

from typing import Mapping

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


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					str    			=     {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  int    				=			'''umt5'''
					UpperCAmelCase__:  List[Any]    				=			['''past_key_values''']






					def __init__(					self       ,			A__=25_0112       ,			A__=512       ,			A__=64       ,			A__=1024       ,			A__=8       ,			A__=None       ,			A__=6       ,			A__=32       ,			A__=128       ,			A__=0.1       ,			A__=1e-6       ,			A__=1.0       ,			A__="gated-gelu"       ,			A__=True       ,			A__=True       ,			A__="T5Tokenizer"       ,			A__=True       ,			A__=0       ,			A__=1       ,			A__=0       ,			**A__       ,			):
						super().__init__(
						    is_encoder_decoder=A__       ,			tokenizer_class=A__       ,			tie_word_embeddings=A__       ,			pad_token_id=A__       ,			eos_token_id=A__       ,			decoder_start_token_id=A__       ,			**A__       ,			)
						A__				:       Dict	  =						vocab_size
						A__				:       int	  =						d_model
						A__				:       str	  =						d_kv
						A__				:       List[str]	  =						d_ff
						A__				:       Dict	  =						num_layers
						A__				:       int	  =						(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						A__				:       Optional[int]	  =						num_heads
						A__				:       Tuple	  =						relative_attention_num_buckets
						A__				:       str	  =						relative_attention_max_distance
						A__				:       Union[str, Any]	  =						dropout_rate
						A__				:       List[str]	  =						layer_norm_epsilon
						A__				:       Any	  =						initializer_factor
						A__				:       List[str]	  =						feed_forward_proj
						A__				:       Dict	  =						use_cache

						A__				:       Any	  =						self.feed_forward_proj.split("""-"""     )
						A__				:       Dict	  =						act_info[-1]
						A__				:       Tuple	  =						act_info[0] == """gated"""

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

						if feed_forward_proj == "gated-gelu":
							A__				:       Optional[int]	  =						"""gelu_new"""






					@property
					def     __A     (					self     ):
						return self.d_model






					@property
					def     __A     (					self     ):
						return self.num_heads






					@property
					def     __A     (					self     ):
						return self.num_layers



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def     __A     (					self     ):
						A__				:       Any	  =						{
						    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
						    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
						}
						if self.use_past:
							A__				:       List[Any]	  =						"""past_encoder_sequence + sequence"""
							A__				:       Optional[Any]	  =						{0: """batch"""}
							A__				:       int	  =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
						else:
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """decoder_sequence"""}
							A__				:       List[str]	  =						{0: """batch""", 1: """decoder_sequence"""}

						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )

						return common_inputs






					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
					def     __A     (					self     ):
						return 13






					@property
					def     __A     (					self     ):
						return 5e-4






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        _a		(__magic_name__						,  __magic_name__						,  __magic_name__						,  unittest.TestCase							):


					'''simple docstring'''






					UpperCAmelCase__:  int    				=			AltDiffusionPipeline
					UpperCAmelCase__:  Tuple    				=			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__:  List[str]    				=			TEXT_TO_IMAGE_BATCH_PARAMS
					UpperCAmelCase__:  Union[str, Any]    				=			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__:  Optional[int]    				=			TEXT_TO_IMAGE_IMAGE_PARAMS






					def     __A     (					self     ):
						torch.manual_seed(0     )
						A__				:       Union[str, Any]	  =						UNetaDConditionModel(
						    block_out_channels=(32, 64)       ,			layers_per_block=2       ,			sample_size=32       ,			in_channels=4       ,			out_channels=4       ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,			cross_attention_dim=32       ,			)
						A__				:       List[str]	  =						DDIMScheduler(
						    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule="""scaled_linear"""       ,			clip_sample=A__       ,			set_alpha_to_one=A__       ,			)
						torch.manual_seed(0     )
						A__				:       int	  =						AutoencoderKL(
						    block_out_channels=[32, 64]       ,			in_channels=3       ,			out_channels=3       ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,			latent_channels=4       ,			)

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0     )
						A__				:       int	  =						CLIPTextConfig(
						    bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=32       ,			projection_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=5002       ,			)
						A__				:       Tuple	  =						CLIPTextModel(A__     )

						A__				:       Union[str, Any]	  =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""     )
						A__				:       Union[str, Any]	  =						77

						A__				:       Tuple	  =						{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






					def     __A     (					self       ,			A__       ,			A__=0     ):
						if str(A__     ).startswith("""mps"""     ):
							A__				:       Dict	  =						torch.manual_seed(A__     )
						else:
							A__				:       str	  =						torch.Generator(device=A__     ).manual_seed(A__     )
						A__				:       int	  =						{
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 6.0,
						    """output_type""": """numpy""",
						}
						return inputs






					def     __A     (					self     ):
						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )






					def     __A     (					self     ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

						A__				:       Union[str, Any]	  =						self.get_dummy_components()
						torch.manual_seed(0     )
						A__				:       List[str]	  =						RobertaSeriesConfig(
						    hidden_size=32       ,			project_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			vocab_size=5002       ,			)
						# TODO: remove after fixing the non-deterministic text encoder
						A__				:       Tuple	  =						RobertaSeriesModelWithTransformation(A__     )
						A__				:       Optional[int]	  =						text_encoder

						A__				:       Optional[int]	  =						AltDiffusionPipeline(**A__     )
						A__				:       str	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       List[str]	  =						self.get_dummy_inputs(A__     )
						A__				:       List[str]	  =						"""A photo of an astronaut"""
						A__				:       Optional[int]	  =						alt_pipe(**A__     )
						A__				:       List[Any]	  =						output.images
						A__				:       int	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__				:       Optional[Any]	  =						np.array(
						    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]     )

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






					def     __A     (					self     ):
						A__				:       Dict	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

						A__				:       Union[str, Any]	  =						self.get_dummy_components()
						A__				:       Any	  =						PNDMScheduler(skip_prk_steps=A__     )
						torch.manual_seed(0     )
						A__				:       List[str]	  =						RobertaSeriesConfig(
						    hidden_size=32       ,			project_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			vocab_size=5002       ,			)
						# TODO: remove after fixing the non-deterministic text encoder
						A__				:       Optional[Any]	  =						RobertaSeriesModelWithTransformation(A__     )
						A__				:       List[Any]	  =						text_encoder
						A__				:       Optional[Any]	  =						AltDiffusionPipeline(**A__     )
						A__				:       Union[str, Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       Optional[Any]	  =						self.get_dummy_inputs(A__     )
						A__				:       Dict	  =						alt_pipe(**A__     )
						A__				:       str	  =						output.images
						A__				:       Optional[int]	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__				:       Dict	  =						np.array(
						    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]     )

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



@slow
@require_torch_gpu
class        _a		(unittest.TestCase							):


					'''simple docstring'''






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






					def     __A     (					self     ):
						# make sure here that pndm scheduler skips prk
						A__				:       Any	  =						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,			safety_checker=A__     )
						A__				:       Union[str, Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       int	  =						"""A painting of a squirrel eating a burger"""
						A__				:       Any	  =						torch.manual_seed(0     )
						A__				:       List[Any]	  =						alt_pipe([prompt]       ,			generator=A__       ,			guidance_scale=6.0       ,			num_inference_steps=20       ,			output_type="""np"""     )

						A__				:       List[Any]	  =						output.images

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

						assert image.shape == (1, 512, 512, 3)
						A__				:       int	  =						np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]     )

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






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""       ,			subfolder="""scheduler"""     )

						A__				:       Optional[Any]	  =						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,			scheduler=A__       ,			safety_checker=A__     )
						A__				:       Optional[Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       Union[str, Any]	  =						"""A painting of a squirrel eating a burger"""
						A__				:       Dict	  =						torch.manual_seed(0     )

						A__				:       List[Any]	  =						alt_pipe([prompt]       ,			generator=A__       ,			num_inference_steps=2       ,			output_type="""numpy"""     )
						A__				:       str	  =						output.images

						A__				:       Union[str, Any]	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						A__				:       List[str]	  =						np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]     )

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



