

import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 					lowercase		( _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UnCLIPPipeline
 __SCREAMING_SNAKE_CASE      							=       TEXT_TO_IMAGE_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
     """guidance_scale""",
     """prompt_embeds""",
     """cross_attention_kwargs""",
 }
 __SCREAMING_SNAKE_CASE      							=       TEXT_TO_IMAGE_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE      							=       [
     """generator""",
     """return_dict""",
     """prior_num_inference_steps""",
     """decoder_num_inference_steps""",
     """super_res_num_inference_steps""",
 ]
 __SCREAMING_SNAKE_CASE      							=       False




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  return 32




 @property
 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  return 32




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  return self.time_input_dim




 @property
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return self.time_input_dim * 4




 @property
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  return 100




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
  return tokenizer




 @property
 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  torch.manual_seed(0				)
  UpperCAmelCase__           =       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-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
  return CLIPTextModelWithProjection(__a				)




 @property
 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  torch.manual_seed(0				)

  UpperCAmelCase__           =       {
      'num_attention_heads': 2,
      'attention_head_dim': 12,
      'embedding_dim': self.text_embedder_hidden_size,
      'num_layers': 1,
  }

  UpperCAmelCase__           =       PriorTransformer(**__a				)
  return model




 @property
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  torch.manual_seed(0				)

  UpperCAmelCase__           =       {
      'clip_embeddings_dim': self.text_embedder_hidden_size,
      'time_embed_dim': self.time_embed_dim,
      'cross_attention_dim': self.cross_attention_dim,
  }

  UpperCAmelCase__           =       UnCLIPTextProjModel(**__a				)
  return model




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  torch.manual_seed(0				)

  UpperCAmelCase__           =       {
      '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__           =       UNetaDConditionModel(**__a				)
  return model




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """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 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  torch.manual_seed(0				)

  UpperCAmelCase__           =       UNetaDModel(**self.dummy_super_res_kwargs				)
  return model




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  torch.manual_seed(1				)

  UpperCAmelCase__           =       UNetaDModel(**self.dummy_super_res_kwargs				)
  return model




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       self.dummy_prior
  UpperCAmelCase__           =       self.dummy_decoder
  UpperCAmelCase__           =       self.dummy_text_proj
  UpperCAmelCase__           =       self.dummy_text_encoder
  UpperCAmelCase__           =       self.dummy_tokenizer
  UpperCAmelCase__           =       self.dummy_super_res_first
  UpperCAmelCase__           =       self.dummy_super_res_last

  UpperCAmelCase__           =       UnCLIPScheduler(
      variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample_range=5.0 , )

  UpperCAmelCase__           =       UnCLIPScheduler(
      variance_type='learned_range' , prediction_type='epsilon' , num_train_timesteps=1000 , )

  UpperCAmelCase__           =       UnCLIPScheduler(
      variance_type='fixed_small_log' , prediction_type='epsilon' , num_train_timesteps=1000 , )

  UpperCAmelCase__           =       {
      'prior': prior,
      'decoder': decoder,
      'text_proj': text_proj,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'super_res_first': super_res_first,
      'super_res_last': super_res_last,
      'prior_scheduler': prior_scheduler,
      'decoder_scheduler': decoder_scheduler,
      'super_res_scheduler': super_res_scheduler,
  }

  return components




 def 						UpperCamelCase__							(self , __a , __a=0				)				->      List[str]:






  """simple docstring"""





  if str(__a				).startswith('mps'				):
   UpperCAmelCase__           =       torch.manual_seed(__a				)
  else:
   UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(__a				)
  UpperCAmelCase__           =       {
      'prompt': 'horse',
      'generator': generator,
      'prior_num_inference_steps': 2,
      'decoder_num_inference_steps': 2,
      'super_res_num_inference_steps': 2,
      'output_type': 'numpy',
  }
  return inputs




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       'cpu'

  UpperCAmelCase__           =       self.get_dummy_components()

  UpperCAmelCase__           =       self.pipeline_class(**__a				)
  UpperCAmelCase__           =       pipe.to(__a				)

  pipe.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       pipe(**self.get_dummy_inputs(__a				)				)
  UpperCAmelCase__           =       output.images

  UpperCAmelCase__           =       pipe(
      **self.get_dummy_inputs(__a				) , return_dict=__a , )[0]

  UpperCAmelCase__           =       image[0, -3:, -3:, -1]
  UpperCAmelCase__           =       image_from_tuple[0, -3:, -3:, -1]

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

  UpperCAmelCase__           =       np.array(
      [
          0.99_97,
          0.99_88,
          0.00_28,
          0.99_97,
          0.99_84,
          0.99_65,
          0.00_29,
          0.99_86,
          0.00_25,
      ]				)

  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 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       torch.device('cpu'				)



  class 					lowercase		:



   '''simple docstring'''


   __SCREAMING_SNAKE_CASE      							=       1







  UpperCAmelCase__           =       self.get_dummy_components()

  UpperCAmelCase__           =       self.pipeline_class(**__a				)
  UpperCAmelCase__           =       pipe.to(__a				)

  UpperCAmelCase__           =       components['prior']
  UpperCAmelCase__           =       components['decoder']
  UpperCAmelCase__           =       components['super_res_first']
  UpperCAmelCase__           =       components['tokenizer']
  UpperCAmelCase__           =       components['text_encoder']

  UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(0				)
  UpperCAmelCase__           =       prior.dtype
  UpperCAmelCase__           =       1

  UpperCAmelCase__           =       (batch_size, prior.config.embedding_dim)
  UpperCAmelCase__           =       pipe.prepare_latents(
      __a , dtype=__a , device=__a , generator=__a , latents=__a , scheduler=DummyScheduler()				)
  UpperCAmelCase__           =       (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  UpperCAmelCase__           =       pipe.prepare_latents(
      __a , dtype=__a , device=__a , generator=__a , latents=__a , scheduler=DummyScheduler()				)

  UpperCAmelCase__           =       (
      batch_size,
      super_res_first.config.in_channels // 2,
      super_res_first.config.sample_size,
      super_res_first.config.sample_size,
  )
  UpperCAmelCase__           =       pipe.prepare_latents(
      __a , dtype=__a , device=__a , generator=__a , latents=__a , scheduler=DummyScheduler()				)

  pipe.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       'this is a prompt example'

  UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(0				)
  UpperCAmelCase__           =       pipe(
      [prompt] , generator=__a , prior_num_inference_steps=2 , decoder_num_inference_steps=2 , super_res_num_inference_steps=2 , prior_latents=__a , decoder_latents=__a , super_res_latents=__a , output_type='np' , )
  UpperCAmelCase__           =       output.images

  UpperCAmelCase__           =       tokenizer(
      __a , padding='max_length' , max_length=tokenizer.model_max_length , return_tensors='pt' , )
  UpperCAmelCase__           =       text_encoder(text_inputs.input_ids				)
  UpperCAmelCase__           =       text_inputs.attention_mask

  UpperCAmelCase__           =       torch.Generator(device=__a				).manual_seed(0				)
  UpperCAmelCase__           =       pipe(
      generator=__a , prior_num_inference_steps=2 , decoder_num_inference_steps=2 , super_res_num_inference_steps=2 , prior_latents=__a , decoder_latents=__a , super_res_latents=__a , text_model_output=__a , text_attention_mask=__a , output_type='np' , )[0]

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




 @skip_mps
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       torch_device == 'cpu'

  self._test_attention_slicing_forward_pass(test_max_difference=__a , expected_max_diff=0.01				)




 @skip_mps
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       torch_device == 'cpu'
  UpperCAmelCase__           =       True
  UpperCAmelCase__           =       [
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]

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




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       [
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]

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




 @skip_mps
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  return super().test_save_load_local()




 @skip_mps
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  return super().test_save_load_optional_components()







@nightly
class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/unclip/karlo_v1_alpha_horse_cpu.npy'				)

  UpperCAmelCase__           =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'				)
  pipeline.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       torch.manual_seed(0				)
  UpperCAmelCase__           =       pipeline(
      'horse' , num_images_per_prompt=1 , generator=__a , output_type='np' , )

  UpperCAmelCase__           =       output.images[0]

  assert image.shape == (256, 256, 3)
  assert np.abs(expected_image - image				).max() < 1E-1







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



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/unclip/karlo_v1_alpha_horse_fp16.npy'				)

  UpperCAmelCase__           =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha' , torch_dtype=torch.floataa				)
  UpperCAmelCase__           =       pipeline.to(__a				)
  pipeline.set_progress_bar_config(disable=__a				)

  UpperCAmelCase__           =       torch.Generator(device='cpu'				).manual_seed(0				)
  UpperCAmelCase__           =       pipeline(
      'horse' , generator=__a , output_type='np' , )

  UpperCAmelCase__           =       output.images[0]

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

  assert_mean_pixel_difference(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()

  UpperCAmelCase__           =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha' , torch_dtype=torch.floataa				)
  UpperCAmelCase__           =       pipe.to(__a				)
  pipe.set_progress_bar_config(disable=__a				)
  pipe.enable_attention_slicing()
  pipe.enable_sequential_cpu_offload()

  UpperCAmelCase__           =       pipe(
      'horse' , num_images_per_prompt=1 , prior_num_inference_steps=2 , decoder_num_inference_steps=2 , super_res_num_inference_steps=2 , output_type='np' , )

  UpperCAmelCase__           =       torch.cuda.max_memory_allocated()
  # make sure that less than 7 GB is allocated
  assert mem_bytes < 7 * 10**9








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

 _UpperCamelCase     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



