



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase__	=       logging.getLogger(__name__)






@dataclass
class       SCREAMING_SNAKE_CASE	:
							__lowerCamelCase		:		str			=field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
							__lowerCamelCase		:		Optional[str]			=field(
							    default=__UpperCAmelCase	,					metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
							__lowerCamelCase		:		Optional[str]			=field(
							    default=__UpperCAmelCase	,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
							__lowerCamelCase		:		Optional[str]			=field(
							    default=__UpperCAmelCase	,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,					)
							__lowerCamelCase		:		bool			=field(default=__UpperCAmelCase	,					metadata={'help': 'Whether tp freeze the encoder.'}    )
							__lowerCamelCase		:		bool			=field(default=__UpperCAmelCase	,					metadata={'help': 'Whether  to freeze the embeddings.'}    )






@dataclass
class       SCREAMING_SNAKE_CASE	:
							__lowerCamelCase		:		str			=field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
							__lowerCamelCase		:		Optional[str]			=field(
							    default='summarization'	,					metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}	,					)
							__lowerCamelCase		:		Optional[int]			=field(
							    default=1_024	,					metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,					)
							__lowerCamelCase		:		Optional[int]			=field(
							    default=128	,					metadata={
							        'help': (
							            'The maximum total sequence length for target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,					)
							__lowerCamelCase		:		Optional[int]			=field(
							    default=142	,					metadata={
							        'help': (
							            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded. '
							            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
							            'during ``evaluate`` and ``predict``.'
							        )
							    }	,					)
							__lowerCamelCase		:		Optional[int]			=field(
							    default=142	,					metadata={
							        'help': (
							            'The maximum total sequence length for test target text after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,					)
							__lowerCamelCase		:		Optional[int]			=field(default=-1	,					metadata={'help': '# training examples. -1 means use all.'}    )
							__lowerCamelCase		:		Optional[int]			=field(default=-1	,					metadata={'help': '# validation examples. -1 means use all.'}    )
							__lowerCamelCase		:		Optional[int]			=field(default=-1	,					metadata={'help': '# test examples. -1 means use all.'}    )
							__lowerCamelCase		:		Optional[str]			=field(default=__UpperCAmelCase	,					metadata={'help': 'Source language id for translation.'}    )
							__lowerCamelCase		:		Optional[str]			=field(default=__UpperCAmelCase	,					metadata={'help': 'Target language id for translation.'}    )
							__lowerCamelCase		:		Optional[int]			=field(default=__UpperCAmelCase	,					metadata={'help': '# num_beams to use for evaluation.'}    )
							__lowerCamelCase		:		bool			=field(
							    default=__UpperCAmelCase	,					metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,					)



def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	int ,   _SCREAMING_SNAKE_CASE				:	Dict ,   _SCREAMING_SNAKE_CASE				:	Optional[Any]			):
				"""simple docstring"""







				logger.info(f"***** {split} metrics *****"			)
				for key in sorted(metrics.keys()			):
								logger.info(f"  {key} = {metrics[key]}"			)
				save_json(lowerCamelCase__ ,   os.path.join(lowerCamelCase__ ,   f"{split}_results.json"			)			)

def      lowerCAmelCase__  (							):
				"""simple docstring"""







				__a        =						HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)			)

				if len(sys.argv			) == 2 and sys.argv[1].endswith(""".json"""			):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								__a        =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
				else:
								__a        =						parser.parse_args_into_dataclasses()

				check_output_dir(lowerCamelCase__			)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,   datefmt="""%m/%d/%Y %H:%M:%S""" ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,   )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" ,   training_args.local_rank ,   training_args.device ,   training_args.n_gpu ,   bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED			) ,   training_args.fpaa ,   )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank			):
								transformers.utils.logging.set_verbosity_info()
				logger.info("""Training/evaluation parameters %s""" ,   lowerCamelCase__			)

				# Set seed
				set_seed(training_args.seed			)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__a        =						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,   cache_dir=model_args.cache_dir ,   )

				__a        =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
				for p in extra_model_params:
								if getattr(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__			):
												assert hasattr(lowerCamelCase__ ,   lowerCamelCase__			), f"({config.__class__.__name__}) doesn\'t have a `{p}` attribute"
												setattr(lowerCamelCase__ ,   lowerCamelCase__ ,   getattr(lowerCamelCase__ ,   lowerCamelCase__			)			)

				__a        =						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,   cache_dir=model_args.cache_dir ,   )
				__a        =						AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path ,   from_tf=""".ckpt""" in model_args.model_name_or_path ,   config=lowerCamelCase__ ,   cache_dir=model_args.cache_dir ,   )

				# use task specific params
				use_task_specific_params(lowerCamelCase__ ,   data_args.task			)

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								__a        =						model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(lowerCamelCase__ ,   (MBartTokenizer, MBartTokenizerFast)			):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(lowerCamelCase__ ,   lowerCamelCase__			):
												__a        =						tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												__a        =						tokenizer.convert_tokens_to_ids(data_args.tgt_lang			)

				if model_args.freeze_embeds:
								freeze_embeds(lowerCamelCase__			)
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()			)
								assert_all_frozen(model.get_encoder()			)

				__a        =						SeqaSeqDataset

				# Get datasets
				__a        =						(
				    dataset_class(
				        lowerCamelCase__ ,   type_path="""train""" ,   data_dir=data_args.data_dir ,   n_obs=data_args.n_train ,   max_target_length=data_args.max_target_length ,   max_source_length=data_args.max_source_length ,   prefix=model.config.prefix or """""" ,   )
				    if training_args.do_train
				    else None
				)
				__a        =						(
				    dataset_class(
				        lowerCamelCase__ ,   type_path="""val""" ,   data_dir=data_args.data_dir ,   n_obs=data_args.n_val ,   max_target_length=data_args.val_max_target_length ,   max_source_length=data_args.max_source_length ,   prefix=model.config.prefix or """""" ,   )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				__a        =						(
				    dataset_class(
				        lowerCamelCase__ ,   type_path="""test""" ,   data_dir=data_args.data_dir ,   n_obs=data_args.n_test ,   max_target_length=data_args.test_max_target_length ,   max_source_length=data_args.max_source_length ,   prefix=model.config.prefix or """""" ,   )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				__a        =						(
				    build_compute_metrics_fn(data_args.task ,   lowerCamelCase__			) if training_args.predict_with_generate else None
				)
				__a        =						SeqaSeqTrainer(
				    model=lowerCamelCase__ ,   args=lowerCamelCase__ ,   data_args=lowerCamelCase__ ,   train_dataset=lowerCamelCase__ ,   eval_dataset=lowerCamelCase__ ,   data_collator=SeqaSeqDataCollator(
				        lowerCamelCase__ ,   lowerCamelCase__ ,   model.config.decoder_start_token_id ,   training_args.tpu_num_cores			) ,   compute_metrics=lowerCamelCase__ ,   tokenizer=lowerCamelCase__ ,   )

				__a        =						{}
				# Training
				if training_args.do_train:
								logger.info("""*** Train ***"""			)

								__a        =						trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
								__a        =						train_result.metrics
								__a        =						data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics("""train""" ,   lowerCamelCase__ ,   training_args.output_dir			)
												all_metrics.update(lowerCamelCase__			)

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir ,   """trainer_state.json"""			)			)

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""			)

								__a        =						trainer.evaluate(metric_key_prefix="""val"""			)
								__a        =						data_args.n_val
								__a        =						round(metrics["""val_loss"""] ,   4			)

								if trainer.is_world_process_zero():
												handle_metrics("""val""" ,   lowerCamelCase__ ,   training_args.output_dir			)
												all_metrics.update(lowerCamelCase__			)

				if training_args.do_predict:
								logger.info("""*** Predict ***"""			)

								__a        =						trainer.predict(test_dataset=lowerCamelCase__ ,   metric_key_prefix="""test"""			)
								__a        =						test_output.metrics
								__a        =						data_args.n_test

								if trainer.is_world_process_zero():
												__a        =						round(metrics["""test_loss"""] ,   4			)
												handle_metrics("""test""" ,   lowerCamelCase__ ,   training_args.output_dir			)
												all_metrics.update(lowerCamelCase__			)

												if training_args.predict_with_generate:
																__a        =						tokenizer.batch_decode(
																    test_output.predictions ,   skip_special_tokens=lowerCamelCase__ ,   clean_up_tokenization_spaces=lowerCamelCase__			)
																__a        =						lmap(str.strip ,   lowerCamelCase__			)
																write_txt_file(lowerCamelCase__ ,   os.path.join(training_args.output_dir ,   """test_generations.txt"""			)			)

				if trainer.is_world_process_zero():
								save_json(lowerCamelCase__ ,   os.path.join(training_args.output_dir ,   """all_results.json"""			)			)

				return all_metrics

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	List[Any]			):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()





'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')