

"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class      _UpperCAmelCase   (							_lowerCAmelCase		,    _lowerCAmelCase		,    unittest.TestCase       ):
	a__		: Optional[Any]					=					StableDiffusionXLImgaImgPipeline
	a__		: Any					=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
	a__		: str					=					PipelineTesterMixin.required_optional_params - {"latents"}
	a__		: Optional[int]					=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	a__		: str					=					IMAGE_TO_IMAGE_IMAGE_PARAMS
	a__		: str					=					IMAGE_TO_IMAGE_IMAGE_PARAMS



	def        a  (  self     :       Union[str, Any]						):
		torch.manual_seed(0						)
		__UpperCAmelCase      		= 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''')			,    attention_head_dim=(2, 4)			,    use_linear_projection=_lowercase			,    addition_embed_type='''text_time'''			,    addition_time_embed_dim=8			,    transformer_layers_per_block=(1, 2)			,    projection_class_embeddings_input_dim=80			,    cross_attention_dim=64			,    )
		__UpperCAmelCase      		= EulerDiscreteScheduler(
		    beta_start=0.00_085			,    beta_end=0.012			,    steps_offset=1			,    beta_schedule='''scaled_linear'''			,    timestep_spacing='''leading'''			,    )
		torch.manual_seed(0						)
		__UpperCAmelCase      		= 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			,    sample_size=1_28			,    )
		torch.manual_seed(0						)
		__UpperCAmelCase      		= 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			,    hidden_act='''gelu'''			,    projection_dim=32			,    )
		__UpperCAmelCase      		= CLIPTextModel(_lowercase						)
		__UpperCAmelCase      		= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			,    local_files_only=_lowercase						)

		__UpperCAmelCase      		= CLIPTextModelWithProjection(_lowercase						)
		__UpperCAmelCase      		= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			,    local_files_only=_lowercase						)

		__UpperCAmelCase      		= {
		    '''unet''': unet,
		    '''scheduler''': scheduler,
		    '''vae''': vae,
		    '''text_encoder''': text_encoder,
		    '''tokenizer''': tokenizer,
		    '''text_encoder_2''': text_encoder_a,
		    '''tokenizer_2''': tokenizer_a,
		    # "safety_checker": None,
		    # "feature_extractor": None,
		}
		return components



	def        a  (  self     :       List[Any]			,    _lowercase     :       List[str]			,    _lowercase     :       Any=0						):
		__UpperCAmelCase      		= floats_tensor((1, 3, 32, 32)			,    rng=random.Random(_lowercase						)						).to(_lowercase						)
		__UpperCAmelCase      		= image / 2 + 0.5
		if str(_lowercase						).startswith('''mps'''						):
			__UpperCAmelCase      		= torch.manual_seed(_lowercase						)
		else:
			__UpperCAmelCase      		= torch.Generator(device=_lowercase						).manual_seed(_lowercase						)
		__UpperCAmelCase      		= {
		    '''prompt''': '''A painting of a squirrel eating a burger''',
		    '''image''': image,
		    '''generator''': generator,
		    '''num_inference_steps''': 2,
		    '''guidance_scale''': 5.0,
		    '''output_type''': '''numpy''',
		    '''strength''': 0.75,
		}
		return inputs



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
		__UpperCAmelCase      		= self.get_dummy_components()
		__UpperCAmelCase      		= StableDiffusionXLImgaImgPipeline(**_lowercase						)
		__UpperCAmelCase      		= sd_pipe.to(_lowercase						)
		sd_pipe.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= self.get_dummy_inputs(_lowercase						)
		__UpperCAmelCase      		= sd_pipe(**_lowercase						).images
		__UpperCAmelCase      		= image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)

		__UpperCAmelCase      		= np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]						)

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



	def        a  (  self     :       List[Any]						):
		super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)



	def        a  (  self     :       int						):
		super().test_inference_batch_single_identical(expected_max_diff=3E-3						)



	def        a  (  self     :       Optional[int]						):
		pass



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= self.get_dummy_components()
		__UpperCAmelCase      		= StableDiffusionXLImgaImgPipeline(**_lowercase						)
		__UpperCAmelCase      		= sd_pipe.to(_lowercase						)
		__UpperCAmelCase      		= sd_pipe.to(_lowercase						)
		sd_pipe.set_progress_bar_config(disable=_lowercase						)

		# forward without prompt embeds
		__UpperCAmelCase      		= self.get_dummy_inputs(_lowercase						)
		__UpperCAmelCase      		= 3 * ['''this is a negative prompt''']
		__UpperCAmelCase      		= negative_prompt
		__UpperCAmelCase      		= 3 * [inputs['''prompt''']]

		__UpperCAmelCase      		= sd_pipe(**_lowercase						)
		__UpperCAmelCase      		= output.images[0, -3:, -3:, -1]

		# forward with prompt embeds
		__UpperCAmelCase      		= self.get_dummy_inputs(_lowercase						)
		__UpperCAmelCase      		= 3 * ['''this is a negative prompt''']
		__UpperCAmelCase      		= 3 * [inputs.pop('''prompt'''						)]

		(
		    (
		    __UpperCAmelCase
		)		,							(
		    __UpperCAmelCase
		)		,							(
		    __UpperCAmelCase
		)		,							(
		    __UpperCAmelCase
		)		,							
		)      		= sd_pipe.encode_prompt(_lowercase			,    negative_prompt=_lowercase						)

		__UpperCAmelCase      		= sd_pipe(
		    **_lowercase			,    prompt_embeds=_lowercase			,    negative_prompt_embeds=_lowercase			,    pooled_prompt_embeds=_lowercase			,    negative_pooled_prompt_embeds=_lowercase			,    )
		__UpperCAmelCase      		= output.images[0, -3:, -3:, -1]

		# make sure that it's equal
		assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1E-4






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



	def        a  (  self     :       Tuple						):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Optional[Any]			,    _lowercase     :       int="cpu"			,    _lowercase     :       List[str]=torch.floataa			,    _lowercase     :       Any=0						):
		__UpperCAmelCase      		= torch.Generator(device=_lowercase						).manual_seed(_lowercase						)
		__UpperCAmelCase      		= np.random.RandomState(_lowercase						).standard_normal((1, 4, 64, 64)						)
		__UpperCAmelCase      		= torch.from_numpy(_lowercase						).to(device=_lowercase			,    dtype=_lowercase						)
		__UpperCAmelCase      		= {
		    '''prompt''': '''a photograph of an astronaut riding a horse''',
		    '''latents''': latents,
		    '''generator''': generator,
		    '''num_inference_steps''': 3,
		    '''guidance_scale''': 7.5,
		    '''output_type''': '''numpy''',
		}
		return inputs



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''						)
		pipe.to(_lowercase						)
		pipe.set_progress_bar_config(disable=_lowercase						)

		__UpperCAmelCase      		= self.get_inputs(_lowercase						)
		__UpperCAmelCase      		= pipe(**_lowercase						).images
		__UpperCAmelCase      		= image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 5_12, 5_12, 3)
		__UpperCAmelCase      		= np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]						)
		assert np.abs(image_slice - expected_slice						).max() < 7E-3







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    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_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



