





'''simple docstring'''






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   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   UnCLIPImageVariationPipeline
   _UpperCamelCase       		=   IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
   _UpperCamelCase       		=   IMAGE_VARIATION_BATCH_PARAMS

   _UpperCamelCase       		=   [
       """generator""",
       """return_dict""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]
   _UpperCamelCase       		=   False


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return self.time_input_dim


   @property
   def 					snake_case				( self					):
       return self.time_input_dim * 4


   @property
   def 					snake_case				( self					):
       return 100


   @property
   def 					snake_case				( self					):
       A       :       str							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
       return tokenizer


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       Union[str, 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-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_000    ,   )
       return CLIPTextModelWithProjection(_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       Optional[Any]							=						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(_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)

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

       A       :       Union[str, Any]							=						UnCLIPTextProjModel(**_UpperCAmelCase					)
       return model


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)

       A       :       Union[str, Any]							=						{
           '''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''',
       }

       A       :       int							=						UNetaDConditionModel(**_UpperCAmelCase					)
       return model


   @property
   def 					snake_case				( self					):
       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 					snake_case				( self					):
       torch.manual_seed(0					)

       A       :       List[Any]							=						UNetaDModel(**self.dummy_super_res_kwargs					)
       return model


   @property
   def 					snake_case				( self					):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1					)

       A       :       Optional[Any]							=						UNetaDModel(**self.dummy_super_res_kwargs					)
       return model


   def 					snake_case				( self					):
       A       :       List[str]							=						self.dummy_decoder
       A       :       Tuple							=						self.dummy_text_proj
       A       :       Union[str, Any]							=						self.dummy_text_encoder
       A       :       Optional[int]							=						self.dummy_tokenizer
       A       :       Any							=						self.dummy_super_res_first
       A       :       Any							=						self.dummy_super_res_last

       A       :       List[str]							=						UnCLIPScheduler(
           variance_type='''learned_range'''    ,   prediction_type='''epsilon'''    ,   num_train_timesteps=1_000    ,   )

       A       :       Any							=						UnCLIPScheduler(
           variance_type='''fixed_small_log'''    ,   prediction_type='''epsilon'''    ,   num_train_timesteps=1_000    ,   )

       A       :       List[Any]							=						CLIPImageProcessor(crop_size=32    ,   size=32					)

       A       :       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 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=True					):
       A       :       List[Any]							=						floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
       if str(_UpperCAmelCase					).startswith('''mps'''					):
           A       :       Dict							=						torch.manual_seed(_UpperCAmelCase					)
       else:
           A       :       Union[str, Any]							=						torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)

       if pil_image:
           A       :       str							=						input_image * 0.5 + 0.5
           A       :       Dict							=						input_image.clamp(0    ,   1					)
           A       :       Union[str, Any]							=						input_image.cpu().permute(0    ,   2    ,   3    ,   1					).float().numpy()
           A       :       Tuple							=						DiffusionPipeline.numpy_to_pil(_UpperCAmelCase					)[0]

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


   def 					snake_case				( self					):
       A       :       Dict							=						'''cpu'''

       A       :       Union[str, Any]							=						self.get_dummy_components()

       A       :       Union[str, Any]							=						self.pipeline_class(**_UpperCAmelCase					)
       A       :       List[str]							=						pipe.to(_UpperCAmelCase					)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       str							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)

       A       :       List[str]							=						pipe(**_UpperCAmelCase					)
       A       :       Any							=						output.images

       A       :       int							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)

       A       :       Dict							=						pipe(
           **_UpperCAmelCase    ,   return_dict=_UpperCAmelCase    ,   )[0]

       A       :       Dict							=						image[0, -3:, -3:, -1]
       A       :       int							=						image_from_tuple[0, -3:, -3:, -1]

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

       A       :       str							=						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 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''cpu'''

       A       :       int							=						self.get_dummy_components()

       A       :       Optional[Any]							=						self.pipeline_class(**_UpperCAmelCase					)
       A       :       Dict							=						pipe.to(_UpperCAmelCase					)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       List[Any]							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)

       A       :       Any							=						pipe(**_UpperCAmelCase					)
       A       :       int							=						output.images

       A       :       Optional[int]							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)

       A       :       Dict							=						pipe(
           **_UpperCAmelCase    ,   return_dict=_UpperCAmelCase    ,   )[0]

       A       :       Dict							=						image[0, -3:, -3:, -1]
       A       :       Tuple							=						image_from_tuple[0, -3:, -3:, -1]

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

       A       :       Optional[int]							=						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 					snake_case				( self					):
       A       :       str							=						'''cpu'''

       A       :       str							=						self.get_dummy_components()

       A       :       int							=						self.pipeline_class(**_UpperCAmelCase					)
       A       :       List[str]							=						pipe.to(_UpperCAmelCase					)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       str							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)
       A       :       Any							=						[
           pipeline_inputs['''image'''],
           pipeline_inputs['''image'''],
       ]

       A       :       Optional[Any]							=						pipe(**_UpperCAmelCase					)
       A       :       Optional[Any]							=						output.images

       A       :       List[str]							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)
       A       :       List[Any]							=						[
           tuple_pipeline_inputs['''image'''],
           tuple_pipeline_inputs['''image'''],
       ]

       A       :       List[str]							=						pipe(
           **_UpperCAmelCase    ,   return_dict=_UpperCAmelCase    ,   )[0]

       A       :       List[Any]							=						image[0, -3:, -3:, -1]
       A       :       Optional[int]							=						image_from_tuple[0, -3:, -3:, -1]

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

       A       :       List[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 					snake_case				( self					):
       A       :       int							=						torch.device('''cpu'''					)


       class   _lowercase						:
          _UpperCamelCase       		=   1







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

       A       :       Optional[Any]							=						self.pipeline_class(**_UpperCAmelCase					)
       A       :       Tuple							=						pipe.to(_UpperCAmelCase					)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Optional[Any]							=						torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
       A       :       Optional[Any]							=						pipe.decoder.dtype
       A       :       int							=						1

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

       A       :       List[Any]							=						(
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       A       :       Optional[int]							=						pipe.prepare_latents(
           _UpperCAmelCase    ,   dtype=_UpperCAmelCase    ,   device=_UpperCAmelCase    ,   generator=_UpperCAmelCase    ,   latents=_UpperCAmelCase    ,   scheduler=DummyScheduler()					)

       A       :       str							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)

       A       :       Dict							=						pipe(
           **_UpperCAmelCase    ,   decoder_latents=_UpperCAmelCase    ,   super_res_latents=_UpperCAmelCase					).images

       A       :       str							=						self.get_dummy_inputs(_UpperCAmelCase    ,   pil_image=_UpperCAmelCase					)
       # Don't pass image, instead pass embedding
       A       :       Dict							=						pipeline_inputs.pop('''image'''					)
       A       :       Any							=						pipe.image_encoder(_UpperCAmelCase					).image_embeds

       A       :       Dict							=						pipe(
           **_UpperCAmelCase    ,   decoder_latents=_UpperCAmelCase    ,   super_res_latents=_UpperCAmelCase    ,   image_embeddings=_UpperCAmelCase    ,   ).images

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


   @skip_mps
   def 					snake_case				( self					):
       A       :       Any							=						torch_device == '''cpu'''

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

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


   @skip_mps
   def 					snake_case				( self					):
       A       :       Optional[int]							=						torch_device == '''cpu'''
       A       :       List[str]							=						True
       A       :       int							=						[
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

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


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						[
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

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


   @skip_mps
   def 					snake_case				( self					):
       return super().test_dict_tuple_outputs_equivalent()


   @skip_mps
   def 					snake_case				( self					):
       return super().test_save_load_local()


   @skip_mps
   def 					snake_case				( self					):
       return super().test_save_load_optional_components()







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


   def 					snake_case				( self					):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 					snake_case				( self					):
       A       :       Tuple							=						load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''					)
       A       :       List[str]							=						load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''					)

       A       :       Union[str, Any]							=						UnCLIPImageVariationPipeline.from_pretrained(
           '''kakaobrain/karlo-v1-alpha-image-variations'''    ,   torch_dtype=torch.floataa					)
       A       :       str							=						pipeline.to(_UpperCAmelCase					)
       pipeline.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Union[str, Any]							=						torch.Generator(device='''cpu'''					).manual_seed(0					)
       A       :       str							=						pipeline(
           _UpperCAmelCase    ,   generator=_UpperCAmelCase    ,   output_type='''np'''    ,   )

       A       :       Dict							=						output.images[0]

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

       assert_mean_pixel_difference(_UpperCAmelCase    ,   _UpperCAmelCase    ,   15					)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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