"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	List[str]			=    StableUnCLIPImgaImgPipeline
					UpperCamelCase_ :	Dict			=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS
					UpperCamelCase_ :	Dict			=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					UpperCamelCase_ :	Optional[Any]			=    frozenset(
					    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					UpperCamelCase_ :	Tuple			=    frozenset([]			)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   32
							lowerCAmelCase__        =   embedder_hidden_size

							# image encoding components

							lowerCAmelCase__        =   CLIPImageProcessor(crop_size=32  ,						size=32     )

							torch.manual_seed(0     )
							lowerCAmelCase__        =   CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=snake_case__  ,						projection_dim=snake_case__  ,						num_hidden_layers=5  ,						num_attention_heads=4  ,						image_size=32  ,						intermediate_size=37  ,						patch_size=1  ,						)     )

							# regular denoising components

							torch.manual_seed(0     )
							lowerCAmelCase__        =   StableUnCLIPImageNormalizer(embedding_dim=snake_case__     )
							lowerCAmelCase__        =   DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""     )

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

							torch.manual_seed(0     )
							lowerCAmelCase__        =   CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0  ,						eos_token_id=2  ,						hidden_size=snake_case__  ,						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__        =   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=snake_case__  ,						layers_per_block=1  ,						upcast_attention=snake_case__  ,						use_linear_projection=snake_case__  ,						)

							torch.manual_seed(0     )
							lowerCAmelCase__        =   DDIMScheduler(
							    beta_schedule="""scaled_linear"""  ,						beta_start=0.0_0085  ,						beta_end=0.012  ,						prediction_type="""v_prediction"""  ,						set_alpha_to_one=snake_case__  ,						steps_offset=1  ,						)

							torch.manual_seed(0     )
							lowerCAmelCase__        =   AutoencoderKL()

							lowerCAmelCase__        =   {
							    # image encoding components
							    """feature_extractor""": feature_extractor,
							    """image_encoder""": image_encoder.eval(),
							    # image noising components
							    """image_normalizer""": image_normalizer.eval(),
							    """image_noising_scheduler""": image_noising_scheduler,
							    # regular denoising components
							    """tokenizer""": tokenizer,
							    """text_encoder""": text_encoder.eval(),
							    """unet""": unet.eval(),
							    """scheduler""": scheduler,
							    """vae""": vae.eval(),
							}

							return components




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     List[str]  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     Any=True     ):
							if str(snake_case__     ).startswith("""mps"""     ):
									lowerCAmelCase__        =   torch.manual_seed(snake_case__     )
							else:
									lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )

							lowerCAmelCase__        =   floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(snake_case__     )     ).to(snake_case__     )

							if pil_image:
									lowerCAmelCase__        =   input_image * 0.5 + 0.5
									lowerCAmelCase__        =   input_image.clamp(0  ,						1     )
									lowerCAmelCase__        =   input_image.cpu().permute(0  ,						2  ,						3  ,						1     ).float().numpy()
									lowerCAmelCase__        =   DiffusionPipeline.numpy_to_pil(snake_case__     )[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}




					@skip_mps
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.get_dummy_components()
							lowerCAmelCase__        =   StableUnCLIPImgaImgPipeline(**snake_case__     )
							lowerCAmelCase__        =   sd_pipe.to(snake_case__     )
							sd_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   self.get_dummy_inputs(snake_case__     )
							inputs.update({"""image_embeds""": None}     )
							lowerCAmelCase__        =   sd_pipe(**snake_case__     ).images
							lowerCAmelCase__        =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   torch_device in ["""cpu""", """mps"""]

							self._test_attention_slicing_forward_pass(test_max_difference=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=snake_case__     )




					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()  ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,						)
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""     )

							lowerCAmelCase__        =   load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""     )

							lowerCAmelCase__        =   StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-l-img2img"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							# 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__        =   torch.Generator(device="""cpu"""     ).manual_seed(0     )
							lowerCAmelCase__        =   pipe(snake_case__  ,						"""anime turle"""  ,						generator=snake_case__  ,						output_type="""np"""     )

							lowerCAmelCase__        =   output.images[0]

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

							assert_mean_pixel_difference(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""     )

							lowerCAmelCase__        =   load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""     )

							lowerCAmelCase__        =   StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""  ,						torch_dtype=torch.floataa     )
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							# 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__        =   torch.Generator(device="""cpu"""     ).manual_seed(0     )
							lowerCAmelCase__        =   pipe(snake_case__  ,						"""anime turle"""  ,						generator=snake_case__  ,						output_type="""np"""     )

							lowerCAmelCase__        =   output.images[0]

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

							assert_mean_pixel_difference(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""     )

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

							lowerCAmelCase__        =   StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""  ,						torch_dtype=torch.floataa     )
							lowerCAmelCase__        =   pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__        =   pipe(
							    snake_case__  ,						"""anime turtle"""  ,						num_inference_steps=2  ,						output_type="""np"""  ,						)

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





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




