

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]       )



class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	List[Any]						 =  StableDiffusionLatentUpscalePipeline
  __lowerCAmelCase     :	int						 =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      'height',
      'width',
      'cross_attention_kwargs',
      'negative_prompt_embeds',
      'prompt_embeds',
  }
  __lowerCAmelCase     :	Any						 =  PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
  __lowerCAmelCase     :	Optional[int]						 =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  __lowerCAmelCase     :	Any						 =  frozenset(
      []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  __lowerCAmelCase     :	Tuple						 =  frozenset([]       )

  __lowerCAmelCase     :	Tuple						 =  True

  @property
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						1
      SCREAMING_SNAKE_CASE_									=						4
      SCREAMING_SNAKE_CASE_									=						(16, 16)

      SCREAMING_SNAKE_CASE_									=						floats_tensor((batch_size, num_channels) + sizes						,		rng=random.Random(0)).to(_A)
      return image

  def 						lowerCAmelCase__     (					self):
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						UNetaDConditionModel(
          act_fn='gelu'						,		attention_head_dim=8						,		norm_num_groups=_A						,		block_out_channels=[32, 32, 64, 64]						,		time_cond_proj_dim=160						,		conv_in_kernel=1						,		conv_out_kernel=1						,		cross_attention_dim=32						,		down_block_types=(
              'KDownBlock2D',
              'KCrossAttnDownBlock2D',
              'KCrossAttnDownBlock2D',
              'KCrossAttnDownBlock2D',
          )						,		in_channels=8						,		mid_block_type=_A						,		only_cross_attention=_A						,		out_channels=5						,		resnet_time_scale_shift='scale_shift'						,		time_embedding_type='fourier'						,		timestep_post_act='gelu'						,		up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')						,		)
      SCREAMING_SNAKE_CASE_									=						AutoencoderKL(
          block_out_channels=[32, 32, 64, 64]						,		in_channels=3						,		out_channels=3						,		down_block_types=[
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
              'DownEncoderBlock2D',
          ]						,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,		latent_channels=4						,		)
      SCREAMING_SNAKE_CASE_									=						EulerDiscreteScheduler(prediction_type='sample')
      SCREAMING_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=1000						,		hidden_act='quick_gelu'						,		projection_dim=512						,		)
      SCREAMING_SNAKE_CASE_									=						CLIPTextModel(_A)
      SCREAMING_SNAKE_CASE_									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

      SCREAMING_SNAKE_CASE_									=						{
          'unet': model.eval(),
          'vae': vae.eval(),
          'scheduler': scheduler,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
      }

      return components

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=0):
      if str(_A).startswith('mps'):
          SCREAMING_SNAKE_CASE_									=						torch.manual_seed(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						torch.Generator(device=_A).manual_seed(_A)
      SCREAMING_SNAKE_CASE_									=						{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': self.dummy_image.cpu(),
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'cpu'

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						self.pipeline_class(**_A)
      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						pipe(**_A).images
      SCREAMING_SNAKE_CASE_									=						image[0, -3:, -3:, -1]

      self.assertEqual(image.shape						,		(1, 256, 256, 3))
      SCREAMING_SNAKE_CASE_									=						np.array(
          [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
      SCREAMING_SNAKE_CASE_									=						np.abs(image_slice.flatten() - expected_slice).max()
      self.assertLessEqual(_A						,		1E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_attention_slicing_forward_pass(expected_max_diff=7E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_cpu_offload_forward_pass(expected_max_diff=3E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_inference_batch_single_identical(expected_max_diff=7E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_save_load_local(expected_max_difference=3E-3)

  def 						lowerCAmelCase__     (					self):
      super().test_save_load_optional_components(expected_max_difference=3E-3)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						[
          'DDIMScheduler',
          'DDPMScheduler',
          'PNDMScheduler',
          'HeunDiscreteScheduler',
          'EulerAncestralDiscreteScheduler',
          'KDPM2DiscreteScheduler',
          'KDPM2AncestralDiscreteScheduler',
          'DPMSolverSDEScheduler',
      ]
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						self.pipeline_class(**_A)

      # make sure that PNDM does not need warm-up
      pipe.scheduler.register_to_config(skip_prk_steps=_A)

      pipe.to(_A)
      pipe.set_progress_bar_config(disable=_A)
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						2

      SCREAMING_SNAKE_CASE_									=						[]
      for scheduler_enum in KarrasDiffusionSchedulers:
          if scheduler_enum.name in skip_schedulers:
              # no sigma schedulers are not supported
              # no schedulers
              continue

          SCREAMING_SNAKE_CASE_									=						getattr(_A						,		scheduler_enum.name)
          SCREAMING_SNAKE_CASE_									=						scheduler_cls.from_config(pipe.scheduler.config)
          SCREAMING_SNAKE_CASE_									=						pipe(**_A)[0]
          outputs.append(_A)

      assert check_same_shape(_A)






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

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						torch.manual_seed(33)

      SCREAMING_SNAKE_CASE_									=						StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						,		torch_dtype=torch.floataa)
      pipe.to('cuda')

      SCREAMING_SNAKE_CASE_									=						StableDiffusionLatentUpscalePipeline.from_pretrained(
          'stabilityai/sd-x2-latent-upscaler'						,		torch_dtype=torch.floataa)
      upscaler.to('cuda')

      SCREAMING_SNAKE_CASE_									=						'a photo of an astronaut high resolution, unreal engine, ultra realistic'

      SCREAMING_SNAKE_CASE_									=						pipe(_A						,		generator=_A						,		output_type='latent').images

      SCREAMING_SNAKE_CASE_									=						upscaler(
          prompt=_A						,		image=_A						,		num_inference_steps=20						,		guidance_scale=0						,		generator=_A						,		output_type='np'						,		).images[0]

      SCREAMING_SNAKE_CASE_									=						load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy')
      assert np.abs((expected_image - image).mean()) < 5E-2


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						torch.manual_seed(33)

      SCREAMING_SNAKE_CASE_									=						StableDiffusionLatentUpscalePipeline.from_pretrained(
          'stabilityai/sd-x2-latent-upscaler'						,		torch_dtype=torch.floataa)
      upscaler.to('cuda')

      SCREAMING_SNAKE_CASE_									=						'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

      SCREAMING_SNAKE_CASE_									=						load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png')

      SCREAMING_SNAKE_CASE_									=						upscaler(
          prompt=_A						,		image=_A						,		num_inference_steps=20						,		guidance_scale=0						,		generator=_A						,		output_type='np'						,		).images[0]

      SCREAMING_SNAKE_CASE_									=						load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy')
      assert np.abs((expected_image - image).max()) < 5E-2






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase__  :      int			=    "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"



class       __snake_case  (    unittest.TestCase     , lowerCAmelCase__       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						load_tool('text-question-answering')
      self.tool.setup()
      SCREAMING_SNAKE_CASE_									=						load_tool('text-question-answering'						,		remote=_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tool(_A						,		'What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.remote_tool(_A						,		'What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tool(text=_A						,		question='What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.remote_tool(text=_A						,		question='What did Hugging Face do in April 2021?')
      self.assertEqual(_A						,		'launched the BigScience Research Workshop')


