




"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


A_      			=       False






class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 pass







@nightly
@require_torch_gpu
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""",   torch_dtype=torch.floataa	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Union[str, Any]			=						load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""	)

   _snake_case							:	Tuple			=						torch.manual_seed(0	)
   _snake_case							:	Dict			=						pipe.dual_guided(
       prompt="""first prompt""",   image=a_,   text_to_image_strength=0.75,   generator=a_,   guidance_scale=7.5,   num_inference_steps=2,   output_type="""numpy""",   ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(a_	)
     _snake_case							:	str			=						VersatileDiffusionPipeline.from_pretrained(a_,   torch_dtype=torch.floataa	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	int			=						generator.manual_seed(0	)
   _snake_case							:	int			=						pipe.dual_guided(
       prompt="""first prompt""",   image=a_,   text_to_image_strength=0.75,   generator=a_,   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 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""",   torch_dtype=torch.floataa	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Optional[Any]			=						"""cyberpunk 2077"""
   _snake_case							:	List[Any]			=						load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""	)
   _snake_case							:	str			=						torch.manual_seed(0	)
   _snake_case							:	Tuple			=						pipe.dual_guided(
       prompt=a_,   image=a_,   text_to_image_strength=0.75,   generator=a_,   guidance_scale=7.5,   num_inference_steps=50,   output_type="""numpy""",   ).images

   _snake_case							:	Tuple			=						image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _snake_case							:	Dict			=						np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]	)

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

   _snake_case							:	Optional[int]			=						"""A painting of a squirrel eating a burger """
   _snake_case							:	Tuple			=						torch.manual_seed(0	)
   _snake_case							:	Dict			=						pipe.text_to_image(
       prompt=a_,   generator=a_,   guidance_scale=7.5,   num_inference_steps=50,   output_type="""numpy"""	).images

   _snake_case							:	Dict			=						image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _snake_case							:	str			=						np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]	)

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

   _snake_case							:	List[Any]			=						pipe.image_variation(a_,   generator=a_,   output_type="""numpy"""	).images

   _snake_case							:	Optional[int]			=						image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _snake_case							:	Any			=						np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]	)

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








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


A_      			=       logging.get_logger(__name__)

A_      			=       {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      "roformer"


 def __init__(       self:	List[str],   a_:	Tuple=50_000,   a_:	Optional[Any]=None,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	Optional[int]=12,   a_:	Optional[Any]=3_072,   a_:	List[str]="gelu",   a_:	List[str]=0.1,   a_:	Tuple=0.1,   a_:	Optional[int]=1_536,   a_:	Any=2,   a_:	Optional[int]=0.02,   a_:	Tuple=1E-12,   a_:	Dict=0,   a_:	str=False,   a_:	Dict=True,   **a_:	Dict,   ):




   '''simple docstring'''



   super().__init__(pad_token_id=a_,   **a_	)

   _snake_case							:	int			=						vocab_size
   _snake_case							:	int			=						hidden_size if embedding_size is None else embedding_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Optional[int]			=						num_hidden_layers
   _snake_case							:	Any			=						num_attention_heads
   _snake_case							:	Dict			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[Any]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	List[Any]			=						initializer_range
   _snake_case							:	List[Any]			=						layer_norm_eps
   _snake_case							:	Optional[Any]			=						rotary_value
   _snake_case							:	List[str]			=						use_cache







class       lowercase(  __a				):







 '''simple docstring'''





 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



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


