'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase			):







   """simple docstring"""

   @register_to_config
   def __init__(      self      :			List[str], UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			float, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			str, UpperCamelCase__      :			bool = False, )  ->			str:
        super().__init__()

        _A    		=  nn.Embedding(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  nn.Embedding(UpperCamelCase__, UpperCamelCase__     )
        _A    		=  False

        _A    		=  nn.Dropout(p=UpperCamelCase__     )

        _A    		=  TaConfig(
            vocab_size=UpperCamelCase__, d_model=UpperCamelCase__, num_heads=UpperCamelCase__, d_kv=UpperCamelCase__, d_ff=UpperCamelCase__, dropout_rate=UpperCamelCase__, feed_forward_proj=UpperCamelCase__, is_decoder=UpperCamelCase__, is_encoder_decoder=UpperCamelCase__, )

        _A    		=  nn.ModuleList()
        for lyr_num in range(UpperCamelCase__     ):
             _A    		=  TaBlock(UpperCamelCase__     )
             self.encoders.append(UpperCamelCase__     )

        _A    		=  TaLayerNorm(UpperCamelCase__     )
        _A    		=  nn.Dropout(p=UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			str, UpperCamelCase__      :			List[str]     )  ->			Dict:
        _A    		=  self.token_embedder(UpperCamelCase__     )

        _A    		=  encoder_input_tokens.shape[1]
        _A    		=  torch.arange(UpperCamelCase__, device=encoder_input_tokens.device     )
        x += self.position_encoding(UpperCamelCase__     )

        _A    		=  self.dropout_pre(UpperCamelCase__     )

        # inverted the attention mask
        _A    		=  encoder_input_tokens.size()
        _A    		=  self.get_extended_attention_mask(UpperCamelCase__, UpperCamelCase__     )

        for lyr in self.encoders:
             _A    		=  lyr(UpperCamelCase__, UpperCamelCase__     )[0]
        _A    		=  self.layer_norm(UpperCamelCase__     )

        return self.dropout_post(UpperCamelCase__     ), encoder_inputs_mask




'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCAmelCase    :     List[Any]         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   pass







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
        pipe.to(UpperCamelCase__     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'     )
        _A    		=  torch.manual_seed(0     )
        _A    		=  pipe(
            image=UpperCamelCase__, generator=UpperCamelCase__, guidance_scale=7.5, num_inference_steps=50, output_type='numpy', ).images

        _A    		=  image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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



