






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__:					Optional[Any]							=     {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__magic_name__:					Any							=     [
	    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "M2M100ForConditionalGeneration",
	    "M2M100Model",
	    "M2M100PreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
	from .tokenization_mam_aaa import MaMaaaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mam_aaa import (
		    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaMaaaForConditionalGeneration,
		    MaMaaaModel,
		    MaMaaaPreTrainedModel,
		)


else:
	import sys

	__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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


__magic_name__:					Union[str, Any]							=     False





@skip_mps
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					Optional[int]											=   StableDiffusionAttendAndExcitePipeline
	lowercase__						:					Tuple											=   False
	lowercase__						:					List[str]											=   TEXT_TO_IMAGE_PARAMS
	lowercase__						:					Dict											=   TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}				)
	lowercase__						:					Union[str, Any]											=   TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase__						:					List[Any]											=   TEXT_TO_IMAGE_IMAGE_PARAMS
	@classmethod
	def       __magic_name__    (    cls			)							->				Tuple:
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls			)							->				Optional[Any]:
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				str:
							torch.manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,       layers_per_block=1      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       attention_head_dim=(2, 4)      ,       use_linear_projection=lowerCAmelCase__      ,       )
							__magic_name__      :       List[Any]       						= DDIMScheduler(
							    beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       beta_schedule="""scaled_linear"""      ,       clip_sample=lowerCAmelCase__      ,       set_alpha_to_one=lowerCAmelCase__      ,       )
							torch.manual_seed(0			)
							__magic_name__      :       List[Any]       						= 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      ,       sample_size=1_28      ,       )
							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= CLIPTextConfig(
							    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       hidden_act="""gelu"""      ,       projection_dim=5_12      ,       )
							__magic_name__      :       Any       						= CLIPTextModel(lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

							__magic_name__      :       Optional[int]       						= {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}

							return components
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0			)							->				Optional[Any]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       int       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       int       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)
							__magic_name__      :       Optional[Any]       						= {
							    """prompt""": """a cat and a frog""",
							    """token_indices""": [2, 5],
							    """generator""": generator,
							    """num_inference_steps""": 1,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							    """max_iter_to_alter""": 2,
							    """thresholds""": {0: 0.7},
							}
							return inputs
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= """cpu"""

							__magic_name__      :       Union[str, Any]       						= self.get_dummy_components()
							__magic_name__      :       int       						= self.pipeline_class(**lowerCAmelCase__			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= self.get_dummy_inputs(lowerCAmelCase__			)
							__magic_name__      :       str       						= pipe(**lowerCAmelCase__			).images
							__magic_name__      :       Any       						= image[0, -3:, -3:, -1]

							self.assertEqual(image.shape      ,       (1, 64, 64, 3)			)
							__magic_name__      :       Dict       						= np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]			)
							__magic_name__      :       Tuple       						= np.abs(image_slice.flatten() - expected_slice			).max()
							self.assertLessEqual(lowerCAmelCase__      ,       1e-3			)
	def       __magic_name__    (    self			)							->				List[Any]:
							super().test_cpu_offload_forward_pass(expected_max_diff=5e-4			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]			)
	def       __magic_name__    (    self			)							->				int:
							self._test_inference_batch_single_identical(batch_size=2      ,       expected_max_diff=7e-4			)
	def       __magic_name__    (    self			)							->				List[str]:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)
	def       __magic_name__    (    self			)							->				Any:
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4			)
	def       __magic_name__    (    self			)							->				Dict:
							super().test_save_load_local(expected_max_difference=5e-4			)







	def       __magic_name__    (    self			)							->				List[Any]:
							super().test_save_load_optional_components(expected_max_difference=4e-4			)





@require_torch_gpu
@slow
class 	snake_case__ (							unittest.TestCase				):
	@classmethod
	def       __magic_name__    (    cls			)							->				Optional[int]:
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls			)							->				List[Any]:
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       str       						= torch.manual_seed(51			)

							__magic_name__      :       Tuple       						= StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    """CompVis/stable-diffusion-v1-4"""      ,       safety_checker=lowerCAmelCase__      ,       torch_dtype=torch.floataa			)
							pipe.to("""cuda"""			)

							__magic_name__      :       List[str]       						= """a painting of an elephant with glasses"""
							__magic_name__      :       Any       						= [5, 7]

							__magic_name__      :       List[Any]       						= pipe(
							    prompt=lowerCAmelCase__      ,       token_indices=lowerCAmelCase__      ,       guidance_scale=7.5      ,       generator=lowerCAmelCase__      ,       num_inference_steps=5      ,       max_iter_to_alter=5      ,       output_type="""numpy"""      ,       ).images[0]

							__magic_name__      :       Optional[int]       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""			)
							assert np.abs((expected_image - image).max()			) < 5e-1
