


"""simple docstring"""






from typing import Mapping

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


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''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      __lowerCamelCase	(       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     ,					__UpperCAmelCase=32128     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=64     ,					__UpperCAmelCase=2048     ,					__UpperCAmelCase=6     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=8     ,					__UpperCAmelCase=127     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=1e-6     ,					__UpperCAmelCase=1.0     ,					__UpperCAmelCase="relu"     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase="local"     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					**__UpperCAmelCase     ,					)							->				int:
          _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      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          _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 				_UpperCAmelCase   (   self				)							->				int:
          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