



import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					IFPipeline
		__lowerCAmelCase	:    List[str]											=					TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
		__lowerCAmelCase	:    Tuple											=					TEXT_TO_IMAGE_BATCH_PARAMS
		__lowerCAmelCase	:    Optional[Any]											=					PipelineTesterMixin.required_optional_params - {"""latents"""}



		def 	__lowerCamelCase			(     self     :int  ):
						return self._get_dummy_components()



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :str   ,__lowercase     :List[str]=0  ):
						if str(__lowercase  ).startswith('''mps'''  ):
										snake_case__	:      Union[str, Any]     		=	torch.manual_seed(__lowercase  )
						else:
										snake_case__	:      int     		=	torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )

						snake_case__	:      int     		=	{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs



		def 	__lowerCamelCase			(     self     :int  ):
						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA'''  )
		def 	__lowerCamelCase			(     self     :int  ):
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1e-1  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )



		def 	__lowerCamelCase			(     self     :str  ):
						self._test_save_load_local()



		def 	__lowerCamelCase			(     self     :Any  ):
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2   ,)






		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
		def 	__lowerCamelCase			(     self     :Dict  ):
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


@slow
@require_torch_gpu
class       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 	__lowerCamelCase			(     self     :int  ):
						# if

						snake_case__	:      str     		=	IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa  )

						snake_case__	:      Tuple     		=	IFSuperResolutionPipeline.from_pretrained(
						    '''DeepFloyd/IF-II-L-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa   ,text_encoder=__lowercase   ,tokenizer=__lowercase  )

						# pre compute text embeddings and remove T5 to save memory

						pipe_a.text_encoder.to('''cuda'''  )

						snake_case__		, snake_case__	:      List[Any]     		=	pipe_a.encode_prompt('''anime turtle'''   ,device='''cuda'''  )

						del pipe_a.tokenizer
						del pipe_a.text_encoder
						gc.collect()

						snake_case__	:      str     		=	None
						snake_case__	:      Tuple     		=	None

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if(__lowercase   ,__lowercase   ,__lowercase   ,__lowercase  )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# img2img

						snake_case__	:      Any     		=	IFImgaImgPipeline(**pipe_a.components  )
						snake_case__	:      Optional[Any]     		=	IFImgaImgSuperResolutionPipeline(**pipe_a.components  )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if_imgaimg(__lowercase   ,__lowercase   ,__lowercase   ,__lowercase  )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# inpainting

						snake_case__	:      Tuple     		=	IFInpaintingPipeline(**pipe_a.components  )
						snake_case__	:      Optional[int]     		=	IFInpaintingSuperResolutionPipeline(**pipe_a.components  )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

						self._test_if_inpainting(__lowercase   ,__lowercase   ,__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :Any   ,__lowercase     :Optional[int]   ,__lowercase     :int   ,__lowercase     :str  ):
						# pipeline 1

						_start_torch_memory_measurement()

						snake_case__	:      Any     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )
						snake_case__	:      Optional[Any]     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,num_inference_steps=2   ,generator=__lowercase   ,output_type='''np'''   ,)

						snake_case__	:      Dict     		=	output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						snake_case__	:      Optional[Any]     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_3 * 1_0**9

						snake_case__	:      str     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )

						# pipeline 2

						_start_torch_memory_measurement()

						snake_case__	:      Any     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )

						snake_case__	:      Dict     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0  )  ).to(__lowercase  )

						snake_case__	:      Tuple     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,image=__lowercase   ,generator=__lowercase   ,num_inference_steps=2   ,output_type='''np'''   ,)

						snake_case__	:      Tuple     		=	output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						snake_case__	:      Dict     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						snake_case__	:      str     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :int   ,__lowercase     :List[Any]   ,__lowercase     :str   ,__lowercase     :Union[str, Any]  ):
						# pipeline 1

						_start_torch_memory_measurement()

						snake_case__	:      List[str]     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0  )  ).to(__lowercase  )

						snake_case__	:      Optional[Any]     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )

						snake_case__	:      Optional[Any]     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,image=__lowercase   ,num_inference_steps=2   ,generator=__lowercase   ,output_type='''np'''   ,)

						snake_case__	:      Optional[int]     		=	output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						snake_case__	:      Any     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_0 * 1_0**9

						snake_case__	:      Optional[int]     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )

						# pipeline 2

						_start_torch_memory_measurement()

						snake_case__	:      Union[str, Any]     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )

						snake_case__	:      List[str]     		=	floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0  )  ).to(__lowercase  )
						snake_case__	:      str     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0  )  ).to(__lowercase  )

						snake_case__	:      Union[str, Any]     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,image=__lowercase   ,original_image=__lowercase   ,generator=__lowercase   ,num_inference_steps=2   ,output_type='''np'''   ,)

						snake_case__	:      Any     		=	output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						snake_case__	:      str     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						snake_case__	:      int     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )






		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :Dict   ,__lowercase     :Optional[int]   ,__lowercase     :str   ,__lowercase     :int  ):
						# pipeline 1

						_start_torch_memory_measurement()

						snake_case__	:      List[Any]     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0  )  ).to(__lowercase  )
						snake_case__	:      List[str]     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(1  )  ).to(__lowercase  )

						snake_case__	:      Any     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )
						snake_case__	:      int     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,image=__lowercase   ,mask_image=__lowercase   ,num_inference_steps=2   ,generator=__lowercase   ,output_type='''np'''   ,)

						snake_case__	:      Dict     		=	output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						snake_case__	:      str     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_0 * 1_0**9

						snake_case__	:      Any     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )

						# pipeline 2

						_start_torch_memory_measurement()

						snake_case__	:      List[Any]     		=	torch.Generator(device='''cpu'''  ).manual_seed(0  )

						snake_case__	:      List[Any]     		=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0  )  ).to(__lowercase  )
						snake_case__	:      Optional[Any]     		=	floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0  )  ).to(__lowercase  )
						snake_case__	:      List[Any]     		=	floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(1  )  ).to(__lowercase  )

						snake_case__	:      Tuple     		=	pipe_a(
						    prompt_embeds=__lowercase   ,negative_prompt_embeds=__lowercase   ,image=__lowercase   ,mask_image=__lowercase   ,original_image=__lowercase   ,generator=__lowercase   ,num_inference_steps=2   ,output_type='''np'''   ,)

						snake_case__	:      str     		=	output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						snake_case__	:      Tuple     		=	torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						snake_case__	:      Optional[Any]     		=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''  )
						assert_mean_pixel_difference(__lowercase   ,__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()






import math
import sys



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	''''''
				try:
								with open(__lowerCAmelCase  ,  '''rb'''      ) as binary_file:
												snake_case__	:      int     		=	binary_file.read()
								for dat in data:
												snake_case__	:      Any     		=	f"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	{'''0''': '''0''', '''1''': '''1'''}
				snake_case__		, snake_case__	:      List[Any]     		=	'''''', ''''''
				snake_case__	:      Tuple     		=	len(__lowerCAmelCase      )

				for i in range(len(__lowerCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								snake_case__	:      Tuple     		=	lexicon[curr_string]
								result += last_match_id
								snake_case__	:      Any     		=	last_match_id + '''0'''

								if math.loga(__lowerCAmelCase      ).is_integer():
												snake_case__	:      Tuple     		=	{}
												for curr_key in list(__lowerCAmelCase      ):
																snake_case__	:      Union[str, Any]     		=	lexicon.pop(__lowerCAmelCase      )
												snake_case__	:      Optional[Any]     		=	new_lex

								snake_case__	:      Tuple     		=	last_match_id + '''1'''
								index += 1
								snake_case__	:      Dict     		=	''''''
				return result



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Dict     		=	8
				try:
								with open(__lowerCAmelCase  ,  '''wb'''      ) as opened_file:
												snake_case__	:      Union[str, Any]     		=	[
												    to_write[i : i + byte_length]
												    for i in range(0  ,  len(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('''10000000'''      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(__lowerCAmelCase  ,  2      ).to_bytes(1  ,  byteorder='''big'''      )      )
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				snake_case__	:      Optional[int]     		=	data_bits[counter:]
				snake_case__	:      int     		=	data_bits[counter + 1 :]
				return data_bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	read_file_binary(__lowerCAmelCase      )
				snake_case__	:      List[str]     		=	remove_prefix(__lowerCAmelCase      )
				snake_case__	:      Any     		=	decompress_data(__lowerCAmelCase      )
				write_file_binary(__lowerCAmelCase  ,  __lowerCAmelCase      )


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])

