






"""simple docstring"""


from typing import Mapping

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}


class    snake_case						(				a__		):


   '''simple docstring'''

   A_		:      Any							=					'longt5'
   A_		:      List[Any]							=					['past_key_values']
   A_		:      List[Any]							=					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


   def __init__(      self					:	Dict,			_lowerCamelCase					:	Any=3_21_28,			_lowerCamelCase					:	str=5_12,			_lowerCamelCase					:	List[str]=64,			_lowerCamelCase					:	Dict=20_48,			_lowerCamelCase					:	List[Any]=6,			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	List[Any]=8,			_lowerCamelCase					:	str=1_27,			_lowerCamelCase					:	int=16,			_lowerCamelCase					:	int=32,			_lowerCamelCase					:	Any=1_28,			_lowerCamelCase					:	str=0.1,			_lowerCamelCase					:	List[Any]=1e-6,			_lowerCamelCase					:	Optional[int]=1.0,			_lowerCamelCase					:	List[Any]="relu",			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Optional[Any]="local",			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	List[Any]=0,			_lowerCamelCase					:	Tuple=1,			**_lowerCamelCase					:	Dict,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					d_model
     __A		     =					d_kv
     __A		     =					d_ff
     __A		     =					num_layers
     # default = symmetry
     __A		     =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     __A		     =					num_heads
     __A		     =					local_radius
     __A		     =					global_block_size
     __A		     =					relative_attention_num_buckets
     __A		     =					relative_attention_max_distance
     __A		     =					dropout_rate
     __A		     =					layer_norm_epsilon
     __A		     =					initializer_factor
     __A		     =					feed_forward_proj
     __A		     =					encoder_attention_type
     __A		     =					use_cache

     __A		     =					self.feed_forward_proj.split('''-'''    )
     __A		     =					act_info[-1]
     __A		     =					act_info[0] == '''gated'''

     if len(__UpperCAmelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase    ) > 2:
       raise ValueError(
           f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
           '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
           '''\'gated-gelu\' or \'relu\''''    )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
       __A		     =					'''gelu_new'''

     super().__init__(
         pad_token_id=__UpperCAmelCase,			eos_token_id=__UpperCAmelCase,			is_encoder_decoder=__UpperCAmelCase,			**__UpperCAmelCase,			)





class    snake_case						(				a__		):


   '''simple docstring'''

   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					{
         '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
         '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
     }
     if self.use_past:
       __A		     =					'''past_encoder_sequence + sequence'''
       __A		     =					{0: '''batch'''}
       __A		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       __A		     =					{0: '''batch''', 1: '''decoder_sequence'''}
       __A		     =					{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase,			direction='''inputs'''    )

     return common_inputs






   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     return 13








"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

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


__snake_case												=							False


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

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          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(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCAmelCase				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					generator.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					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				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					'''cyberpunk 2077'''
          _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(
              prompt=__UpperCAmelCase     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=50     ,					output_type='''numpy'''     ,					).images

          _a     						=					image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a     						=					np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]				)

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