




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()



class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  StableDiffusionXLImgaImgPipeline
     lowerCAmelCase    :	Dict       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	List[str]       		=  PipelineTesterMixin.required_optional_params - {'latents'}
     lowerCAmelCase    :	str       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Dict       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     lowerCAmelCase    :	List[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     def   lowerCamelCase_       (     self       :      Tuple    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					UNetaDConditionModel(
             block_out_channels=(32, 64) ,   layers_per_block=2 ,   sample_size=32 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,   attention_head_dim=(2, 4) ,   use_linear_projection=__a ,   addition_embed_type='''text_time''' ,   addition_time_embed_dim=8 ,   transformer_layers_per_block=(1, 2) ,   projection_class_embeddings_input_dim=80 ,   cross_attention_dim=64 ,   )
         UpperCAmelCase_										=					EulerDiscreteScheduler(
             beta_start=0.00_085 ,   beta_end=0.012 ,   steps_offset=1 ,   beta_schedule='''scaled_linear''' ,   timestep_spacing='''leading''' ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					AutoencoderKL(
             block_out_channels=[32, 64] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=4 ,   sample_size=1_28 ,   )
         torch.manual_seed(0    )
         UpperCAmelCase_										=					CLIPTextConfig(
             bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=32 ,   intermediate_size=37 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=10_00 ,   hidden_act='''gelu''' ,   projection_dim=32 ,   )
         UpperCAmelCase_										=					CLIPTextModel(__a    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,   local_files_only=__a    )

         UpperCAmelCase_										=					CLIPTextModelWithProjection(__a    )
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,   local_files_only=__a    )

         UpperCAmelCase_										=					{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''text_encoder_2''': text_encoder_a,
             '''tokenizer_2''': tokenizer_a,
             # "safety_checker": None,
             # "feature_extractor": None,
         }
         return components
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      int ,   __snake_case       :      Any=0    ):
         UpperCAmelCase_										=					floats_tensor((1, 3, 32, 32) ,   rng=random.Random(__a    )    ).to(__a    )
         UpperCAmelCase_										=					image / 2 + 0.5
         if str(__a    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__a    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__a    ).manual_seed(__a    )
         UpperCAmelCase_										=					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 5.0,
             '''output_type''': '''numpy''',
             '''strength''': 0.75,
         }
         return inputs
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					StableDiffusionXLImgaImgPipeline(**__a    )
         UpperCAmelCase_										=					sd_pipe.to(__a    )
         sd_pipe.set_progress_bar_config(disable=__a    )

         UpperCAmelCase_										=					self.get_dummy_inputs(__a    )
         UpperCAmelCase_										=					sd_pipe(**__a    ).images
         UpperCAmelCase_										=					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)

         UpperCAmelCase_										=					np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
     def   lowerCamelCase_       (     self       :      int    ):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         pass




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					StableDiffusionXLImgaImgPipeline(**__a    )
         UpperCAmelCase_										=					sd_pipe.to(__a    )
         UpperCAmelCase_										=					sd_pipe.to(__a    )
         sd_pipe.set_progress_bar_config(disable=__a    )

         # forward without prompt embeds
         UpperCAmelCase_										=					self.get_dummy_inputs(__a    )
         UpperCAmelCase_										=					3 * ['''this is a negative prompt''']
         UpperCAmelCase_										=					negative_prompt
         UpperCAmelCase_										=					3 * [inputs['''prompt''']]

         UpperCAmelCase_										=					sd_pipe(**__a    )
         UpperCAmelCase_										=					output.images[0, -3:, -3:, -1]

         # forward with prompt embeds
         UpperCAmelCase_										=					self.get_dummy_inputs(__a    )
         UpperCAmelCase_										=					3 * ['''this is a negative prompt''']
         UpperCAmelCase_										=					3 * [inputs.pop('''prompt'''    )]

         (
             (
             UpperCAmelCase_  
         )			,							(
             UpperCAmelCase_  
         )			,							(
             UpperCAmelCase_  
         )			,							(
             UpperCAmelCase_  
         )			,							  
         )										=					sd_pipe.encode_prompt(__a ,   negative_prompt=__a    )

         UpperCAmelCase_										=					sd_pipe(
             **__a ,   prompt_embeds=__a ,   negative_prompt_embeds=__a ,   pooled_prompt_embeds=__a ,   negative_pooled_prompt_embeds=__a ,   )
         UpperCAmelCase_										=					output.images[0, -3:, -3:, -1]

         # make sure that it's equal
         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4



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

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Any    ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      str ,   __snake_case       :      List[Any]="cpu" ,   __snake_case       :      Optional[int]=torch.floataa ,   __snake_case       :      List[Any]=0    ):
         UpperCAmelCase_										=					torch.Generator(device=__a    ).manual_seed(__a    )
         UpperCAmelCase_										=					np.random.RandomState(__a    ).standard_normal((1, 4, 64, 64)    )
         UpperCAmelCase_										=					torch.from_numpy(__a    ).to(device=__a ,   dtype=__a    )
         UpperCAmelCase_										=					{
             '''prompt''': '''a photograph of an astronaut riding a horse''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs




     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''    )
         pipe.to(__a    )
         pipe.set_progress_bar_config(disable=__a    )

         UpperCAmelCase_										=					self.get_inputs(__a    )
         UpperCAmelCase_										=					pipe(**__a    ).images
         UpperCAmelCase_										=					image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         UpperCAmelCase_										=					np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]    )
         assert np.abs(image_slice - expected_slice    ).max() < 7E-3











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					predicted_token_bpe.replace('''\u2581'''	,					''' '''				)
        if " {0}".format(__UpperCamelCase				) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(__UpperCamelCase				)	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(__UpperCamelCase	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
    return topk_filled_outputs


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


print(fill_mask(masked_input, model, tokenizer, topk=3))





