





'''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      (			UpperCamelCase__     ,			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							StableDiffusionXLImgaImgPipeline
 __lowercase  						=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 __lowercase  						=							PipelineTesterMixin.required_optional_params - {"""latents"""}
 __lowercase  						=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __lowercase  						=							IMAGE_TO_IMAGE_IMAGE_PARAMS
 __lowercase  						=							IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Optional[int]:
     torch.manual_seed(0				)
     A__							=     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		,			)
     A__							=     EulerDiscreteScheduler(
         beta_start=0.0_0_0_8_5		,			beta_end=0.0_1_2		,			steps_offset=1		,			beta_schedule="scaled_linear"		,			timestep_spacing="leading"		,			)
     torch.manual_seed(0				)
     A__							=     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				)
     A__							=     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		,			)
     A__							=     CLIPTextModel(lowercase_				)
     A__							=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		,			local_files_only=lowercase_				)

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

     A__							=     {
         "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 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :List[str]		,			lowercase_ :Optional[int]=0				)->				Optional[Any]:
     A__							=     floats_tensor((1, 3, 32, 32)		,			rng=random.Random(lowercase_				)				).to(lowercase_				)
     A__							=     image / 2 + 0.5
     if str(lowercase_				).startswith("mps"				):
         A__							=     torch.manual_seed(lowercase_				)
     else:
         A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     {
         "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": 0.7_5,
     }
     return inputs
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     A__							=     "cpu"  # ensure determinism for the device-dependent torch.Generator
     A__							=     self.get_dummy_components()
     A__							=     StableDiffusionXLImgaImgPipeline(**lowercase_				)
     A__							=     sd_pipe.to(lowercase_				)
     sd_pipe.set_progress_bar_config(disable=lowercase_				)

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

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

     A__							=     np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]				)

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)
 def 	UpperCAmelCase_				(							self :int				)->				Any:
     super().test_inference_batch_single_identical(expected_max_diff=3E-3				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     pass



 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     self.get_dummy_components()
     A__							=     StableDiffusionXLImgaImgPipeline(**lowercase_				)
     A__							=     sd_pipe.to(lowercase_				)
     A__							=     sd_pipe.to(lowercase_				)
     sd_pipe.set_progress_bar_config(disable=lowercase_				)

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

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

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

     (
         (
         A__
     ),      (
         A__
     ),      (
         A__
     ),      (
         A__
     ),      
     )							=     sd_pipe.encode_prompt(lowercase_		,			negative_prompt=lowercase_				)

     A__							=     sd_pipe(
         **lowercase_		,			prompt_embeds=lowercase_		,			negative_prompt_embeds=lowercase_		,			pooled_prompt_embeds=lowercase_		,			negative_pooled_prompt_embeds=lowercase_		,			)
     A__							=     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 	UpperCAmelCase_				(							self :Any				)->				List[Any]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :List[str]		,			lowercase_ :Optional[Any]="cpu"		,			lowercase_ :int=torch.floataa		,			lowercase_ :List[Any]=0				)->				int:
     A__							=     torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
     A__							=     np.random.RandomState(lowercase_				).standard_normal((1, 4, 64, 64)				)
     A__							=     torch.from_numpy(lowercase_				).to(device=lowercase_		,			dtype=lowercase_				)
     A__							=     {
         "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 	UpperCAmelCase_				(							self :List[str]				)->				Optional[int]:
     A__							=     DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"				)
     pipe.to(lowercase_				)
     pipe.set_progress_bar_config(disable=lowercase_				)

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

     assert image.shape == (1, 5_12, 5_12, 3)
     A__							=     np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]				)
     assert np.abs(image_slice - expected_slice				).max() < 7E-3










'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10				),
             MaxTimeCriteria(max_time=0.1				),
         ]				)

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

     with self.assertWarns(lowercase_				):
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			11				)

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



