


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       StableUnCLIPPipeline
      __snake_case 	=       TEXT_TO_IMAGE_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_BATCH_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_IMAGE_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_IMAGE_PARAMS

      # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
      __snake_case 	=       False




      def 					_snake_case     (    self:	Union[str, Any]    ) ->    Tuple:
       __lowerCamelCase		:							List[str]		 =						32
       __lowerCamelCase		:							Union[str, Any]		 =						embedder_hidden_size

       # prior components

       torch.manual_seed(0    )
       __lowerCamelCase		:							int		 =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

       torch.manual_seed(0    )
       __lowerCamelCase		:							Dict		 =						CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0						,				eos_token_id=2						,				hidden_size=a						,				projection_dim=a						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				)    )

       torch.manual_seed(0    )
       __lowerCamelCase		:							str		 =						PriorTransformer(
           num_attention_heads=2						,				attention_head_dim=12						,				embedding_dim=a						,				num_layers=1						,				)

       torch.manual_seed(0    )
       __lowerCamelCase		:							Union[str, Any]		 =						DDPMScheduler(
           variance_type='fixed_small_log'						,				prediction_type='sample'						,				num_train_timesteps=1000						,				clip_sample=a						,				clip_sample_range=5.0						,				beta_schedule='squaredcos_cap_v2'						,				)

       # regular denoising components

       torch.manual_seed(0    )
       __lowerCamelCase		:							List[Any]		 =						StableUnCLIPImageNormalizer(embedding_dim=a    )
       __lowerCamelCase		:							Optional[int]		 =						DDPMScheduler(beta_schedule='squaredcos_cap_v2'    )

       torch.manual_seed(0    )
       __lowerCamelCase		:							Optional[int]		 =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

       torch.manual_seed(0    )
       __lowerCamelCase		:							List[str]		 =						CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0						,				eos_token_id=2						,				hidden_size=a						,				projection_dim=32						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				)    )

       torch.manual_seed(0    )
       __lowerCamelCase		:							Optional[int]		 =						UNetaDConditionModel(
           sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')						,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')						,				block_out_channels=(32, 64)						,				attention_head_dim=(2, 4)						,				class_embed_type='projection'						,				projection_class_embeddings_input_dim=embedder_projection_dim * 2						,				cross_attention_dim=a						,				layers_per_block=1						,				upcast_attention=a						,				use_linear_projection=a						,				)

       torch.manual_seed(0    )
       __lowerCamelCase		:							List[str]		 =						DDIMScheduler(
           beta_schedule='scaled_linear'						,				beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				prediction_type='v_prediction'						,				set_alpha_to_one=a						,				steps_offset=1						,				)

       torch.manual_seed(0    )
       __lowerCamelCase		:							List[str]		 =						AutoencoderKL()

       __lowerCamelCase		:							List[str]		 =						{
           # prior components
           'prior_tokenizer': prior_tokenizer,
           'prior_text_encoder': prior_text_encoder,
           'prior': prior,
           'prior_scheduler': prior_scheduler,
           # image noising components
           'image_normalizer': image_normalizer,
           'image_noising_scheduler': image_noising_scheduler,
           # regular denoising components
           'tokenizer': tokenizer,
           'text_encoder': text_encoder,
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
       }

       return components




      def 					_snake_case     (    self:	Optional[int]						,				a:	List[Any]						,				a:	int=0    ) ->    Optional[int]:
       if str(a    ).startswith('mps'    ):
        __lowerCamelCase		:							str		 =						torch.manual_seed(a    )
       else:
        __lowerCamelCase		:							List[str]		 =						torch.Generator(device=a    ).manual_seed(a    )
       __lowerCamelCase		:							List[Any]		 =						{
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'prior_num_inference_steps': 2,
           'output_type': 'numpy',
       }
       return inputs




      def 					_snake_case     (    self:	List[Any]    ) ->    List[str]:
       __lowerCamelCase		:							Tuple		 =						torch_device == 'cpu'

       self._test_attention_slicing_forward_pass(test_max_difference=a    )




      def 					_snake_case     (    self:	str    ) ->    Optional[int]:
       __lowerCamelCase		:							Tuple		 =						torch_device in ['cpu', 'mps']

       self._test_inference_batch_single_identical(test_max_difference=a    )



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




      '''simple docstring'''


      def 					_snake_case     (    self:	Tuple    ) ->    str:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




      def 					_snake_case     (    self:	str    ) ->    str:
       __lowerCamelCase		:							int		 =						load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'    )

       __lowerCamelCase		:							Union[str, Any]		 =						StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'						,				torch_dtype=torch.floataa    )
       pipe.to(a    )
       pipe.set_progress_bar_config(disable=a    )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       __lowerCamelCase		:							List[str]		 =						torch.Generator(device='cpu'    ).manual_seed(0    )
       __lowerCamelCase		:							Optional[Any]		 =						pipe('anime turle'						,				generator=a						,				output_type='np'    )

       __lowerCamelCase		:							Optional[int]		 =						output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a						,				a    )




      def 					_snake_case     (    self:	Union[str, Any]    ) ->    Optional[int]:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       __lowerCamelCase		:							int		 =						StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'						,				torch_dtype=torch.floataa    )
       __lowerCamelCase		:							List[str]		 =						pipe.to(a    )
       pipe.set_progress_bar_config(disable=a    )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       __lowerCamelCase		:							str		 =						pipe(
           'anime turtle'						,				prior_num_inference_steps=2						,				num_inference_steps=2						,				output_type='np'						,				)

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





import warnings

from .generation import TFGenerationMixin


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,				__UpperCamelCase ,				)

