



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      :





							def     UpperCamelCase_       ( self		:		Dict						):

											'''simple docstring'''
											torch.manual_seed(0						)
											__a	  =		TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

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

											torch.manual_seed(0						)
											__a	  =		UNetaDConditionModel(
											    sample_size=32   ,					layers_per_block=1   ,					block_out_channels=[32, 64]   ,					down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]   ,					mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""   ,					up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]   ,					in_channels=3   ,					out_channels=6   ,					cross_attention_dim=32   ,					encoder_hid_dim=32   ,					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						)
											__a	  =		DDPMScheduler(
											    num_train_timesteps=1000   ,					beta_schedule="""squaredcos_cap_v2"""   ,					beta_start=0.0001   ,					beta_end=0.02   ,					thresholding=_SCREAMING_SNAKE_CASE   ,					dynamic_thresholding_ratio=0.95   ,					sample_max_value=1.0   ,					prediction_type="""epsilon"""   ,					variance_type="""learned_range"""   ,					)

											torch.manual_seed(0						)
											__a	  =		IFWatermarker()

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





							def     UpperCamelCase_       ( self		:		List[str]						):

											'''simple docstring'''
											torch.manual_seed(0						)
											__a	  =		TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

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

											torch.manual_seed(0						)
											__a	  =		UNetaDConditionModel(
											    sample_size=32   ,					layers_per_block=[1, 2]   ,					block_out_channels=[32, 64]   ,					down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]   ,					mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""   ,					up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]   ,					in_channels=6   ,					out_channels=6   ,					cross_attention_dim=32   ,					encoder_hid_dim=32   ,					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=1.414   ,					time_embedding_act_fn="""gelu"""   ,					time_embedding_dim=32   ,					)
											unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

											torch.manual_seed(0						)
											__a	  =		DDPMScheduler(
											    num_train_timesteps=1000   ,					beta_schedule="""squaredcos_cap_v2"""   ,					beta_start=0.0001   ,					beta_end=0.02   ,					thresholding=_SCREAMING_SNAKE_CASE   ,					dynamic_thresholding_ratio=0.95   ,					sample_max_value=1.0   ,					prediction_type="""epsilon"""   ,					variance_type="""learned_range"""   ,					)

											torch.manual_seed(0						)
											__a	  =		DDPMScheduler(
											    num_train_timesteps=1000   ,					beta_schedule="""squaredcos_cap_v2"""   ,					beta_start=0.0001   ,					beta_end=0.02   ,					)

											torch.manual_seed(0						)
											__a	  =		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     UpperCamelCase_       ( self		:		Tuple						):

											'''simple docstring'''
											__a	  =		self.get_dummy_components()
											__a	  =		self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
											pipe.to(_SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

											__a	  =		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)

											__a	  =		inputs['prompt']
											__a	  =		inputs['generator']
											__a	  =		inputs['num_inference_steps']
											__a	  =		inputs['output_type']

											if "image" in inputs:
															__a	  =		inputs['image']
											else:
															__a	  =		None

											if "mask_image" in inputs:
															__a	  =		inputs['mask_image']
											else:
															__a	  =		None

											if "original_image" in inputs:
															__a	  =		inputs['original_image']
											else:
															__a	  =		None

											__a	  =		pipe.encode_prompt(_SCREAMING_SNAKE_CASE						)

											# inputs with prompt converted to embeddings
											__a	  =		{
											    '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:
															__a	  =		image

											if mask_image is not None:
															__a	  =		mask_image

											if original_image is not None:
															__a	  =		original_image

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

											__a	  =		pipe(**_SCREAMING_SNAKE_CASE						)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(_SCREAMING_SNAKE_CASE						)
															__a	  =		self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE						)
															pipe_loaded.to(_SCREAMING_SNAKE_CASE						)
															pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

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

											__a	  =		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)

											__a	  =		inputs['generator']
											__a	  =		inputs['num_inference_steps']
											__a	  =		inputs['output_type']

											# inputs with prompt converted to embeddings
											__a	  =		{
											    '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:
															__a	  =		image

											if mask_image is not None:
															__a	  =		mask_image

											if original_image is not None:
															__a	  =		original_image

											__a	  =		pipe_loaded(**_SCREAMING_SNAKE_CASE						)[0]

											__a	  =		np.abs(to_np(_SCREAMING_SNAKE_CASE						) - to_np(_SCREAMING_SNAKE_CASE						)						).max()
											self.assertLess(_SCREAMING_SNAKE_CASE   ,					1E-4						)







							def     UpperCamelCase_       ( self		:		List[Any]						):

											'''simple docstring'''
											__a	  =		self.get_dummy_components()
											__a	  =		self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
											pipe.to(_SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

											__a	  =		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
											__a	  =		pipe(**_SCREAMING_SNAKE_CASE						)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(_SCREAMING_SNAKE_CASE						)
															__a	  =		self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE						)
															pipe_loaded.to(_SCREAMING_SNAKE_CASE						)
															pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

											__a	  =		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
											__a	  =		pipe_loaded(**_SCREAMING_SNAKE_CASE						)[0]

											__a	  =		np.abs(to_np(_SCREAMING_SNAKE_CASE						) - to_np(_SCREAMING_SNAKE_CASE						)						).max()
											self.assertLess(_SCREAMING_SNAKE_CASE   ,					1E-4						)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





