


'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase    :     int					=						False
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'   )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       'A painting of a squirrel eating a burger '
            _lowerCAmelCase    :							str   			=       torch.manual_seed(0   )
            _lowerCAmelCase    :							Tuple   			=       pipe(
                prompt=snake_case__      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=2      ,	output_type='numpy'   ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__   )
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							int   			=       generator.manual_seed(0   )
            _lowerCAmelCase    :							Any   			=       pipe(
                prompt=snake_case__      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=2      ,	output_type='numpy'   ).images

            assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'      ,	torch_dtype=torch.floataa   )
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							str   			=       'A painting of a squirrel eating a burger '
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )
            _lowerCAmelCase    :							str   			=       pipe(
                prompt=snake_case__      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=50      ,	output_type='numpy'   ).images

            _lowerCAmelCase    :							Optional[Any]   			=       image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase    :							Optional[int]   			=       np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase    :     str					=						logging.get_logger(__name__)

lowerCAmelCase    :     int					=						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "data2vec-text"







     def __init__( self      ,	snake_case__=3_0522      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__="absolute"      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(pad_token_id=snake_case__      ,	bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       vocab_size
            _lowerCAmelCase    :							Tuple   			=       hidden_size
            _lowerCAmelCase    :							Dict   			=       num_hidden_layers
            _lowerCAmelCase    :							int   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       intermediate_size
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							str   			=       max_position_embeddings
            _lowerCAmelCase    :							Any   			=       type_vocab_size
            _lowerCAmelCase    :							int   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps
            _lowerCAmelCase    :							List[Any]   			=       position_embedding_type
            _lowerCAmelCase    :							str   			=       use_cache
            _lowerCAmelCase    :							Union[str, Any]   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     @property
     def a	( self   ):

            '''simple docstring'''

            if self.task == "multiple-choice":
                   _lowerCAmelCase    :							Union[str, Any]   			=       {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _lowerCAmelCase    :							List[Any]   			=       {0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]   )

