				,				padding="""longest"""				,				return_tensors="""pt"""					)

							# Instantiate dataloaders.
							__lowercase					:							Union[str, Any]			=   DataLoader(
							    tokenized_datasets["""train"""]				,				shuffle=lowerCAmelCase_				,				collate_fn=lowerCAmelCase_				,				batch_size=lowerCAmelCase_					)
							__lowercase					:							Union[str, Any]			=   DataLoader(
							    tokenized_datasets["""validation"""]				,				shuffle=lowerCAmelCase_				,				collate_fn=lowerCAmelCase_				,				batch_size=lowerCAmelCase_					)

							return train_dataloader, eval_dataloader
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Optional[int]					):
							# Initialize accelerator
							__lowercase					:							str			=   Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowercase					:							Optional[Any]			=   config["""lr"""]
							__lowercase					:							str			=   int(config["""num_epochs"""]					)
							__lowercase					:							str			=   int(config["""seed"""]					)
							__lowercase					:							Optional[Any]			=   int(config["""batch_size"""]					)
							__lowercase					:							Any			=   args.model_name_or_path

							set_seed(lowerCAmelCase_					)
							__lowercase	,	__lowercase					:							Any			=   get_dataloaders(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowercase					:							List[Any]			=   AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_				,				return_dict=lowerCAmelCase_					)

							# Instantiate optimizer
							__lowercase					:							Tuple			=   (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							__lowercase					:							Optional[int]			=   optimizer_cls(params=model.parameters()				,				lr=lowerCAmelCase_					)

							if accelerator.state.deepspeed_plugin is not None:
														__lowercase					:							Optional[Any]			=   accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														__lowercase					:							Optional[int]			=   1
							__lowercase					:							Dict			=   (len(lowerCAmelCase_					) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														__lowercase					:							List[Any]			=   get_linear_schedule_with_warmup(
														    optimizer=lowerCAmelCase_				,				num_warmup_steps=0				,				num_training_steps=lowerCAmelCase_				,				)
							else:
														__lowercase					:							Optional[int]			=   DummyScheduler(lowerCAmelCase_				,				total_num_steps=lowerCAmelCase_				,				warmup_num_steps=0					)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							__lowercase	,	__lowercase	,	__lowercase	,	__lowercase	,	__lowercase					:							Optional[int]			=   accelerator.prepare(
							    lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

							# We need to keep track of how many total steps we have iterated over
							__lowercase					:							int			=   0
							# We also need to keep track of the stating epoch so files are named properly
							__lowercase					:							List[str]			=   0

							# Now we train the model
							__lowercase					:							Any			=   evaluate.load("""glue"""				,				"""mrpc"""					)
							__lowercase					:							int			=   0
							__lowercase					:							List[str]			=   {}
							for epoch in range(lowerCAmelCase_				,				lowerCAmelCase_					):
														model.train()
														for step, batch in enumerate(lowerCAmelCase_					):
																					__lowercase					:							Union[str, Any]			=   model(**lowerCAmelCase_					)
																					__lowercase					:							Optional[int]			=   outputs.loss
																					__lowercase					:							Any			=   loss / gradient_accumulation_steps
																					accelerator.backward(lowerCAmelCase_					)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														__lowercase					:							Tuple			=   0
														for step, batch in enumerate(lowerCAmelCase_					):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device					)
																					with torch.no_grad():
																												__lowercase					:							Optional[Any]			=   model(**lowerCAmelCase_					)
																					__lowercase					:							Optional[int]			=   outputs.logits.argmax(dim=-1					)
																					# It is slightly faster to call this once, than multiple times
																					__lowercase	,	__lowercase					:							Optional[int]			=   accelerator.gather(
																					    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(lowerCAmelCase_					) - 1:
																																			__lowercase					:							Optional[Any]			=   predictions[: len(eval_dataloader.dataset					) - samples_seen]
																																			__lowercase					:							Optional[Any]			=   references[: len(eval_dataloader.dataset					) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=lowerCAmelCase_				,				references=lowerCAmelCase_				,				)

														__lowercase					:							List[str]			=   metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:"				,				lowerCAmelCase_					)
														__lowercase					:							Dict			=   eval_metric["""accuracy"""]

														if best_performance < eval_metric["accuracy"]:
																					__lowercase					:							Union[str, Any]			=   eval_metric["""accuracy"""]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), F"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir				,				"""all_results.json"""					)				,				"""w"""					) as f:
																					json.dump(lowerCAmelCase_				,				lowerCAmelCase_					)
def 					snake_case_						(   ):
							__lowercase					:							Union[str, Any]			=   argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
							parser.add_argument(
							    """--model_name_or_path"""				,				type=lowerCAmelCase_				,				default="""bert-base-cased"""				,				help="""Path to pretrained model or model identifier from huggingface.co/models."""				,				required=lowerCAmelCase_				,				)
							parser.add_argument(
							    """--output_dir"""				,				type=lowerCAmelCase_				,				default="""."""				,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,				)
							parser.add_argument(
							    """--performance_lower_bound"""				,				type=lowerCAmelCase_				,				default=lowerCAmelCase_				,				help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""				,				)
							parser.add_argument(
							    """--num_epochs"""				,				type=lowerCAmelCase_				,				default=3				,				help="""Number of train epochs."""				,				)
							__lowercase					:							List[str]			=   parser.parse_args()
							__lowercase					:							Dict			=   {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(lowerCAmelCase_				,				lowerCAmelCase_					)


if __name__ == "__main__":
	main()







# 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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
	from PIL import Image

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	Any     =					(
					    '''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
					    '''should be the document containing the information, as well as a `question` that is the question about the '''
					    '''document. It returns a text that contains the answer to the question.'''
					)
					_A  :	Tuple     =					'''document_qa'''
					_A  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												if not is_vision_available():
																			raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""							)

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							int			=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__lowercase					:							str			=   task_prompt.replace("""{user_input}"""						,   __a							)
												__lowercase					:							Union[str, Any]			=   self.pre_processor.tokenizer(
												    __a						,   add_special_tokens=__a						,   return_tensors="""pt"""							).input_ids
												__lowercase					:							int			=   self.pre_processor(__a						,   return_tensors="""pt"""							).pixel_values

												return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     int:


												"""simple docstring"""

												return self.model.generate(
												    inputs["""pixel_values"""].to(self.device							)						,   decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device							)						,   max_length=self.model.decoder.config.max_position_embeddings						,   early_stopping=__a						,   pad_token_id=self.pre_processor.tokenizer.pad_token_id						,   eos_token_id=self.pre_processor.tokenizer.eos_token_id						,   use_cache=__a						,   num_beams=1						,   bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]						,   return_dict_in_generate=__a						,   ).sequences



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.pre_processor.batch_decode(__a							)[0]
												__lowercase					:							int			=   sequence.replace(self.pre_processor.tokenizer.eos_token						,   """"""							)
												__lowercase					:							Union[str, Any]			=   sequence.replace(self.pre_processor.tokenizer.pad_token						,   """"""							)
												__lowercase					:							Optional[Any]			=   re.sub(r"""<.*?>"""						,   """"""						,   __a						,   count=1							).strip()  # remove first task start token
												__lowercase					:							Dict			=   self.pre_processor.tokenajson(__a							)

												return sequence["answer"]