



"""simple docstring"""
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 			snake_case_						(						a_     ,a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					AltDiffusionPipeline
							__lowerCAmelCase    					=					TEXT_TO_IMAGE_PARAMS
							__lowerCAmelCase    					=					TEXT_TO_IMAGE_BATCH_PARAMS
							__lowerCAmelCase    					=					TEXT_TO_IMAGE_IMAGE_PARAMS
							__lowerCAmelCase    					=					TEXT_TO_IMAGE_IMAGE_PARAMS






							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=4			,     out_channels=4			,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,     cross_attention_dim=3_2			,     )
									a_     :      Union[str, Any] 						=     DDIMScheduler(
									    beta_start=0.00_085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=a_			,     set_alpha_to_one=a_			,     )
									torch.manual_seed(0      )
									a_     :      int 						=     AutoencoderKL(
									    block_out_channels=[3_2, 6_4]			,     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_     :      List[str] 						=     CLIPTextConfig(
									    bos_token_id=0			,     eos_token_id=2			,     hidden_size=3_2			,     projection_dim=3_2			,     intermediate_size=3_7			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=5_0_0_2			,     )
									a_     :      List[str] 						=     CLIPTextModel(a_      )

									a_     :      int 						=     XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"      )
									a_     :      List[Any] 						=     7_7

									a_     :      Union[str, Any] 						=     {
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									if str(a_      ).startswith("mps"      ):
											a_     :      int 						=     torch.manual_seed(a_      )
									else:
											a_     :      Tuple 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "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 snake_case_  (   self      ):
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )






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






							def snake_case_  (   self      ):
									a_     :      Any 						=     "cpu"  # ensure determinism for the device-dependent torch.Generator

									a_     :      List[str] 						=     self.get_dummy_components()
									torch.manual_seed(0      )
									a_     :      List[str] 						=     RobertaSeriesConfig(
									    hidden_size=3_2			,     project_dim=3_2			,     intermediate_size=3_7			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5_0_0_2			,     )
									# TODO: remove after fixing the non-deterministic text encoder
									a_     :      Optional[int] 						=     RobertaSeriesModelWithTransformation(a_      )
									a_     :      List[str] 						=     text_encoder

									a_     :      int 						=     AltDiffusionPipeline(**a_      )
									a_     :      Dict 						=     alt_pipe.to(a_      )
									alt_pipe.set_progress_bar_config(disable=a_      )

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

									assert image.shape == (1, 6_4, 6_4, 3)
									a_     :      Optional[int] 						=     np.array(
									    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]      )

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






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "cpu"  # ensure determinism for the device-dependent torch.Generator

									a_     :      Tuple 						=     self.get_dummy_components()
									a_     :      str 						=     PNDMScheduler(skip_prk_steps=a_      )
									torch.manual_seed(0      )
									a_     :      int 						=     RobertaSeriesConfig(
									    hidden_size=3_2			,     project_dim=3_2			,     intermediate_size=3_7			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=5_0_0_2			,     )
									# TODO: remove after fixing the non-deterministic text encoder
									a_     :      Dict 						=     RobertaSeriesModelWithTransformation(a_      )
									a_     :      Union[str, Any] 						=     text_encoder
									a_     :      int 						=     AltDiffusionPipeline(**a_      )
									a_     :      Optional[int] 						=     alt_pipe.to(a_      )
									alt_pipe.set_progress_bar_config(disable=a_      )

									a_     :      List[Any] 						=     self.get_dummy_inputs(a_      )
									a_     :      Tuple 						=     alt_pipe(**a_      )
									a_     :      int 						=     output.images
									a_     :      Any 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									a_     :      Optional[int] 						=     np.array(
									    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]      )

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




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






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






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

									a_     :      Tuple 						=     "A painting of a squirrel eating a burger"
									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     alt_pipe([prompt]			,     generator=a_			,     guidance_scale=6.0			,     num_inference_steps=2_0			,     output_type="np"      )

									a_     :      Optional[int] 						=     output.images

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]      )

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






							def snake_case_  (   self      ):
									a_     :      Any 						=     DDIMScheduler.from_pretrained("BAAI/AltDiffusion"			,     subfolder="scheduler"      )

									a_     :      int 						=     AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"			,     scheduler=a_			,     safety_checker=a_      )
									a_     :      Tuple 						=     alt_pipe.to(a_      )
									alt_pipe.set_progress_bar_config(disable=a_      )

									a_     :      List[Any] 						=     "A painting of a squirrel eating a burger"
									a_     :      str 						=     torch.manual_seed(0      )

									a_     :      str 						=     alt_pipe([prompt]			,     generator=a_			,     num_inference_steps=2			,     output_type="numpy"      )
									a_     :      Dict 						=     output.images

									a_     :      int 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Union[str, Any] 						=     np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]      )

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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)