import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()




@skip_mps
class  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			StableDiffusionModelEditingPipeline
 _lowerCamelCase    :						List[str]					     =			TEXT_TO_IMAGE_PARAMS
 _lowerCamelCase    :						Any					     =			TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase    :						Optional[Any]					     =			TEXT_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase    :						Optional[int]					     =			TEXT_TO_IMAGE_IMAGE_PARAMS

 def        __snake_case       (      self							:		Any	)			->				Any:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[Any]	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        _UpperCamelCase  :							Tuple	    =	DDIMScheduler()
        torch.manual_seed(0	)
        _UpperCamelCase  :							Tuple	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							List[str]	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							Dict	    =	CLIPTextModel(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

        _UpperCamelCase  :							List[Any]	    =	{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]=0	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[Any]	    =	torch.manual_seed(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	{
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs

 def        __snake_case       (      self							:		str	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Tuple	    =	self.get_dummy_components()
        _UpperCamelCase  :							Optional[Any]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							Tuple	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	sd_pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Any	    =	image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							int	    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							Optional[Any]	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[int]	    =	self.get_dummy_components()
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							str	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	'french fries'
        _UpperCamelCase  :							List[str]	    =	sd_pipe(**lowerCamelCase							,      negative_prompt=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	output.images
        _UpperCamelCase  :							List[Any]	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							str	    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		Any	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							str	    =	EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'	)
        _UpperCamelCase  :							Optional[int]	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							Tuple	    =	sd_pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Union[str, Any]	    =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        _UpperCamelCase  :							List[Any]	    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

 def        __snake_case       (      self							:		Optional[int]	)			->				List[str]:
        _UpperCamelCase  :							int	    =	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _UpperCamelCase  :							Optional[Any]	    =	self.get_dummy_components()
        _UpperCamelCase  :							Optional[Any]	    =	PNDMScheduler()
        _UpperCamelCase  :							Tuple	    =	StableDiffusionModelEditingPipeline(**lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	sd_pipe.to(lowerCamelCase	)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							List[str]	    =	self.get_dummy_inputs(lowerCamelCase	)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowerCamelCase	):
               _UpperCamelCase  :							Optional[int]	    =	sd_pipe(**lowerCamelCase	).images

 def        __snake_case       (      self							:		str	)			->				List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=5e-3	)

 def        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3	)







@slow
@require_torch_gpu
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any=0	)			->				Optional[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	torch.manual_seed(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	{
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

 def        __snake_case       (      self							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							Tuple	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Dict	    =	StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase							,      safety_checker=lowerCamelCase	)
        pipe.to(lowerCamelCase	)
        pipe.set_progress_bar_config(disable=lowerCamelCase	)
        pipe.enable_attention_slicing()

        _UpperCamelCase  :							Tuple	    =	self.get_inputs()
        _UpperCamelCase  :							Tuple	    =	pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Optional[int]	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        _UpperCamelCase  :							List[Any]	    =	np.array(
            [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-2

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'							,      'A pack of blue roses'	)

        _UpperCamelCase  :							List[Any]	    =	pipe(**lowerCamelCase	).images
        _UpperCamelCase  :							Optional[int]	    =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase  :							int	    =	'CompVis/stable-diffusion-v1-4'
        _UpperCamelCase  :							Optional[Any]	    =	DDIMScheduler.from_pretrained(lowerCamelCase							,      subfolder='scheduler'	)
        _UpperCamelCase  :							int	    =	StableDiffusionModelEditingPipeline.from_pretrained(
            lowerCamelCase							,      scheduler=lowerCamelCase							,      safety_checker=lowerCamelCase	)
        _UpperCamelCase  :							str	    =	pipe.to(lowerCamelCase	)
        pipe.set_progress_bar_config(disable=lowerCamelCase	)
        pipe.enable_attention_slicing(1	)
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase  :							Any	    =	self.get_inputs()
        _UpperCamelCase  :							Any	    =	pipe(**lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 1_0**9




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[Any]					     =			'flaubert'
 _lowerCamelCase    :						List[str]					     =			{
     'hidden_size': 'emb_dim',
     'num_attention_heads': 'n_heads',
     'num_hidden_layers': 'n_layers',
     'n_words': 'vocab_size',  # For backward compatibility
 }

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		Tuple=False							,      lowerCamelCase							:		str=0.0							,      lowerCamelCase							:		int=3_0_1_4_5							,      lowerCamelCase							:		List[str]=2_0_4_8							,      lowerCamelCase							:		List[str]=1_2							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Any=False							,      lowerCamelCase							:		Optional[int]=False							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		Optional[Any]=1							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		str=5_1_2							,      lowerCamelCase							:		Tuple=2_0_4_8**-0.5							,      lowerCamelCase							:		str=1e-12							,      lowerCamelCase							:		Any=0.0_2							,      lowerCamelCase							:		Dict=0							,      lowerCamelCase							:		Union[str, Any]=1							,      lowerCamelCase							:		List[Any]=2							,      lowerCamelCase							:		Union[str, Any]=3							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Dict="first"							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Any=None							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		Optional[Any]=5							,      lowerCamelCase							:		str=0							,      lowerCamelCase							:		Optional[int]=0							,      lowerCamelCase							:		List[str]=2							,      lowerCamelCase							:		Any=0							,      **lowerCamelCase							:		Tuple							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	pre_norm
        _UpperCamelCase  :							Optional[int]	    =	layerdrop
        _UpperCamelCase  :							Dict	    =	vocab_size
        _UpperCamelCase  :							Dict	    =	emb_dim
        _UpperCamelCase  :							int	    =	n_layers
        _UpperCamelCase  :							Tuple	    =	n_heads
        _UpperCamelCase  :							Tuple	    =	dropout
        _UpperCamelCase  :							Any	    =	attention_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	gelu_activation
        _UpperCamelCase  :							Dict	    =	sinusoidal_embeddings
        _UpperCamelCase  :							str	    =	causal
        _UpperCamelCase  :							str	    =	asm
        _UpperCamelCase  :							Tuple	    =	n_langs
        _UpperCamelCase  :							Optional[int]	    =	use_lang_emb
        _UpperCamelCase  :							Optional[Any]	    =	layer_norm_eps
        _UpperCamelCase  :							Optional[Any]	    =	bos_index
        _UpperCamelCase  :							Dict	    =	eos_index
        _UpperCamelCase  :							Union[str, Any]	    =	pad_index
        _UpperCamelCase  :							Union[str, Any]	    =	unk_index
        _UpperCamelCase  :							Optional[Any]	    =	mask_index
        _UpperCamelCase  :							Any	    =	is_encoder
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings
        _UpperCamelCase  :							Union[str, Any]	    =	embed_init_std
        _UpperCamelCase  :							Optional[int]	    =	init_std
        _UpperCamelCase  :							Any	    =	summary_type
        _UpperCamelCase  :							Any	    =	summary_use_proj
        _UpperCamelCase  :							Optional[int]	    =	summary_activation
        _UpperCamelCase  :							Union[str, Any]	    =	summary_proj_to_labels
        _UpperCamelCase  :							Dict	    =	summary_first_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	start_n_top
        _UpperCamelCase  :							Any	    =	end_n_top
        _UpperCamelCase  :							List[Any]	    =	mask_token_id
        _UpperCamelCase  :							str	    =	lang_id

        if "n_words" in kwargs:
               _UpperCamelCase  :							Tuple	    =	kwargs['n_words']

        super().__init__(pad_token_id=lowerCamelCase							,      bos_token_id=lowerCamelCase							,      **lowerCamelCase	)







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		str	)			->				Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCamelCase  :							int	    =	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               _UpperCamelCase  :							Dict	    =	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]	)



