


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'''






					A__     =					StableUnCLIPPipeline
					A__     =					TEXT_TO_IMAGE_PARAMS
					A__     =					TEXT_TO_IMAGE_BATCH_PARAMS
					A__     =					TEXT_TO_IMAGE_IMAGE_PARAMS
					A__     =					TEXT_TO_IMAGE_IMAGE_PARAMS

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

					def     lowerCamelCase__	(self       :		Any						)		->  str:




							"""simple docstring"""


							lowercase__	     =						32
							lowercase__	     =						embedder_hidden_size

							# prior components

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=_UpperCAmelCase ,   projection_dim=_UpperCAmelCase ,   intermediate_size=37 ,   layer_norm_eps=1E-0_5 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1000 ,   )						)

							torch.manual_seed(0						)
							lowercase__	     =						PriorTransformer(
							    num_attention_heads=2 ,   attention_head_dim=12 ,   embedding_dim=_UpperCAmelCase ,   num_layers=1 ,   )

							torch.manual_seed(0						)
							lowercase__	     =						DDPMScheduler(
							    variance_type="""fixed_small_log""" ,   prediction_type="""sample""" ,   num_train_timesteps=1000 ,   clip_sample=_UpperCAmelCase ,   clip_sample_range=5.0 ,   beta_schedule="""squaredcos_cap_v2""" ,   )

							# regular denoising components

							torch.manual_seed(0						)
							lowercase__	     =						StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase						)
							lowercase__	     =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

							torch.manual_seed(0						)
							lowercase__	     =						CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=_UpperCAmelCase ,   projection_dim=32 ,   intermediate_size=37 ,   layer_norm_eps=1E-0_5 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1000 ,   )						)

							torch.manual_seed(0						)
							lowercase__	     =						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=_UpperCAmelCase ,   layers_per_block=1 ,   upcast_attention=_UpperCAmelCase ,   use_linear_projection=_UpperCAmelCase ,   )

							torch.manual_seed(0						)
							lowercase__	     =						DDIMScheduler(
							    beta_schedule="""scaled_linear""" ,   beta_start=0.00_085 ,   beta_end=0.012 ,   prediction_type="""v_prediction""" ,   set_alpha_to_one=_UpperCAmelCase ,   steps_offset=1 ,   )

							torch.manual_seed(0						)
							lowercase__	     =						AutoencoderKL()

							lowercase__	     =						{
							    # 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     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[str]=0						)		->  List[str]:




							"""simple docstring"""


							if str(_UpperCAmelCase						).startswith("""mps"""						):
									lowercase__	     =						torch.manual_seed(_UpperCAmelCase						)
							else:
									lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
							lowercase__	     =						{
							    """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     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						torch_device == """cpu"""

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase						)






@slow
@require_torch_gpu
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[int]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""						)

							lowercase__	     =						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,   torch_dtype=torch.floataa						)
							pipe.to(_UpperCAmelCase						)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
							# 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()

							lowercase__	     =						torch.Generator(device="""cpu"""						).manual_seed(0						)
							lowercase__	     =						pipe("""anime turle""" ,   generator=_UpperCAmelCase ,   output_type="""np"""						)

							lowercase__	     =						output.images[0]

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

							assert_mean_pixel_difference(_UpperCAmelCase ,   _UpperCAmelCase						)


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




							"""simple docstring"""


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

							lowercase__	     =						StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,   torch_dtype=torch.floataa						)
							lowercase__	     =						pipe.to(_UpperCAmelCase						)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowercase__	     =						pipe(
							    """anime turtle""" ,   prior_num_inference_steps=2 ,   num_inference_steps=2 ,   output_type="""np""" ,   )

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





'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



