





"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a__    :   str    						= logging.get_logger(__name__)

a__    :   Optional[int]    						= {
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   """gpt2"""
       snake_case__ :							Dict 							=   ["""past_key_values"""]
       snake_case__ :							List[str] 							=   {
           """hidden_size""": """n_embd""",
           """max_position_embeddings""": """n_positions""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=5_0257      ,    __SCREAMING_SNAKE_CASE=1024      ,    __SCREAMING_SNAKE_CASE=768      ,    __SCREAMING_SNAKE_CASE=12      ,    __SCREAMING_SNAKE_CASE=12      ,    __SCREAMING_SNAKE_CASE=None      ,    __SCREAMING_SNAKE_CASE="gelu_new"      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=1e-5      ,    __SCREAMING_SNAKE_CASE=0.02      ,    __SCREAMING_SNAKE_CASE="cls_index"      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=None      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=0.1      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=5_0256      ,    __SCREAMING_SNAKE_CASE=5_0256      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=False      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= vocab_size
        snake_case__										= n_positions
        snake_case__										= n_embd
        snake_case__										= n_layer
        snake_case__										= n_head
        snake_case__										= n_inner
        snake_case__										= activation_function
        snake_case__										= resid_pdrop
        snake_case__										= embd_pdrop
        snake_case__										= attn_pdrop
        snake_case__										= layer_norm_epsilon
        snake_case__										= initializer_range
        snake_case__										= summary_type
        snake_case__										= summary_use_proj
        snake_case__										= summary_activation
        snake_case__										= summary_first_dropout
        snake_case__										= summary_proj_to_labels
        snake_case__										= scale_attn_weights
        snake_case__										= use_cache
        snake_case__										= scale_attn_by_inverse_layer_idx
        snake_case__										= reorder_and_upcast_attn

        snake_case__										= bos_token_id
        snake_case__										= eos_token_id

        super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE      ,    eos_token_id=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = "default"      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = False      ,    ):






        '''simple docstring'''

        super().__init__(__SCREAMING_SNAKE_CASE      ,    task=__SCREAMING_SNAKE_CASE      ,    patching_specs=__SCREAMING_SNAKE_CASE      ,    use_past=__SCREAMING_SNAKE_CASE   )
        if not getattr(self._config      ,    "pad_token_id"      ,    __SCREAMING_SNAKE_CASE   ):
         # TODO: how to do that better?
         snake_case__										= 0

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
        if self.use_past:
         self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,    direction="inputs"   )
         snake_case__										= {0: "batch", 1: "past_sequence + sequence"}
        else:
         snake_case__										= {0: "batch", 1: "sequence"}

        return common_inputs

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return self._config.n_layer

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return self._config.n_head

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = -1      ,    __SCREAMING_SNAKE_CASE = -1      ,    __SCREAMING_SNAKE_CASE = False      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= super(__SCREAMING_SNAKE_CASE      ,    self   ).generate_dummy_inputs(
            __SCREAMING_SNAKE_CASE      ,    batch_size=__SCREAMING_SNAKE_CASE      ,    seq_length=__SCREAMING_SNAKE_CASE      ,    is_pair=__SCREAMING_SNAKE_CASE      ,    framework=__SCREAMING_SNAKE_CASE   )

        # We need to order the input in the way they appears in the forward()
        snake_case__										= OrderedDict({"input_ids": common_inputs["input_ids"]}   )

        # Need to add the past_keys
        if self.use_past:
         if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
         else:
          import torch

          snake_case__							,      snake_case__										= common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          snake_case__										= seqlen + 2
          snake_case__										= (
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          snake_case__										= [
              (torch.zeros(__SCREAMING_SNAKE_CASE   ), torch.zeros(__SCREAMING_SNAKE_CASE   )) for _ in range(self.num_layers   )
          ]

        snake_case__										= common_inputs["attention_mask"]
        if self.use_past:
         snake_case__										= ordered_inputs["attention_mask"].dtype
         snake_case__										= torch.cat(
             [ordered_inputs["attention_mask"], torch.ones(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )]      ,    dim=1   )

        return ordered_inputs

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return 13








"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= UNetaDConditionModel(
            block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=9      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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      ,    )
        torch.manual_seed(0   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

        snake_case__										= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= np.array(image.convert("RGB"   )   )
        snake_case__										= image[None].transpose(0      ,    3      ,    1      ,    2   )
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

        snake_case__										= floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((32, 32)   )

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= pipe.to("cpu"   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= "cpu"
        snake_case__										= self.get_dummy_inputs()
        inputs.pop("mask_image"   )
        snake_case__										= self.convert_to_pt(inputs.pop("image"   )   )
        snake_case__										= image.clamp(0      ,    1   ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**self.get_dummy_components()   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= mask_image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"   )

        snake_case__										= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.manual_seed(321   )
        snake_case__										= pipe(
            image=__SCREAMING_SNAKE_CASE      ,    mask_image=__SCREAMING_SNAKE_CASE      ,    example_image=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    guidance_scale=5.0      ,    num_inference_steps=50      ,    output_type="np"      ,    )

        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

