




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 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      PreTrainedTokenizer ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[int] = None ,     )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    {}

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

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

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

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

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

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

				__lowerCamelCase		       =    (
				    tf.data.Dataset.from_generator(
				        UpperCamelCase__ ,     ({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:
								__lowerCamelCase		       =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

				__lowerCamelCase		       =    (
				    tf.data.Dataset.from_generator(
				        UpperCamelCase__ ,     ({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:
								__lowerCamelCase		       =    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

				__lowerCamelCase		       =    (
				    tf.data.Dataset.from_generator(
				        UpperCamelCase__ ,     ({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:
								__lowerCamelCase		       =    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

				return train_ds, val_ds, test_ds, labelaid


__A        =    logging.getLogger(__name__)







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       field(metadata={'''help''': '''Which column contains the label'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the training file'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the development file'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the test file'''} )
			snake_case_   		=       field(
			    default=1_28			,				metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }			,				)
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
			snake_case_   		=       field(default=__magic_name__			,				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.
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,				)







def 			lowerCamelCase_		(       )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    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.

				__lowerCamelCase		       =    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 ,     )

				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    get_tfds(
				    train_file=data_args.train_file ,     eval_file=data_args.dev_file ,     test_file=data_args.test_file ,     tokenizer=UpperCamelCase__ ,     label_column_id=data_args.label_column_id ,     max_seq_length=data_args.max_seq_length ,     )

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

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

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

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

				# Initialize our Trainer
				__lowerCamelCase		       =    TFTrainer(
				    model=UpperCamelCase__ ,     args=UpperCamelCase__ ,     train_dataset=UpperCamelCase__ ,     eval_dataset=UpperCamelCase__ ,     compute_metrics=UpperCamelCase__ ,     )

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

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

								with open(UpperCamelCase__ ,     '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(UpperCamelCase__      )

				return results


if __name__ == "__main__":
					main()






"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


SCREAMING_SNAKE_CASE :    List[str]									=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

			super().__init__()
			self.register_modules(
			    vae=a_				,       text_encoder=a_				,       tokenizer=a_				,       unet=a_				,       scheduler=a_				,       safety_checker=a_				,       feature_extractor=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = "auto"					):





			'''simple docstring'''

			if slice_size == "auto":
				# half the attention head size is usually a good trade-off between
				# speed and memory
				__snake_case	:						Optional[int]						=				self.unet.config.attention_head_dim // 2
			self.unet.set_attention_slice(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.enable_attention_slicing(a_					)



		@torch.no_grad()
		def __call__(self				,       a_				,       a_ = 5_12				,       a_ = 5_12				,       a_ = 50				,       a_ = 7.5				,       a_ = None				,       a_ = 1				,       a_ = 0.0				,       a_ = None				,       a_ = None				,       a_ = "pil"				,       a_ = True				,       a_ = None				,       a_ = 1				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			if isinstance(a_				,       a_					):
				__snake_case	:						Any						=				1
			elif isinstance(a_				,       a_					):
				__snake_case	:						Any						=				len(a_					)
			else:
				raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a_					)}"""					)

			if height % 8 != 0 or width % 8 != 0:
				raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""					)

			if (callback_steps is None) or (
			    callback_steps is not None and (not isinstance(a_				,       a_					) or callback_steps <= 0)
			):
				raise ValueError(
				    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
				    f""" {type(a_					)}."""					)

			# get prompt text embeddings
			__snake_case	:						int						=				self.tokenizer(
			    a_				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
			__snake_case	:						int						=				text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
				__snake_case	:						List[Any]						=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
				logger.warning(
				    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
				    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)
				__snake_case	:						Optional[int]						=				text_input_ids[:, : self.tokenizer.model_max_length]

			if text_embeddings is None:
				__snake_case	:						Optional[int]						=				self.text_encoder(text_input_ids.to(self.device					)					)[0]

			# duplicate text embeddings for each generation per prompt, using mps friendly method
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				text_embeddings.shape
			__snake_case	:						Optional[int]						=				text_embeddings.repeat(1				,       a_				,       1					)
			__snake_case	:						Dict						=				text_embeddings.view(bs_embed * num_images_per_prompt				,       a_				,       -1					)

			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			__snake_case	:						Dict						=				guidance_scale > 1.0
			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance:
				__snake_case	:						List[str]
				if negative_prompt is None:
					__snake_case	:						List[Any]						=				['''''']
				elif type(a_					) is not type(a_					):
					raise TypeError(
					    f"""`negative_prompt` should be the same type to `prompt`, but got {type(a_					)} !="""
					    f""" {type(a_					)}."""					)
				elif isinstance(a_				,       a_					):
					__snake_case	:						List[str]						=				[negative_prompt]
				elif batch_size != len(a_					):
					raise ValueError(
					    f"""`negative_prompt`: {negative_prompt} has batch size {len(a_					)}, but `prompt`:"""
					    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
					    ''' the batch size of `prompt`.'''					)
				else:
					__snake_case	:						Optional[int]						=				negative_prompt

				__snake_case	:						Optional[int]						=				text_input_ids.shape[-1]
				__snake_case	:						List[Any]						=				self.tokenizer(
				    a_				,       padding='''max_length'''				,       max_length=a_				,       truncation=a_				,       return_tensors='''pt'''				,       )
				__snake_case	:						str						=				self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
				__snake_case	:						str						=				uncond_embeddings.shape[1]
				__snake_case	:						int						=				uncond_embeddings.repeat(a_				,       a_				,       1					)
				__snake_case	:						int						=				uncond_embeddings.view(batch_size * num_images_per_prompt				,       a_				,       -1					)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				__snake_case	:						Any						=				torch.cat([uncond_embeddings, text_embeddings]					)

			# get the initial random noise unless the user supplied it

			# Unlike in other pipelines, latents need to be generated in the target device
			# for 1-to-1 results reproducibility with the CompVis implementation.
			# However this currently doesn't work in `mps`.
			__snake_case	:						Any						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
			__snake_case	:						Dict						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
			__snake_case	:						Dict						=				text_embeddings.dtype
			if latents is None:
				if self.device.type == "mps":
					# randn does not exist on mps
					__snake_case	:						Union[str, Any]						=				torch.randn(
					    a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(self.device					)
					__snake_case	:						Tuple						=				torch.randn(a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(
					    self.device					)
				else:
					__snake_case	:						Dict						=				torch.randn(
					    a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
					__snake_case	:						Dict						=				torch.randn(a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
			else:
				if latents_reference.shape != latents_shape:
					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
				__snake_case	:						Union[str, Any]						=				latents_reference.to(self.device					)
				__snake_case	:						Dict						=				latents.to(self.device					)

			# This is the key part of the pipeline where we
			# try to ensure that the generated images w/ the same seed
			# but different sizes actually result in similar images
			__snake_case	:						int						=				(latents_shape[3] - latents_shape_reference[3]) // 2
			__snake_case	:						Tuple						=				(latents_shape[2] - latents_shape_reference[2]) // 2
			__snake_case	:						Optional[int]						=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
			__snake_case	:						Union[str, Any]						=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
			__snake_case	:						int						=				0 if dx < 0 else dx
			__snake_case	:						Union[str, Any]						=				0 if dy < 0 else dy
			__snake_case	:						str						=				max(-dx				,       0					)
			__snake_case	:						Tuple						=				max(-dy				,       0					)
			# import pdb
			# pdb.set_trace()
			__snake_case	:						Any						=				latents_reference[:, :, dy : dy + h, dx : dx + w]

			# set timesteps
			self.scheduler.set_timesteps(a_					)

			# Some schedulers like PNDM have timesteps as arrays
			# It's more optimized to move all timesteps to correct device beforehand
			__snake_case	:						Optional[Any]						=				self.scheduler.timesteps.to(self.device					)

			# scale the initial noise by the standard deviation required by the scheduler
			__snake_case	:						List[Any]						=				latents * self.scheduler.init_noise_sigma

			# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
			# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
			# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
			# and should be between [0, 1]
			__snake_case	:						Any						=				'''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
			__snake_case	:						Tuple						=				{}
			if accepts_eta:
				__snake_case	:						List[str]						=				eta

			for i, t in enumerate(self.progress_bar(a_					)					):
				# expand the latents if we are doing classifier free guidance
				__snake_case	:						str						=				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
				__snake_case	:						Tuple						=				self.scheduler.scale_model_input(a_				,       a_					)

				# predict the noise residual
				__snake_case	:						int						=				self.unet(a_				,       a_				,       encoder_hidden_states=a_					).sample

				# perform guidance
				if do_classifier_free_guidance:
					__snake_case	,							__snake_case	:						Tuple						=				noise_pred.chunk(2					)
					__snake_case	:						str						=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# compute the previous noisy sample x_t -> x_t-1
				__snake_case	:						Optional[Any]						=				self.scheduler.step(a_				,       a_				,       a_				,       **a_					).prev_sample

				# call the callback, if provided
				if callback is not None and i % callback_steps == 0:
					callback(a_				,       a_				,       a_					)

			__snake_case	:						Union[str, Any]						=				1 / 0.1_8215 * latents
			__snake_case	:						Optional[Any]						=				self.vae.decode(a_					).sample

			__snake_case	:						Union[str, Any]						=				(image / 2 + 0.5).clamp(0				,       1					)

			# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
			__snake_case	:						Union[str, Any]						=				image.cpu().permute(0				,       2				,       3				,       1					).float().numpy()

			if self.safety_checker is not None:
				__snake_case	:						Optional[int]						=				self.feature_extractor(self.numpy_to_pil(a_					)				,       return_tensors='''pt'''					).to(
				    self.device					)
				__snake_case	,							__snake_case	:						List[Any]						=				self.safety_checker(
				    images=a_				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype					)					)
			else:
				__snake_case	:						Union[str, Any]						=				None

			if output_type == "pil":
				__snake_case	:						Union[str, Any]						=				self.numpy_to_pil(a_					)

			if not return_dict:
				return (image, has_nsfw_concept)

			return StableDiffusionPipelineOutput(images=a_				,       nsfw_content_detected=a_					)





