






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__UpperCamelCase					:		str	  =						logging.get_logger(__name__)


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Any       ,	UpperCamelCase:      int       ,	UpperCamelCase:      str						)		->		int:
											snake_case__									=     question_encoder
											snake_case__									=     generator
											snake_case__									=     self.question_encoder
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      Optional[Any]						)		->		Optional[int]:



											if os.path.isfile(__A						):
															raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
											os.makedirs(__A       ,	exist_ok=__A						)
											snake_case__									=     os.path.join(__A       ,	'question_encoder_tokenizer'						)
											snake_case__									=     os.path.join(__A       ,	'generator_tokenizer'						)
											self.question_encoder.save_pretrained(__A						)
											self.generator.save_pretrained(__A						)
							@classmethod
							def lowerCAmelCase_ (     cls:      List[str]       ,	UpperCamelCase:      Optional[int]       ,	**UpperCamelCase:      str						)		->		Optional[int]:
											from ..auto.tokenization_auto import AutoTokenizer

											snake_case__									=     kwargs.pop('config'       ,	__A						)

											if config is None:
															snake_case__									=     RagConfig.from_pretrained(__A						)

											snake_case__									=     AutoTokenizer.from_pretrained(
											    __A       ,	config=config.question_encoder       ,	subfolder='question_encoder_tokenizer'						)
											snake_case__									=     AutoTokenizer.from_pretrained(
											    __A       ,	config=config.generator       ,	subfolder='generator_tokenizer'						)
											return cls(question_encoder=__A       ,	generator=__A						)
							def __call__(     self:      Optional[int]       ,	*UpperCamelCase:      List[Any]       ,	**UpperCamelCase:      str						)		->		Optional[Any]:
											return self.current_tokenizer(*__A       ,	**__A						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	*UpperCamelCase:      Optional[Any]       ,	**UpperCamelCase:      List[Any]						)		->		Optional[int]:
											return self.generator.batch_decode(*__A       ,	**__A						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	*UpperCamelCase:      int       ,	**UpperCamelCase:      List[Any]						)		->		List[Any]:
											return self.generator.decode(*__A       ,	**__A						)
							def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											snake_case__									=     self.question_encoder
							def lowerCAmelCase_ (     self:      int						)		->		Dict:
											snake_case__									=     self.generator


							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Optional[List[str]] = None       ,	UpperCamelCase:      Optional[int] = None       ,	UpperCamelCase:      Optional[int] = None       ,	UpperCamelCase:      str = "longest"       ,	UpperCamelCase:      str = None       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		BatchEncoding:
											warnings.warn(
											    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
											    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
											    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
											    'details'       ,	__A       ,	)
											if max_length is None:
															snake_case__									=     self.current_tokenizer.model_max_length
											snake_case__									=     self(
											    __A       ,	add_special_tokens=__A       ,	return_tensors=__A       ,	max_length=__A       ,	padding=__A       ,	truncation=__A       ,	**__A       ,	)
											if tgt_texts is None:
															return model_inputs
											# Process tgt_texts
											if max_target_length is None:
															snake_case__									=     self.current_tokenizer.model_max_length
											snake_case__									=     self(
											    text_target=__A       ,	add_special_tokens=__A       ,	return_tensors=__A       ,	padding=__A       ,	max_length=__A       ,	truncation=__A       ,	**__A       ,	)
											snake_case__									=     labels['input_ids']
											return model_inputs











import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,	cross_attention_dim=32       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,	latent_channels=4       ,	)
											torch.manual_seed(0						)
											snake_case__									=     CLIPTextConfig(
											    bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




