



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _UpperCAmelCase			(       unittest.TestCase     ):




     @property
     def 		snake_case_		(				self):
         torch.manual_seed(0)
         A__		 =					UNetaDModel(
             block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=3 ,						out_channels=3 ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,						)
         return model




     def 		snake_case_		(				self):
         A__		 =					self.dummy_uncond_unet
         A__		 =					PNDMScheduler()

         A__		 =					PNDMPipeline(unet=UpperCamelCase_ ,						scheduler=UpperCamelCase_)
         pndm.to(UpperCamelCase_)
         pndm.set_progress_bar_config(disable=UpperCamelCase_)

         A__		 =					torch.manual_seed(0)
         A__		 =					pndm(generator=UpperCamelCase_ ,						num_inference_steps=2_0 ,						output_type='''numpy''').images

         A__		 =					torch.manual_seed(0)
         A__		 =					pndm(generator=UpperCamelCase_ ,						num_inference_steps=2_0 ,						output_type='''numpy''' ,						return_dict=UpperCamelCase_)[0]

         A__		 =					image[0, -3:, -3:, -1]
         A__		 =					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         A__		 =					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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







@slow
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					'''google/ddpm-cifar10-32'''

         A__		 =					UNetaDModel.from_pretrained(UpperCamelCase_)
         A__		 =					PNDMScheduler()

         A__		 =					PNDMPipeline(unet=UpperCamelCase_ ,						scheduler=UpperCamelCase_)
         pndm.to(UpperCamelCase_)
         pndm.set_progress_bar_config(disable=UpperCamelCase_)
         A__		 =					torch.manual_seed(0)
         A__		 =					pndm(generator=UpperCamelCase_ ,						output_type='''numpy''').images

         A__		 =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         A__		 =					np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5])

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





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    A__		 =					jnp.zeros_like(UpperCamelCase_	)
    A__		 =					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]	)
    A__		 =					shifted_input_ids.at[:, 0].set(UpperCamelCase_	)

    A__		 =					jnp.where(shifted_input_ids == -1_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
