
"""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 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		StableDiffusionXLImgaImgPipeline
		__lowercase							:  List[str]						   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		__lowercase							:  Union[str, Any]						   =		PipelineTesterMixin.required_optional_params - {'''latents'''}
		__lowercase							:  Dict						   =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		__lowercase							:  Union[str, Any]						   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
		__lowercase							:  str						   =		IMAGE_TO_IMAGE_IMAGE_PARAMS




		def 	snake_case_     (     self):
				torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = 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""")							,  attention_head_dim=(2, 4)							,  use_linear_projection=lowerCAmelCase__							,  addition_embed_type="""text_time"""							,  addition_time_embed_dim=8							,  transformer_layers_per_block=(1, 2)							,  projection_class_embeddings_input_dim=8_0							,  cross_attention_dim=6_4							,  )
				__SCREAMING_SNAKE_CASE            = EulerDiscreteScheduler(
				    beta_start=0.0_00_85							,  beta_end=0.0_12							,  steps_offset=1							,  beta_schedule="""scaled_linear"""							,  timestep_spacing="""leading"""							,  )
				torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = 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							,  sample_size=1_2_8							,  )
				torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = 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							,  hidden_act="""gelu"""							,  projection_dim=3_2							,  )
				__SCREAMING_SNAKE_CASE            = CLIPTextModel(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,  local_files_only=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = CLIPTextModelWithProjection(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							,  local_files_only=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = {
				    """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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0):
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, 3, 3_2, 3_2)							,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = image / 2 + 0.5
				if str(lowerCAmelCase__).startswith("""mps"""):
						__SCREAMING_SNAKE_CASE            = torch.manual_seed(lowerCAmelCase__)
				else:
						__SCREAMING_SNAKE_CASE            = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = {
				    """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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """cpu"""  # ensure determinism for the device-dependent torch.Generator
				__SCREAMING_SNAKE_CASE            = self.get_dummy_components()
				__SCREAMING_SNAKE_CASE            = StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = sd_pipe.to(lowerCAmelCase__)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 3_2, 3_2, 3)

				__SCREAMING_SNAKE_CASE            = np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65])

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




		def 	snake_case_     (     self):
				super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)




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




		def 	snake_case_     (     self):
				pass




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.get_dummy_components()
				__SCREAMING_SNAKE_CASE            = StableDiffusionXLImgaImgPipeline(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = sd_pipe.to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = sd_pipe.to(lowerCAmelCase__)
				sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

				# forward without prompt embeds
				__SCREAMING_SNAKE_CASE            = self.get_dummy_inputs(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 3 * ["""this is a negative prompt"""]
				__SCREAMING_SNAKE_CASE            = negative_prompt
				__SCREAMING_SNAKE_CASE            = 3 * [inputs["""prompt"""]]

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

				# forward with prompt embeds
				__SCREAMING_SNAKE_CASE            = self.get_dummy_inputs(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 3 * ["""this is a negative prompt"""]
				__SCREAMING_SNAKE_CASE            = 3 * [inputs.pop("""prompt""")]

				(
				    (
				    __SCREAMING_SNAKE_CASE
				)       ,(
				    __SCREAMING_SNAKE_CASE
				)       ,(
				    __SCREAMING_SNAKE_CASE
				)       ,(
				    __SCREAMING_SNAKE_CASE
				)       ,
				)            = sd_pipe.encode_prompt(lowerCAmelCase__							,  negative_prompt=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = sd_pipe(
				    **lowerCAmelCase__							,  prompt_embeds=lowerCAmelCase__							,  negative_prompt_embeds=lowerCAmelCase__							,  pooled_prompt_embeds=lowerCAmelCase__							,  negative_pooled_prompt_embeds=lowerCAmelCase__							,  )
				__SCREAMING_SNAKE_CASE            = 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 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





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




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__="cpu"							,  lowerCAmelCase__=torch.floataa							,  lowerCAmelCase__=0):
				__SCREAMING_SNAKE_CASE            = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = np.random.RandomState(lowerCAmelCase__).standard_normal((1, 4, 6_4, 6_4))
				__SCREAMING_SNAKE_CASE            = torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__							,  dtype=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = {
				    """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 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""")
				pipe.to(lowerCAmelCase__)
				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				__SCREAMING_SNAKE_CASE            = np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06])
				assert np.abs(image_slice - expected_slice).max() < 7E-3






"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''dandelin/vilt-b32-finetuned-vqa'''
		__lowercase							:  str						   =		(
		    '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
		    '''image containing the information, as well as a `question` which should be the question in English. It '''
		    '''returns a text that is the answer to the question.'''
		)
		__lowercase							:  Optional[int]						   =		'''image_qa'''
		__lowercase							:  Union[str, Any]						   =		AutoProcessor
		__lowercase							:  Optional[Any]						   =		AutoModelForVisualQuestionAnswering

		__lowercase							:  List[str]						   =		['''image''', '''text''']
		__lowercase							:  Optional[Any]						   =		['''text''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""vision"""])
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				return self.pre_processor(lowerCAmelCase__							,  lowerCAmelCase__							,  return_tensors="""pt""")




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				with torch.no_grad():
						return self.model(**lowerCAmelCase__).logits




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = outputs.argmax(-1).item()
				return self.model.config.idalabel[idx]




