

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_		,       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							)



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
