



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   (    __snake_case	):







					'''simple docstring'''







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



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


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



					def  A__		(      self  ):
							self.enable_attention_slicing(__UpperCamelCase  )



					@torch.no_grad()
					def __call__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = 512     ,						lowerCAmelCase = 512     ,						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     ,						):


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

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

							# get prompt text embeddings
							UpperCAmelCase_						       =					self.tokenizer(
							    __UpperCamelCase     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						return_tensors="pt"     ,						)
							UpperCAmelCase_						       =					text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									UpperCAmelCase_						       =					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}'''  )
									UpperCAmelCase_						       =					text_input_ids[:, : self.tokenizer.model_max_length]

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

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							UpperCAmelCase_       ,    UpperCAmelCase_       ,    UpperCAmelCase_						       =					text_embeddings.shape
							UpperCAmelCase_						       =					text_embeddings.repeat(1     ,						__UpperCamelCase     ,						1  )
							UpperCAmelCase_						       =					text_embeddings.view(bs_embed * num_images_per_prompt     ,						__UpperCamelCase     ,						-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.
							UpperCAmelCase_						       =					guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									UpperCAmelCase_						       =					42
									if negative_prompt is None:
											UpperCAmelCase_						       =					[""]
									elif type(__UpperCamelCase  ) is not type(__UpperCamelCase  ):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCamelCase  )} !='''
											    f''' {type(__UpperCamelCase  )}.'''  )
									elif isinstance(__UpperCamelCase     ,						__UpperCamelCase  ):
											UpperCAmelCase_						       =					[negative_prompt]
									elif batch_size != len(__UpperCamelCase  ):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(__UpperCamelCase  )}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    " the batch size of `prompt`."  )
									else:
											UpperCAmelCase_						       =					negative_prompt

									UpperCAmelCase_						       =					text_input_ids.shape[-1]
									UpperCAmelCase_						       =					self.tokenizer(
									    __UpperCamelCase     ,						padding="max_length"     ,						max_length=__UpperCamelCase     ,						truncation=__UpperCamelCase     ,						return_tensors="pt"     ,						)
									UpperCAmelCase_						       =					self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									UpperCAmelCase_						       =					uncond_embeddings.shape[1]
									UpperCAmelCase_						       =					uncond_embeddings.repeat(__UpperCamelCase     ,						__UpperCamelCase     ,						1  )
									UpperCAmelCase_						       =					uncond_embeddings.view(batch_size * num_images_per_prompt     ,						__UpperCamelCase     ,						-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
									UpperCAmelCase_						       =					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`.
							UpperCAmelCase_						       =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							UpperCAmelCase_						       =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							UpperCAmelCase_						       =					text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not exist on mps
											UpperCAmelCase_						       =					torch.randn(
											    __UpperCamelCase     ,						generator=__UpperCamelCase     ,						device="cpu"     ,						dtype=__UpperCamelCase  ).to(self.device  )
											UpperCAmelCase_						       =					torch.randn(__UpperCamelCase     ,						generator=__UpperCamelCase     ,						device="cpu"     ,						dtype=__UpperCamelCase  ).to(
											    self.device  )
									else:
											UpperCAmelCase_						       =					torch.randn(
											    __UpperCamelCase     ,						generator=__UpperCamelCase     ,						device=self.device     ,						dtype=__UpperCamelCase  )
											UpperCAmelCase_						       =					torch.randn(__UpperCamelCase     ,						generator=__UpperCamelCase     ,						device=self.device     ,						dtype=__UpperCamelCase  )
							else:
									if latents_reference.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
									UpperCAmelCase_						       =					latents_reference.to(self.device  )
									UpperCAmelCase_						       =					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
							UpperCAmelCase_						       =					(latents_shape[3] - latents_shape_reference[3]) // 2
							UpperCAmelCase_						       =					(latents_shape[2] - latents_shape_reference[2]) // 2
							UpperCAmelCase_						       =					latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							UpperCAmelCase_						       =					latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							UpperCAmelCase_						       =					0 if dx < 0 else dx
							UpperCAmelCase_						       =					0 if dy < 0 else dy
							UpperCAmelCase_						       =					max(-dx     ,						0  )
							UpperCAmelCase_						       =					max(-dy     ,						0  )
							# import pdb
							# pdb.set_trace()
							UpperCAmelCase_						       =					latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(__UpperCamelCase  )

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

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

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

									# predict the noise residual
									UpperCAmelCase_						       =					self.unet(__UpperCamelCase     ,						__UpperCamelCase     ,						encoder_hidden_states=__UpperCamelCase  ).sample

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

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

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

							UpperCAmelCase_						       =					1 / 0.18215 * latents
							UpperCAmelCase_						       =					self.vae.decode(__UpperCamelCase  ).sample

							UpperCAmelCase_						       =					(image / 2 + 0.5).clamp(0     ,						1  )

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

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

							if output_type == "pil":
									UpperCAmelCase_						       =					self.numpy_to_pil(__UpperCamelCase  )

							if not return_dict:
									return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=__UpperCamelCase     ,						nsfw_content_detected=__UpperCamelCase  )




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A									=  logging.getLogger()

A									=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				os.makedirs(__UpperCamelCase			,					exist_ok=__UpperCamelCase				)
				snake_case_   		=			{'source': 'What is love ?', 'target': 'life'}
				snake_case_   		=			{'train': 12, 'val': 2, 'test': 2}
				for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
						snake_case_   		=			'\n'.join([contents[field]] * n_lines[split]				)
						with open(os.path.join(__UpperCamelCase			,					f"""{split}.{field}"""				)			,					'w'				) as f:
							f.write(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = "pytorch"				):

				"""simple docstring"""


				snake_case_   		=			self.get_auto_remove_tmp_dir()
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'output'				)
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'data'				)
				self._create_dummy_data(data_dir=__UpperCamelCase				)

				snake_case_   		=			f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

				if gpus > 0:
					testargs.append(f"""--gpus={gpus}"""				)
					if is_apex_available():
						testargs.append('--fp16'				)
				else:
					testargs.append('--gpus=0'				)
					testargs.append('--distributed_backend=ddp_cpu'				)
					testargs.append('--num_processes=2'				)

				snake_case_   		=			[sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
				execute_subprocess_async(__UpperCamelCase			,					env=self.get_env()				)

				snake_case_   		=			os.path.join(__UpperCamelCase			,					'metrics.json'				)
				with open(__UpperCamelCase				) as f:
					snake_case_   		=			json.load(__UpperCamelCase				)
				return result


			@require_torch_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_multi_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=2				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)







			@require_torch_multi_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)
