






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						torch.manual_seed(0	)
						snake_case__			:				Union[str, Any]       =  TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

						torch.manual_seed(0	)
						snake_case__			:				List[str]       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

						torch.manual_seed(0	)
						snake_case__			:				int       =  UNetaDConditionModel(
						    sample_size=3_2							,		layers_per_block=1							,		block_out_channels=[3_2, 6_4]							,		down_block_types=[
						        "ResnetDownsampleBlock2D",
						        "SimpleCrossAttnDownBlock2D",
						    ]							,		mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,		up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,		in_channels=3							,		out_channels=6							,		cross_attention_dim=3_2							,		encoder_hid_dim=3_2							,		attention_head_dim=8							,		addition_embed_type="text"							,		addition_embed_type_num_heads=2							,		cross_attention_norm="group_norm"							,		resnet_time_scale_shift="scale_shift"							,		act_fn="gelu"							,		)
						unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

						torch.manual_seed(0	)
						snake_case__			:				Any       =  DDPMScheduler(
						    num_train_timesteps=1_0_0_0							,		beta_schedule="squaredcos_cap_v2"							,		beta_start=0.0_0_0_1							,		beta_end=0.0_2							,		thresholding=__A							,		dynamic_thresholding_ratio=0.9_5							,		sample_max_value=1.0							,		prediction_type="epsilon"							,		variance_type="learned_range"							,		)

						torch.manual_seed(0	)
						snake_case__			:				List[str]       =  IFWatermarker()

						return {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "watermarker": watermarker,
						    "safety_checker": None,
						    "feature_extractor": None,
						}



					def 	_lowercase					( self					:  Union[str, Any]	):
						torch.manual_seed(0	)
						snake_case__			:				str       =  TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"	)

						torch.manual_seed(0	)
						snake_case__			:				Tuple       =  AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"	)

						torch.manual_seed(0	)
						snake_case__			:				List[Any]       =  UNetaDConditionModel(
						    sample_size=3_2							,		layers_per_block=[1, 2]							,		block_out_channels=[3_2, 6_4]							,		down_block_types=[
						        "ResnetDownsampleBlock2D",
						        "SimpleCrossAttnDownBlock2D",
						    ]							,		mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,		up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,		in_channels=6							,		out_channels=6							,		cross_attention_dim=3_2							,		encoder_hid_dim=3_2							,		attention_head_dim=8							,		addition_embed_type="text"							,		addition_embed_type_num_heads=2							,		cross_attention_norm="group_norm"							,		resnet_time_scale_shift="scale_shift"							,		act_fn="gelu"							,		class_embed_type="timestep"							,		mid_block_scale_factor=1.4_1_4							,		time_embedding_act_fn="gelu"							,		time_embedding_dim=3_2							,		)
						unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

						torch.manual_seed(0	)
						snake_case__			:				Optional[Any]       =  DDPMScheduler(
						    num_train_timesteps=1_0_0_0							,		beta_schedule="squaredcos_cap_v2"							,		beta_start=0.0_0_0_1							,		beta_end=0.0_2							,		thresholding=__A							,		dynamic_thresholding_ratio=0.9_5							,		sample_max_value=1.0							,		prediction_type="epsilon"							,		variance_type="learned_range"							,		)

						torch.manual_seed(0	)
						snake_case__			:				Dict       =  DDPMScheduler(
						    num_train_timesteps=1_0_0_0							,		beta_schedule="squaredcos_cap_v2"							,		beta_start=0.0_0_0_1							,		beta_end=0.0_2							,		)

						torch.manual_seed(0	)
						snake_case__			:				int       =  IFWatermarker()

						return {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "image_noising_scheduler": image_noising_scheduler,
						    "watermarker": watermarker,
						    "safety_checker": None,
						    "feature_extractor": None,
						}



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[int]       =  self.get_dummy_components()
						snake_case__			:				int       =  self.pipeline_class(**__A	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				List[str]       =  self.get_dummy_inputs(__A	)

						snake_case__			:				Optional[int]       =  inputs["prompt"]
						snake_case__			:				List[str]       =  inputs["generator"]
						snake_case__			:				Dict       =  inputs["num_inference_steps"]
						snake_case__			:				str       =  inputs["output_type"]

						if "image" in inputs:
							snake_case__			:				str       =  inputs["image"]
						else:
							snake_case__			:				Optional[int]       =  None

						if "mask_image" in inputs:
							snake_case__			:				List[Any]       =  inputs["mask_image"]
						else:
							snake_case__			:				List[Any]       =  None

						if "original_image" in inputs:
							snake_case__			:				List[Any]       =  inputs["original_image"]
						else:
							snake_case__			:				int       =  None

						snake_case__			:				List[Any]       =  pipe.encode_prompt(__A	)

						# inputs with prompt converted to embeddings
						snake_case__			:				Union[str, Any]       =  {
						    "prompt_embeds": prompt_embeds,
						    "negative_prompt_embeds": negative_prompt_embeds,
						    "generator": generator,
						    "num_inference_steps": num_inference_steps,
						    "output_type": output_type,
						}

						if image is not None:
							snake_case__			:				List[Any]       =  image

						if mask_image is not None:
							snake_case__			:				Any       =  mask_image

						if original_image is not None:
							snake_case__			:				Dict       =  original_image

						# set all optional components to None
						for optional_component in pipe._optional_components:
							setattr(__A							,		__A							,		__A	)

						snake_case__			:				Union[str, Any]       =  pipe(**__A	)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(__A	)
							snake_case__			:				Any       =  self.pipeline_class.from_pretrained(__A	)
							pipe_loaded.to(__A	)
							pipe_loaded.set_progress_bar_config(disable=__A	)

						pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

						for optional_component in pipe._optional_components:
							self.assertTrue(
							    getattr(__A							,		__A	) is None							,		f'''`{optional_component}` did not stay set to None after loading.'''							,		)

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)

						snake_case__			:				List[str]       =  inputs["generator"]
						snake_case__			:				List[str]       =  inputs["num_inference_steps"]
						snake_case__			:				Dict       =  inputs["output_type"]

						# inputs with prompt converted to embeddings
						snake_case__			:				List[str]       =  {
						    "prompt_embeds": prompt_embeds,
						    "negative_prompt_embeds": negative_prompt_embeds,
						    "generator": generator,
						    "num_inference_steps": num_inference_steps,
						    "output_type": output_type,
						}

						if image is not None:
							snake_case__			:				List[Any]       =  image

						if mask_image is not None:
							snake_case__			:				Any       =  mask_image

						if original_image is not None:
							snake_case__			:				Any       =  original_image

						snake_case__			:				Dict       =  pipe_loaded(**__A	)[0]

						snake_case__			:				str       =  np.abs(to_np(__A	) - to_np(__A	)	).max()
						self.assertLess(__A							,		1e-4	)






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Tuple       =  self.get_dummy_components()
						snake_case__			:				Optional[Any]       =  self.pipeline_class(**__A	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				List[str]       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Tuple       =  pipe(**__A	)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(__A	)
							snake_case__			:				int       =  self.pipeline_class.from_pretrained(__A	)
							pipe_loaded.to(__A	)
							pipe_loaded.set_progress_bar_config(disable=__A	)

						pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()	)  # For reproducibility tests

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Optional[int]       =  pipe_loaded(**__A	)[0]

						snake_case__			:				str       =  np.abs(to_np(__A	) - to_np(__A	)	).max()
						self.assertLess(__A							,		1e-4	)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



