


'''simple docstring'''




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ImageGPTImageProcessor




class 			_lowercase (	unittest.TestCase							):
   def __init__(						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Any=7       ,   SCREAMING_SNAKE_CASE_       :      List[str]=3       ,   SCREAMING_SNAKE_CASE_       :      Tuple=18       ,   SCREAMING_SNAKE_CASE_       :      str=30       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=400       ,   SCREAMING_SNAKE_CASE_       :      str=True       ,   SCREAMING_SNAKE_CASE_       :      List[str]=None       ,   SCREAMING_SNAKE_CASE_       :      List[str]=True       ,   ) ->							int:
        __snake_case           =						size if size is not None else {'height': 18, 'width': 18}
        __snake_case           =						parent
        __snake_case           =						batch_size
        __snake_case           =						num_channels
        __snake_case           =						image_size
        __snake_case           =						min_resolution
        __snake_case           =						max_resolution
        __snake_case           =						do_resize
        __snake_case           =						size
        __snake_case           =						do_normalize





   def a  (						self       :      Optional[int]      ) ->							List[str]:
        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                    [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                ]      ),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }




@require_torch
@require_vision
class 			_lowercase (	__lowercase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Tuple          =	ImageGPTImageProcessor if is_vision_available() else None
   def a  (						self       :      str      ) ->							Union[str, Any]:
        __snake_case           =						ImageGPTImageProcessingTester(self      )
   @property
   def a  (						self       :      Optional[int]      ) ->							List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,   'clusters'      )      )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,   'do_resize'      )      )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,   'size'      )      )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,   'do_normalize'      )      )
   def a  (						self       :      str      ) ->							List[str]:
        __snake_case           =						self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 18}      )

        __snake_case           =						self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42      )
        self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42}      )
   def a  (						self       :      Union[str, Any]      ) ->							int:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )
        __snake_case           =						json.loads(image_processor.to_json_string()      )
        for key, value in self.image_processor_dict.items():
             if key == "clusters":
                  self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_       ,   obj[key]      )      )
             else:
                  self.assertEqual(obj[key]       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Tuple      ) ->							Dict:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
             __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   'image_processor.json'      )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_      ).to_dict()

        __snake_case           =						image_processor_first.to_dict()
        for key, value in image_processor_first.items():
             if key == "clusters":
                  self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_       ,   image_processor_second[key]      )      )
             else:
                  self.assertEqual(image_processor_first[key]       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      str      ) ->							List[Any]:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_      ).to_dict()

        __snake_case           =						image_processor_first.to_dict()
        for key, value in image_processor_first.items():
             if key == "clusters":
                  self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_       ,   image_processor_second[key]      )      )
             else:
                  self.assertEqual(image_processor_first[key]       ,   SCREAMING_SNAKE_CASE_      )





   @unittest.skip('ImageGPT requires clusters at initialization'      )
   def a  (						self       :      List[Any]      ) ->							List[Any]:
        pass





def   _a		()			->			Any:






     """simple docstring"""
     __snake_case           =						load_dataset('hf-internal-testing/fixtures_image_utils'							,   split='test'						)

     __snake_case           =						Image.open(dataset[4]['file']						)
     __snake_case           =						Image.open(dataset[5]['file']						)

     __snake_case           =						[imagea, imagea]

     return images




@require_vision
@require_torch
class 			_lowercase (	unittest.TestCase							):
   @slow
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

        __snake_case           =						prepare_images()

        # test non-batched
        __snake_case           =						image_processing(images[0]       ,   return_tensors='pt'      )

        self.assertIsInstance(encoding.input_ids       ,   torch.LongTensor      )
        self.assertEqual(encoding.input_ids.shape       ,   (1, 1024)      )

        __snake_case           =						[306, 191, 191]
        self.assertEqual(encoding.input_ids[0, :3].tolist()       ,   SCREAMING_SNAKE_CASE_      )

        # test batched
        __snake_case           =						image_processing(SCREAMING_SNAKE_CASE_       ,   return_tensors='pt'      )

        self.assertIsInstance(encoding.input_ids       ,   torch.LongTensor      )
        self.assertEqual(encoding.input_ids.shape       ,   (2, 1024)      )

        __snake_case           =						[303, 13, 13]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,   SCREAMING_SNAKE_CASE_      )











'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   1



     @register_to_config
     def __init__(   self							:			str   ,       __snake_case							:			int = 20_00   ,       __snake_case							:			float = 0.15   ,       __snake_case							:			float = 0.01   ,       __snake_case							:			float = 13_48.0   ,       __snake_case							:			float = 1e-5   ,       __snake_case							:			int = 1   ,       )->	str:
        # standard deviation of the initial noise distribution
        snake_case 						=   sigma_max

        # setable values
        snake_case 						=   None

        self.set_sigmas(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[int] = None						)->	torch.FloatTensor:
        return sample



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			Union[str, torch.device] = None						)->	Optional[Any]:
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        snake_case 						=   torch.linspace(1   ,       __snake_case   ,       __snake_case   ,       device=__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None						)->	str:
        snake_case 						=   sigma_min if sigma_min is not None else self.config.sigma_min
        snake_case 						=   sigma_max if sigma_max is not None else self.config.sigma_max
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(__snake_case   ,       __snake_case						)

        snake_case 						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        snake_case 						=   torch.exp(torch.linspace(math.log(__snake_case						)   ,       math.log(__snake_case						)   ,       __snake_case						)						)
        snake_case 						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[str]   ,       __snake_case							:			str						)->	Optional[int]:
        return torch.where(
            timesteps == 0   ,       torch.zeros_like(t.to(timesteps.device						)						)   ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device						)   ,       )



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        snake_case 						=   timestep * torch.ones(
            sample.shape[0]   ,       device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
        snake_case 						=   (timestep * (len(self.timesteps						) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        snake_case 						=   timesteps.to(self.discrete_sigmas.device						)

        snake_case 						=   self.discrete_sigmas[timesteps].to(sample.device						)
        snake_case 						=   self.get_adjacent_sigma(__snake_case   ,       __snake_case						).to(sample.device						)
        snake_case 						=   torch.zeros_like(__snake_case						)
        snake_case 						=   (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        snake_case 						=   diffusion.flatten()
        while len(diffusion.shape						) < len(sample.shape						):
           snake_case 						=   diffusion.unsqueeze(-1						)
        snake_case 						=   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        snake_case 						=   randn_tensor(
            sample.shape   ,       layout=sample.layout   ,       generator=__snake_case   ,       device=sample.device   ,       dtype=sample.dtype						)
        snake_case 						=   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        snake_case 						=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__snake_case   ,       prev_sample_mean=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SchedulerOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        snake_case 						=   randn_tensor(sample.shape   ,       layout=sample.layout   ,       generator=__snake_case						).to(sample.device						)

        # compute step size from the model_output, the noise, and the snr
        snake_case 						=   torch.norm(model_output.reshape(model_output.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   torch.norm(noise.reshape(noise.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        snake_case 						=   step_size * torch.ones(sample.shape[0]						).to(sample.device						)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        snake_case 						=   step_size.flatten()
        while len(step_size.shape						) < len(sample.shape						):
           snake_case 						=   step_size.unsqueeze(-1						)
        snake_case 						=   sample + step_size * model_output
        snake_case 						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       )->	torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case 						=   timesteps.to(original_samples.device						)
        snake_case 						=   self.discrete_sigmas.to(original_samples.device						)[timesteps]
        snake_case 						=   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__snake_case						) * sigmas[:, None, None, None]
        )
        snake_case 						=   noise + original_samples
        return noisy_samples



     def __len__(   self							:			str						)->	Any:
        return self.config.num_train_timesteps

