"""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__      						=  logging.get_logger(__name__)  # pylint: disable=invalid-name



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase = "auto"    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.enable_attention_slicing(lowerCAmelCase    )

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






										"""simple docstring"""

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

										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(lowerCAmelCase		,    lowerCAmelCase    ) or callback_steps <= 0)
										):
																raise ValueError(
																    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																    F""" {type(lowerCAmelCase    )}."""    )

										# get prompt text embeddings
										snake_case								=			self.tokenizer(
										    lowerCAmelCase		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    return_tensors='pt'		,    )
										snake_case								=			text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																snake_case								=			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								=			text_input_ids[:, : self.tokenizer.model_max_length]

										if text_embeddings is None:
																snake_case								=			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								=			text_embeddings.shape
										snake_case								=			text_embeddings.repeat(1		,    lowerCAmelCase		,    1    )
										snake_case								=			text_embeddings.view(bs_embed * num_images_per_prompt		,    lowerCAmelCase		,    -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								=			guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																snake_case								=			42
																if negative_prompt is None:
																						snake_case								=			['']
																elif type(lowerCAmelCase    ) is not type(lowerCAmelCase    ):
																						raise TypeError(
																						    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase    )} !="""
																						    F""" {type(lowerCAmelCase    )}."""    )
																elif isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																						snake_case								=			[negative_prompt]
																elif batch_size != len(lowerCAmelCase    ):
																						raise ValueError(
																						    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase    )}, 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								=			negative_prompt

																snake_case								=			text_input_ids.shape[-1]
																snake_case								=			self.tokenizer(
																    lowerCAmelCase		,    padding='max_length'		,    max_length=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'		,    )
																snake_case								=			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								=			uncond_embeddings.shape[1]
																snake_case								=			uncond_embeddings.repeat(lowerCAmelCase		,    lowerCAmelCase		,    1    )
																snake_case								=			uncond_embeddings.view(batch_size * num_images_per_prompt		,    lowerCAmelCase		,    -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								=			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								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										snake_case								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
										snake_case								=			text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not exist on mps
																						snake_case								=			torch.randn(
																						    lowerCAmelCase		,    generator=lowerCAmelCase		,    device='cpu'		,    dtype=lowerCAmelCase    ).to(self.device    )
																						snake_case								=			torch.randn(lowerCAmelCase		,    generator=lowerCAmelCase		,    device='cpu'		,    dtype=lowerCAmelCase    ).to(
																						    self.device    )
																else:
																						snake_case								=			torch.randn(
																						    lowerCAmelCase		,    generator=lowerCAmelCase		,    device=self.device		,    dtype=lowerCAmelCase    )
																						snake_case								=			torch.randn(lowerCAmelCase		,    generator=lowerCAmelCase		,    device=self.device		,    dtype=lowerCAmelCase    )
										else:
																if latents_reference.shape != latents_shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
																snake_case								=			latents_reference.to(self.device    )
																snake_case								=			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								=			(latents_shape[3] - latents_shape_reference[3]) // 2
										snake_case								=			(latents_shape[2] - latents_shape_reference[2]) // 2
										snake_case								=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										snake_case								=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										snake_case								=			0 if dx < 0 else dx
										snake_case								=			0 if dy < 0 else dy
										snake_case								=			max(-dx		,    0    )
										snake_case								=			max(-dy		,    0    )
										# import pdb
										# pdb.set_trace()
										snake_case								=			latents_reference[:, :, dy : dy + h, dx : dx + w]

										# set timesteps
										self.scheduler.set_timesteps(lowerCAmelCase    )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										snake_case								=			self.scheduler.timesteps.to(self.device    )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case								=			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								=			'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
										snake_case								=			{}
										if accepts_eta:
																snake_case								=			eta

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

																# predict the noise residual
																snake_case								=			self.unet(lowerCAmelCase		,    lowerCAmelCase		,    encoder_hidden_states=lowerCAmelCase    ).sample

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

																# compute the previous noisy sample x_t -> x_t-1
																snake_case								=			self.scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

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

										snake_case								=			1 / 0.1_82_15 * latents
										snake_case								=			self.vae.decode(lowerCAmelCase    ).sample

										snake_case								=			(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								=			image.cpu().permute(0		,    2		,    3		,    1    ).float().numpy()

										if self.safety_checker is not None:
																snake_case								=			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase    )		,    return_tensors='pt'    ).to(
																    self.device    )
																snake_case					,snake_case								=			self.safety_checker(
																    images=lowerCAmelCase		,    clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
										else:
																snake_case								=			None

										if output_type == "pil":
																snake_case								=			self.numpy_to_pil(lowerCAmelCase    )

										if not return_dict:
																return (image, has_nsfw_concept)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase		,    nsfw_content_detected=lowerCAmelCase    )



"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
					raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

										train_parser.add_argument(
										    '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
										train_parser.add_argument(
										    '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
										train_parser.add_argument(
										    '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
										train_parser.add_argument(
										    '--column_id'		,    type=lowerCAmelCase		,    default=2		,    help='Column of the dataset csv file with example ids.'    )
										train_parser.add_argument(
										    '--skip_first_row'		,    action='store_true'		,    help='Skip the first row of the csv file (headers).'    )

										train_parser.add_argument('--validation_data'		,    type=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
										train_parser.add_argument(
										    '--validation_split'		,    type=lowerCAmelCase		,    default=0.1		,    help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'		,    )

										train_parser.add_argument('--output'		,    type=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

										train_parser.add_argument(
										    '--task'		,    type=lowerCAmelCase		,    default='text_classification'		,    help='Task to train the model on.'    )
										train_parser.add_argument(
										    '--model'		,    type=lowerCAmelCase		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'    )
										train_parser.add_argument('--train_batch_size'		,    type=lowerCAmelCase		,    default=32		,    help='Batch size for training.'    )
										train_parser.add_argument('--valid_batch_size'		,    type=lowerCAmelCase		,    default=64		,    help='Batch size for validation.'    )
										train_parser.add_argument('--learning_rate'		,    type=lowerCAmelCase		,    default=3E-5		,    help='Learning rate.'    )
										train_parser.add_argument('--adam_epsilon'		,    type=lowerCAmelCase		,    default=1E-08		,    help='Epsilon for Adam optimizer.'    )
										train_parser.set_defaults(func=lowerCAmelCase    )

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			TextClassificationPipeline.from_pretrained(args.model    )
										elif args.task == "token_classification":
																raise NotImplementedError
										elif args.task == "question_answering":
																raise NotImplementedError

										self.logger.info(F"""Loading dataset from {args.train_data}"""    )
										snake_case								=			Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			Processor.create_from_csv(
																    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										snake_case								=			args.validation_split
										snake_case								=			args.train_batch_size
										snake_case								=			args.valid_batch_size
										snake_case								=			args.learning_rate
										snake_case								=			args.adam_epsilon

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output    )


