



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   :	Optional[int]				)->List[Any]:
      _lowerCAmelCase 			=  [tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]				)





class 		UpperCAmelCase						(						snake_case_       ,snake_case_       ,snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     StableDiffusionLatentUpscalePipeline
   SCREAMING_SNAKE_CASE__							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       '''height''',
       '''width''',
       '''cross_attention_kwargs''',
       '''negative_prompt_embeds''',
       '''prompt_embeds''',
   }
   SCREAMING_SNAKE_CASE__							=     PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
   SCREAMING_SNAKE_CASE__							=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__							=     frozenset(
       []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   SCREAMING_SNAKE_CASE__							=     frozenset([]				)

   SCREAMING_SNAKE_CASE__							=     True





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  1
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  (16, 16)

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





   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(0			)
         _lowerCAmelCase 			=  UNetaDConditionModel(
             act_fn='''gelu'''       ,     attention_head_dim=8       ,     norm_num_groups=_lowerCAmelCase       ,     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=_lowerCAmelCase       ,     only_cross_attention=_lowerCAmelCase       ,     out_channels=5       ,     resnet_time_scale_shift='''scale_shift'''       ,     time_embedding_type='''fourier'''       ,     timestep_post_act='''gelu'''       ,     up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')       ,     )
         _lowerCAmelCase 			=  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       ,     )
         _lowerCAmelCase 			=  EulerDiscreteScheduler(prediction_type='''sample'''			)
         _lowerCAmelCase 			=  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=1_000       ,     hidden_act='''quick_gelu'''       ,     projection_dim=512       ,     )
         _lowerCAmelCase 			=  CLIPTextModel(_lowerCAmelCase			)
         _lowerCAmelCase 			=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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

         return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
         if str(_lowerCAmelCase			).startswith('''mps'''			):
               _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
         _lowerCAmelCase 			=  {
             '''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			):
         _lowerCAmelCase 			=  '''cpu'''

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

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

         self.assertEqual(image.shape       ,     (1, 256, 256, 3)			)
         _lowerCAmelCase 			=  np.array(
             [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]			)
         _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
         self.assertLessEqual(_lowerCAmelCase       ,     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			):
         _lowerCAmelCase 			=  [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         _lowerCAmelCase 			=  self.get_dummy_components()
         _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)

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

         pipe.to(_lowerCAmelCase			)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
         _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
         _lowerCAmelCase 			=  2

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

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

         assert check_same_shape(_lowerCAmelCase			)






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





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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  torch.manual_seed(33			)

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

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

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

         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     output_type='''latent'''			).images

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

         _lowerCAmelCase 			=  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			):
         _lowerCAmelCase 			=  torch.manual_seed(33			)

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

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

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

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

         _lowerCAmelCase 			=  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




# Copyright 2022 The HuggingFace Team and The OpenBMB 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)