
"""simple docstring"""


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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str	,      snake_case__   :     str	,      snake_case__   :     PreTrainedTokenizer	,      snake_case__   :     int	,      snake_case__   :     Optional[int] = None	,      ):
	A						   =   {}

	if train_file is not None:
		A						   =   [train_file]
	if eval_file is not None:
		A						   =   [eval_file]
	if test_file is not None:
		A						   =   [test_file]

	A						   =   datasets.load_dataset('csv'	,      data_files=snake_case__     )
	A						   =   list(ds[list(files.keys()     )[0]].features.keys()     )
	A						   =   features_name.pop(snake_case__     )
	A						   =   list(set(ds[list(files.keys()     )[0]][label_name]     )     )
	A						   =   {label: i for i, label in enumerate(snake_case__     )}
	A						   =   tokenizer.model_input_names
	A						   =   {}

	if len(snake_case__     ) == 1:
		for k in files.keys():
			A						   =   ds[k].map(
			    lambda snake_case__     : tokenizer.batch_encode_plus(
			        example[features_name[0]]	,      truncation=snake_case__	,      max_length=snake_case__	,      padding='max_length'     )	,      batched=snake_case__	,      )
	elif len(snake_case__     ) == 2:
		for k in files.keys():
			A						   =   ds[k].map(
			    lambda snake_case__     : tokenizer.batch_encode_plus(
			        (example[features_name[0]], example[features_name[1]])	,      truncation=snake_case__	,      max_length=snake_case__	,      padding='max_length'	,      )	,      batched=snake_case__	,      )

	def gen_train():
		for ex in transformed_ds[datasets.Split.TRAIN]:
			A						   =   {k: v for k, v in ex.items() if k in input_names}
			A						   =   labelaid[ex[label_name]]
			yield (d, label)

	def gen_val():
		for ex in transformed_ds[datasets.Split.VALIDATION]:
			A						   =   {k: v for k, v in ex.items() if k in input_names}
			A						   =   labelaid[ex[label_name]]
			yield (d, label)

	def gen_test():
		for ex in transformed_ds[datasets.Split.TEST]:
			A						   =   {k: v for k, v in ex.items() if k in input_names}
			A						   =   labelaid[ex[label_name]]
			yield (d, label)

	A						   =   (
	    tf.data.Dataset.from_generator(
	        snake_case__	,      ({k: tf.intaa for k in input_names}, tf.intaa)	,      ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))	,      )
	    if datasets.Split.TRAIN in transformed_ds
	    else None
	)

	if train_ds is not None:
		A						   =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

	A						   =   (
	    tf.data.Dataset.from_generator(
	        snake_case__	,      ({k: tf.intaa for k in input_names}, tf.intaa)	,      ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))	,      )
	    if datasets.Split.VALIDATION in transformed_ds
	    else None
	)

	if val_ds is not None:
		A						   =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

	A						   =   (
	    tf.data.Dataset.from_generator(
	        snake_case__	,      ({k: tf.intaa for k in input_names}, tf.intaa)	,      ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))	,      )
	    if datasets.Split.TEST in transformed_ds
	    else None
	)

	if test_ds is not None:
		A						   =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

	return train_ds, val_ds, test_ds, labelaid


_lowercase							   =				logging.getLogger(__name__)







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_lowerCamelCase:    int      =    field(metadata={'''help''': '''Which column contains the label'''}    )
						_lowerCamelCase:    str      =    field(default=_lowercase		,					metadata={'''help''': '''The path of the training file'''}    )
						_lowerCamelCase:    Optional[str]      =    field(default=_lowercase		,					metadata={'''help''': '''The path of the development file'''}    )
						_lowerCamelCase:    Optional[str]      =    field(default=_lowercase		,					metadata={'''help''': '''The path of the test file'''}    )
						_lowerCamelCase:    int      =    field(
						    default=128		,					metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated, sequences shorter will be padded.'''
						        )
						    }		,					)
						_lowerCamelCase:    bool      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_lowerCamelCase:    str      =    field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
						_lowerCamelCase:    bool      =    field(default=_lowercase		,					metadata={'''help''': '''Set this flag to use fast tokenization.'''}    )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						_lowerCamelCase:    Optional[str]      =    field(
						    default=_lowercase		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,					)






def       _snake_case				(		):
	# 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.
	A						   =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
	A   ,      A   ,      A						   =   parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir     )
	    and os.listdir(training_args.output_dir     )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
		    ' --overwrite_output_dir to overcome.'     )

	# Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,      datefmt='%m/%d/%Y %H:%M:%S'	,      level=logging.INFO	,      )
	logger.info(
	    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1     )}, '
	    F'16-bits training: {training_args.fpaa}'     )
	logger.info(F'Training/evaluation parameters {training_args}'     )

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

	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   ,      A   ,      A   ,      A						   =   get_tfds(
	    train_file=data_args.train_file	,      eval_file=data_args.dev_file	,      test_file=data_args.test_file	,      tokenizer=snake_case__	,      label_column_id=data_args.label_column_id	,      max_seq_length=data_args.max_seq_length	,      )

	A						   =   AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,      num_labels=len(snake_case__     )	,      labelaid=snake_case__	,      idalabel={id: label for label, id in labelaid.items()}	,      finetuning_task='text-classification'	,      cache_dir=model_args.cache_dir	,      )

	with training_args.strategy.scope():
		A						   =   TFAutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path	,      from_pt=bool('.bin' in model_args.model_name_or_path     )	,      config=snake_case__	,      cache_dir=model_args.cache_dir	,      )

	def compute_metrics(snake_case__   :     EvalPrediction     ) -> Dict:
		A						   =   np.argmax(p.predictions	,      axis=1     )

		return {"acc": (preds == p.label_ids).mean()}

	# Initialize our Trainer
	A						   =   TFTrainer(
	    model=snake_case__	,      args=snake_case__	,      train_dataset=snake_case__	,      eval_dataset=snake_case__	,      compute_metrics=snake_case__	,      )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir     )

	# Evaluation
	A						   =   {}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'     )
		A						   =   trainer.evaluate()
		A						   =   os.path.join(training_args.output_dir	,      'eval_results.txt'     )

		with open(snake_case__	,      'w'     ) as writer:
			logger.info('***** Eval results *****'     )

			for key, value in result.items():
				logger.info(F'  {key} = {value}'     )
				writer.write(F'{key} = {value}\n'     )

			results.update(snake_case__     )

	return results


if __name__ == "__main__":
		main()

"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort







class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=0      )     ->   Dict:
							A						   =   floats_tensor((1, 3, 128, 128)  ,rng=random.Random(A_      )      )
							A						   =   np.random.RandomState(A_      )
							A						   =   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]      )
							assert np.abs(image_slice - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   PNDMScheduler.from_config(pipe.scheduler.config  ,skip_prk_steps=A_      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							# warmup pass to apply optimizations
							A						   =   pipe(**self.get_dummy_inputs()      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   self.get_dummy_inputs()
							A						   =   pipe(**A_      ).images
							A						   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]      )

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							A						   =   ort.SessionOptions()
							A						   =   False
							return options


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							# using the PNDM scheduler by default
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'  ,revision='onnx'  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Any:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							A						   =   LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,subfolder='scheduler'  ,revision='onnx'      )
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,revision='onnx'  ,scheduler=A_  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=20  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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