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 __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
			lowerCAmelCase_														=							IFPipeline
			lowerCAmelCase_														=							TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
			lowerCAmelCase_														=							TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCAmelCase_														=							PipelineTesterMixin.required_optional_params - {"latents"}




			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					return self._get_dummy_components()




			def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Optional[Any]=0   ):



					"""simple docstring"""






					if str(_lowercase   ).startswith("""mps"""   ):
							SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(_lowercase   )
					else:
							SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   {
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """output_type""": """numpy""",
					}

					return inputs




			def 			__a		(					self    :       str   ):



					"""simple docstring"""






					self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != """cuda""" ,			reason="""float16 requires CUDA"""   )
			def 			__a		(					self    :       Dict   ):



					"""simple docstring"""






					super().test_save_load_floataa(expected_max_diff=1E-1   )




			def 			__a		(					self    :       Dict   ):



					"""simple docstring"""






					self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )




			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					self._test_save_load_local()




			def 			__a		(					self    :       List[Any]   ):



					"""simple docstring"""






					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 			__a		(					self    :       Union[str, Any]   ):



					"""simple docstring"""






					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )







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




			def 			__a		(					self    :       str   ):



					"""simple docstring"""






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




			def 			__a		(					self    :       Dict   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" ,			variant="""fp16""" ,			torch_dtype=torch.floataa   )

					SCREAMING_SNAKE_CASE__    							=   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"""   )

					SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   pipe_a.encode_prompt("""anime turtle""" ,			device="""cuda"""   )

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

					SCREAMING_SNAKE_CASE__    							=   None
					SCREAMING_SNAKE_CASE__    							=   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

					SCREAMING_SNAKE_CASE__    							=   IFImgaImgPipeline(**pipe_a.components   )
					SCREAMING_SNAKE_CASE__    							=   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

					SCREAMING_SNAKE_CASE__    							=   IFInpaintingPipeline(**pipe_a.components   )
					SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       List[str] ,			_lowercase    :       Dict ,			_lowercase    :       int   ):



					"""simple docstring"""






					_start_torch_memory_measurement()

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   pipe_a(
					    prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			num_inference_steps=2 ,			generator=_lowercase ,			output_type="""np""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (64, 64, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 13 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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()

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   pipe_a(
					    prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			generator=_lowercase ,			num_inference_steps=2 ,			output_type="""np""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (2_56, 2_56, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 4 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Dict ,			_lowercase    :       Any ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int]   ):



					"""simple docstring"""






					_start_torch_memory_measurement()

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )

					SCREAMING_SNAKE_CASE__    							=   pipe_a(
					    prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			num_inference_steps=2 ,			generator=_lowercase ,			output_type="""np""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (64, 64, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 10 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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()

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(0   )   ).to(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   pipe_a(
					    prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			original_image=_lowercase ,			generator=_lowercase ,			num_inference_steps=2 ,			output_type="""np""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (2_56, 2_56, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 4 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Any ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Union[str, Any]   ):



					"""simple docstring"""






					_start_torch_memory_measurement()

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(1   )   ).to(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   pipe_a(
					    prompt_embeds=_lowercase ,			negative_prompt_embeds=_lowercase ,			image=_lowercase ,			mask_image=_lowercase ,			num_inference_steps=2 ,			generator=_lowercase ,			output_type="""np""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (64, 64, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 10 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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()

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(0   )   ).to(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(0   )   ).to(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 2_56, 2_56) ,			rng=random.Random(1   )   ).to(_lowercase   )

					SCREAMING_SNAKE_CASE__    							=   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""" ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images[0]

					assert image.shape == (2_56, 2_56, 3)

					SCREAMING_SNAKE_CASE__    							=   torch.cuda.max_memory_allocated()
					assert mem_bytes < 4 * 10**9

					SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[int]:







		"""simple docstring"""





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

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
