

from typing import Mapping

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


_lowerCAmelCase      :    Optional[int]						       =    logging.get_logger(__name__)






class 			__magic_name__					(    lowerCAmelCase__   ):
			SCREAMING_SNAKE_CASE							     =		"""mt5"""
			SCREAMING_SNAKE_CASE							     =		["""past_key_values"""]





			def __init__(	self      ,						__snake_case=25_0112      ,						__snake_case=512      ,						__snake_case=64      ,						__snake_case=1024      ,						__snake_case=8      ,						__snake_case=None      ,						__snake_case=6      ,						__snake_case=32      ,						__snake_case=128      ,						__snake_case=0.1      ,						__snake_case=1e-6      ,						__snake_case=1.0      ,						__snake_case="gated-gelu"      ,						__snake_case=True      ,						__snake_case=True      ,						__snake_case="T5Tokenizer"      ,						__snake_case=False      ,						__snake_case=0      ,						__snake_case=1      ,						__snake_case=0      ,						**__snake_case      ,						)     ->				Optional[int]:




						'''simple docstring'''
						super().__init__(
						    is_encoder_decoder=lowerCAmelCase_      ,						tokenizer_class=lowerCAmelCase_      ,						tie_word_embeddings=lowerCAmelCase_      ,						pad_token_id=lowerCAmelCase_      ,						eos_token_id=lowerCAmelCase_      ,						decoder_start_token_id=lowerCAmelCase_      ,						**lowerCAmelCase_      ,						)
						__a						   =vocab_size
						__a						   =d_model
						__a						   =d_kv
						__a						   =d_ff
						__a						   =num_layers
						__a						   =(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a						   =num_heads
						__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						   =use_cache

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

						if len(lowerCAmelCase_      ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_      ) > 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'





			@property
			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						return self.d_model





			@property
			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						return self.num_heads





			@property
			def 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						return self.num_layers






class 			__magic_name__					(    lowerCAmelCase__   ):





			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 	__magic_name__					(	self      )     ->				Mapping[str, Mapping[int, str]]:




						'''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_(lowerCAmelCase_      ,						direction='inputs'      )

						return common_inputs





			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						return 13





			@property
			def 	__magic_name__					(	self      )     ->				float:




						'''simple docstring'''
						return 5e-4









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


_lowerCAmelCase      :    List[str]						       =    False






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





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.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      )

						__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=__snake_case      ,						text_to_image_strength=0.75      ,						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      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

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

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						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 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__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=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						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






