

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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'							)

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

				torch.manual_seed(0							)
				snake_case_      		= 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							)
				snake_case_      		= DDPMScheduler(
				    num_train_timesteps=10_00 ,				beta_schedule='squaredcos_cap_v2' ,				beta_start=0.0001 ,				beta_end=0.02 ,				thresholding=UpperCamelCase__ ,				dynamic_thresholding_ratio=0.95 ,				sample_max_value=1.0 ,				prediction_type='epsilon' ,				variance_type='learned_range' ,				)

				torch.manual_seed(0							)
				snake_case_      		= IFWatermarker()

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'							)

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

				torch.manual_seed(0							)
				snake_case_      		= 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							)
				snake_case_      		= DDPMScheduler(
				    num_train_timesteps=10_00 ,				beta_schedule='squaredcos_cap_v2' ,				beta_start=0.0001 ,				beta_end=0.02 ,				thresholding=UpperCamelCase__ ,				dynamic_thresholding_ratio=0.95 ,				sample_max_value=1.0 ,				prediction_type='epsilon' ,				variance_type='learned_range' ,				)

				torch.manual_seed(0							)
				snake_case_      		= DDPMScheduler(
				    num_train_timesteps=10_00 ,				beta_schedule='squaredcos_cap_v2' ,				beta_start=0.0001 ,				beta_end=0.02 ,				)

				torch.manual_seed(0							)
				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= self.pipeline_class(**UpperCamelCase__							)
				pipe.to(UpperCamelCase__							)
				pipe.set_progress_bar_config(disable=UpperCamelCase__							)

				snake_case_      		= self.get_dummy_inputs(UpperCamelCase__							)

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

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

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

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

				snake_case_      		= pipe.encode_prompt(UpperCamelCase__							)

				# inputs with prompt converted to embeddings
				snake_case_      		= {
				    '''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_      		= image

				if mask_image is not None:
					snake_case_      		= mask_image

				if original_image is not None:
					snake_case_      		= original_image

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

				snake_case_      		= pipe(**UpperCamelCase__							)[0]

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

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

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

				snake_case_      		= self.get_dummy_inputs(UpperCamelCase__							)

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

				# inputs with prompt converted to embeddings
				snake_case_      		= {
				    '''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_      		= image

				if mask_image is not None:
					snake_case_      		= mask_image

				if original_image is not None:
					snake_case_      		= original_image

				snake_case_      		= pipe_loaded(**UpperCamelCase__							)[0]

				snake_case_      		= np.abs(to_np(UpperCamelCase__							) - to_np(UpperCamelCase__							)							).max()
				self.assertLess(UpperCamelCase__ ,				1E-4							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= self.pipeline_class(**UpperCamelCase__							)
				pipe.to(UpperCamelCase__							)
				pipe.set_progress_bar_config(disable=UpperCamelCase__							)

				snake_case_      		= self.get_dummy_inputs(UpperCamelCase__							)
				snake_case_      		= pipe(**UpperCamelCase__							)[0]

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

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

				snake_case_      		= self.get_dummy_inputs(UpperCamelCase__							)
				snake_case_      		= pipe_loaded(**UpperCamelCase__							)[0]

				snake_case_      		= np.abs(to_np(UpperCamelCase__							) - to_np(UpperCamelCase__							)							).max()
				self.assertLess(UpperCamelCase__ ,				1E-4							)



from typing import TYPE_CHECKING

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


A											=     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
