import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   UnCLIPImageVariationPipeline
   lowerCAmelCase      :    Dict  			=   IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
   lowerCAmelCase      :    Optional[Any]  			=   IMAGE_VARIATION_BATCH_PARAMS

   lowerCAmelCase      :    Union[str, Any]  			=   [
       '''generator''',
       '''return_dict''',
       '''decoder_num_inference_steps''',
       '''super_res_num_inference_steps''',
   ]
   lowerCAmelCase      :    Optional[int]  			=   False



   @property
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return 32



   @property
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return 32



   @property
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    return self.time_input_dim



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    return self.time_input_dim * 4



   @property
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    return 100



   @property
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)
    return tokenizer



   @property
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[Any]							= CLIPTextConfig(
        bos_token_id=0		,  eos_token_id=2		,  hidden_size=self.text_embedder_hidden_size		,  projection_dim=self.text_embedder_hidden_size		,  intermediate_size=37		,  layer_norm_eps=1E-05		,  num_attention_heads=4		,  num_hidden_layers=5		,  pad_token_id=1		,  vocab_size=1_000		,  )
    return CLIPTextModelWithProjection(_lowercase							)



   @property
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     Tuple							= CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size		,  projection_dim=self.text_embedder_hidden_size		,  num_hidden_layers=5		,  num_attention_heads=4		,  image_size=32		,  intermediate_size=37		,  patch_size=1		,  )
    return CLIPVisionModelWithProjection(_lowercase							)



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    torch.manual_seed(0							)

    _UpperCamelCase:     List[str]							= {
        '''clip_embeddings_dim''': self.text_embedder_hidden_size,
        '''time_embed_dim''': self.time_embed_dim,
        '''cross_attention_dim''': self.cross_attention_dim,
    }

    _UpperCamelCase:     Optional[int]							= UnCLIPTextProjModel(**_lowercase							)
    return model



   @property
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    torch.manual_seed(0							)

    _UpperCamelCase:     Tuple							= {
        '''sample_size''': 32,
        # RGB in channels
        '''in_channels''': 3,
        # Out channels is double in channels because predicts mean and variance
        '''out_channels''': 6,
        '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
        '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
        '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
        '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
        '''layers_per_block''': 1,
        '''cross_attention_dim''': self.cross_attention_dim,
        '''attention_head_dim''': 4,
        '''resnet_time_scale_shift''': '''scale_shift''',
        '''class_embed_type''': '''identity''',
    }

    _UpperCamelCase:     List[Any]							= UNetaDConditionModel(**_lowercase							)
    return model



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return {
        "sample_size": 64,
        "layers_per_block": 1,
        "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
        "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
        "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
        "in_channels": 6,
        "out_channels": 3,
    }



   @property
   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    torch.manual_seed(0							)

    _UpperCamelCase:     Dict							= UNetaDModel(**self.dummy_super_res_kwargs							)
    return model



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    torch.manual_seed(1							)

    _UpperCamelCase:     List[str]							= UNetaDModel(**self.dummy_super_res_kwargs							)
    return model



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.dummy_decoder
    _UpperCamelCase:     Any							= self.dummy_text_proj
    _UpperCamelCase:     int							= self.dummy_text_encoder
    _UpperCamelCase:     Tuple							= self.dummy_tokenizer
    _UpperCamelCase:     int							= self.dummy_super_res_first
    _UpperCamelCase:     List[Any]							= self.dummy_super_res_last

    _UpperCamelCase:     Optional[Any]							= UnCLIPScheduler(
        variance_type='''learned_range'''		,  prediction_type='''epsilon'''		,  num_train_timesteps=1_000		,  )

    _UpperCamelCase:     Dict							= UnCLIPScheduler(
        variance_type='''fixed_small_log'''		,  prediction_type='''epsilon'''		,  num_train_timesteps=1_000		,  )

    _UpperCamelCase:     Any							= CLIPImageProcessor(crop_size=32		,  size=32							)

    _UpperCamelCase:     Dict							= self.dummy_image_encoder

    return {
        "decoder": decoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "text_proj": text_proj,
        "feature_extractor": feature_extractor,
        "image_encoder": image_encoder,
        "super_res_first": super_res_first,
        "super_res_last": super_res_last,
        "decoder_scheduler": decoder_scheduler,
        "super_res_scheduler": super_res_scheduler,
    }



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  List[str]		,  _lowercase					:  Optional[Any]=0		,  _lowercase					:  List[Any]=True							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Any							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     List[str]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)

    if pil_image:
     _UpperCamelCase:     List[Any]							= input_image * 0.5 + 0.5
     _UpperCamelCase:     Optional[int]							= input_image.clamp(0		,  1							)
     _UpperCamelCase:     Union[str, Any]							= input_image.cpu().permute(0		,  2		,  3		,  1							).float().numpy()
     _UpperCamelCase:     str							= DiffusionPipeline.numpy_to_pil(_lowercase							)[0]

    return {
        "image": input_image,
        "generator": generator,
        "decoder_num_inference_steps": 2,
        "super_res_num_inference_steps": 2,
        "output_type": "np",
    }



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= '''cpu'''

    _UpperCamelCase:     Any							= self.get_dummy_components()

    _UpperCamelCase:     int							= self.pipeline_class(**_lowercase							)
    _UpperCamelCase:     int							= pipe.to(_lowercase							)

    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Dict							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)

    _UpperCamelCase:     List[Any]							= pipe(**_lowercase							)
    _UpperCamelCase:     Optional[int]							= output.images

    _UpperCamelCase:     Optional[int]							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)

    _UpperCamelCase:     str							= pipe(
        **_lowercase		,  return_dict=_lowercase		,  )[0]

    _UpperCamelCase:     List[str]							= image[0, -3:, -3:, -1]
    _UpperCamelCase:     Optional[int]							= image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    _UpperCamelCase:     Optional[int]							= np.array(
        [
            0.9997,
            0.0002,
            0.9997,
            0.9997,
            0.9969,
            0.0023,
            0.9997,
            0.9969,
            0.9970,
        ]							)

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



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     str							= '''cpu'''

    _UpperCamelCase:     Optional[int]							= self.get_dummy_components()

    _UpperCamelCase:     int							= self.pipeline_class(**_lowercase							)
    _UpperCamelCase:     List[Any]							= pipe.to(_lowercase							)

    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     int							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)

    _UpperCamelCase:     List[Any]							= pipe(**_lowercase							)
    _UpperCamelCase:     Optional[int]							= output.images

    _UpperCamelCase:     int							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= pipe(
        **_lowercase		,  return_dict=_lowercase		,  )[0]

    _UpperCamelCase:     Union[str, Any]							= image[0, -3:, -3:, -1]
    _UpperCamelCase:     int							= image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)

    _UpperCamelCase:     str							= np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]							)

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



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''

    _UpperCamelCase:     Optional[int]							= self.get_dummy_components()

    _UpperCamelCase:     List[str]							= self.pipeline_class(**_lowercase							)
    _UpperCamelCase:     Optional[int]							= pipe.to(_lowercase							)

    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)
    _UpperCamelCase:     Tuple							= [
        pipeline_inputs['''image'''],
        pipeline_inputs['''image'''],
    ]

    _UpperCamelCase:     str							= pipe(**_lowercase							)
    _UpperCamelCase:     Dict							= output.images

    _UpperCamelCase:     Any							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)
    _UpperCamelCase:     List[Any]							= [
        tuple_pipeline_inputs['''image'''],
        tuple_pipeline_inputs['''image'''],
    ]

    _UpperCamelCase:     Optional[Any]							= pipe(
        **_lowercase		,  return_dict=_lowercase		,  )[0]

    _UpperCamelCase:     Any							= image[0, -3:, -3:, -1]
    _UpperCamelCase:     List[str]							= image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (2, 64, 64, 3)

    _UpperCamelCase:     Any							= np.array(
        [
            0.9997,
            0.9989,
            0.0008,
            0.0021,
            0.9960,
            0.0018,
            0.0014,
            0.0002,
            0.9933,
        ]							)

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



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= torch.device('''cpu'''							)




    class  __magic_name__  :





       """simple docstring"""




       lowerCAmelCase      :    Any  			=   1




    _UpperCamelCase:     int							= self.get_dummy_components()

    _UpperCamelCase:     Any							= self.pipeline_class(**_lowercase							)
    _UpperCamelCase:     int							= pipe.to(_lowercase							)

    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Tuple							= torch.Generator(device=_lowercase							).manual_seed(0							)
    _UpperCamelCase:     Tuple							= pipe.decoder.dtype
    _UpperCamelCase:     Any							= 1

    _UpperCamelCase:     Any							= (
        batch_size,
        pipe.decoder.config.in_channels,
        pipe.decoder.config.sample_size,
        pipe.decoder.config.sample_size,
    )
    _UpperCamelCase:     Dict							= pipe.prepare_latents(
        _lowercase		,  dtype=_lowercase		,  device=_lowercase		,  generator=_lowercase		,  latents=_lowercase		,  scheduler=DummyScheduler()							)

    _UpperCamelCase:     Tuple							= (
        batch_size,
        pipe.super_res_first.config.in_channels // 2,
        pipe.super_res_first.config.sample_size,
        pipe.super_res_first.config.sample_size,
    )
    _UpperCamelCase:     Tuple							= pipe.prepare_latents(
        _lowercase		,  dtype=_lowercase		,  device=_lowercase		,  generator=_lowercase		,  latents=_lowercase		,  scheduler=DummyScheduler()							)

    _UpperCamelCase:     Optional[Any]							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)

    _UpperCamelCase:     List[Any]							= pipe(
        **_lowercase		,  decoder_latents=_lowercase		,  super_res_latents=_lowercase							).images

    _UpperCamelCase:     List[Any]							= self.get_dummy_inputs(_lowercase		,  pil_image=_lowercase							)
    # Don't pass image, instead pass embedding
    _UpperCamelCase:     List[str]							= pipeline_inputs.pop('''image'''							)
    _UpperCamelCase:     Optional[int]							= pipe.image_encoder(_lowercase							).image_embeds

    _UpperCamelCase:     str							= pipe(
        **_lowercase		,  decoder_latents=_lowercase		,  super_res_latents=_lowercase		,  image_embeddings=_lowercase		,  ).images

    # make sure passing text embeddings manually is identical
    assert np.abs(img_out_a - img_out_a							).max() < 1E-4



   @skip_mps
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= torch_device == '''cpu'''

    # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
    _UpperCamelCase:     Optional[int]							= 1E-2

    self._test_attention_slicing_forward_pass(
        test_max_difference=_lowercase		,  expected_max_diff=_lowercase							)



   @skip_mps
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= torch_device == '''cpu'''
    _UpperCamelCase:     int							= True
    _UpperCamelCase:     Tuple							= [
        '''decoder_num_inference_steps''',
        '''super_res_num_inference_steps''',
    ]

    self._test_inference_batch_single_identical(
        test_max_difference=_lowercase		,  relax_max_difference=_lowercase		,  additional_params_copy_to_batched_inputs=_lowercase		,  )



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Any							= [
        '''decoder_num_inference_steps''',
        '''super_res_num_inference_steps''',
    ]

    if torch_device == "mps":
     # TODO: MPS errors with larger batch sizes
     _UpperCamelCase:     Optional[Any]							= [2, 3]
     self._test_inference_batch_consistent(
         batch_sizes=_lowercase		,  additional_params_copy_to_batched_inputs=_lowercase		,  )
    else:
     self._test_inference_batch_consistent(
         additional_params_copy_to_batched_inputs=_lowercase							)



   @skip_mps
   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    return super().test_dict_tuple_outputs_equivalent()



   @skip_mps
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    return super().test_save_load_local()



   @skip_mps
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    return super().test_save_load_optional_components()




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





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     int							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''							)
    _UpperCamelCase:     int							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''							)

    _UpperCamelCase:     Any							= UnCLIPImageVariationPipeline.from_pretrained(
        '''kakaobrain/karlo-v1-alpha-image-variations'''		,  torch_dtype=torch.floataa							)
    _UpperCamelCase:     Dict							= pipeline.to(_lowercase							)
    pipeline.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= torch.Generator(device='''cpu'''							).manual_seed(0							)
    _UpperCamelCase:     List[Any]							= pipeline(
        _lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )

    _UpperCamelCase:     Union[str, Any]							= output.images[0]

    assert image.shape == (256, 256, 3)

    assert_mean_pixel_difference(_lowercase		,  _lowercase		,  15							)
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()