






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a_		:						List[Any]             =    False

class 		_snake_case  (  unittest.TestCase					):
			pass



@nightly
@require_torch_gpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion')
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= 'A painting of a squirrel eating a burger '
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=2		,					output_type='numpy').images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(a)
									SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained(a)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= generator.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=2		,					output_type='numpy').images

						assert np.abs(image - new_image).sum() < 1E-5, "Models don't have the same forward pass"







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= VersatileDiffusionTextToImagePipeline.from_pretrained(
						    'shi-labs/versatile-diffusion'		,					torch_dtype=torch.floataa)
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

						SCREAMING_SNAKE_CASE						= 'A painting of a squirrel eating a burger '
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= pipe(
						    prompt=a		,					generator=a		,					guidance_scale=7.5		,					num_inference_steps=50		,					output_type='numpy').images

						SCREAMING_SNAKE_CASE						= image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE						= np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2












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,
)


a_		:						Optional[Any]             =    logging.getLogger(__name__)

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



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default='''summarization'''   ,    metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=10_24   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    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``.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    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.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# training examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# validation examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# test examples. -1 means use all.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Source language id for translation.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Target language id for translation.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=A__   ,    metadata={'''help''': '''# num_beams to use for evaluation.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}   ,    )






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






def 				lowerCamelCase__				():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			SCREAMING_SNAKE_CASE						= 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.
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			check_output_dir(_UpperCAmelCase)

			# 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'							,       _UpperCAmelCase)

			# 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.

			SCREAMING_SNAKE_CASE						= 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							,       )

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

			SCREAMING_SNAKE_CASE						= 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							,       )
			SCREAMING_SNAKE_CASE						= AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path							,       from_tf='.ckpt' in model_args.model_name_or_path							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       )

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

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

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(_UpperCAmelCase							,       (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(_UpperCAmelCase							,       _UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(data_args.tgt_lang)

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

			SCREAMING_SNAKE_CASE						= SeqaSeqDataset

			# Get datasets
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       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
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       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
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       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
			SCREAMING_SNAKE_CASE						= (
			    build_compute_metrics_fn(data_args.task							,       _UpperCAmelCase) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE						= SeqaSeqTrainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       data_args=_UpperCAmelCase							,       train_dataset=_UpperCAmelCase							,       eval_dataset=_UpperCAmelCase							,       data_collator=SeqaSeqDataCollator(
			        _UpperCAmelCase							,       _UpperCAmelCase							,       model.config.decoder_start_token_id							,       training_args.tpu_num_cores)							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       )

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

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

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics('train'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									# 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 ***')

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

						if trainer.is_world_process_zero():
									handle_metrics('val'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

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

						SCREAMING_SNAKE_CASE						= trainer.predict(test_dataset=_UpperCAmelCase							,       metric_key_prefix='test')
						SCREAMING_SNAKE_CASE						= test_output.metrics
						SCREAMING_SNAKE_CASE						= data_args.n_test

						if trainer.is_world_process_zero():
									SCREAMING_SNAKE_CASE						= round(metrics['test_loss']							,       4)
									handle_metrics('test'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

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

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

			return all_metrics






def 				lowerCamelCase__				(_UpperCAmelCase):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()




