





'''simple docstring'''






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    None





def       lowercase_      (		lowercase__				,  lowercase__=0.9_9_9				,  lowercase__="cosine"				,  )   ->List[Any]:
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase__						):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase__						):
																		return math.exp(t * -1_2.0						)

						else:
												raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

						_snake_case:   List[Any]  					=		[]
						for i in range(lowercase__						):
												_snake_case:   Dict  					=		i / num_diffusion_timesteps
												_snake_case:   int  					=		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__						) / alpha_bar_fn(lowercase__						)				,  lowercase__						)						)
						return torch.tensor(lowercase__				,  dtype=torch.floataa						)







class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase  ):


					@register_to_config
					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  int = 10_00       ,     __snake_case	:  str = "fixed_small_log"       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[float] = 1.0       ,     __snake_case	:  str = "epsilon"       ,     __snake_case	:  str = "squaredcos_cap_v2"       ,     ):





											'''simple docstring'''





											if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''       )

											_snake_case:   List[Any]  					=		betas_for_alpha_bar(__snake_case       )

											_snake_case:   Optional[Any]  					=		1.0 - self.betas
											_snake_case:   Optional[int]  					=		torch.cumprod(self.alphas       ,     dim=0       )
											_snake_case:   List[str]  					=		torch.tensor(1.0       )

											# standard deviation of the initial noise distribution
											_snake_case:   List[Any]  					=		1.0

											# setable values
											_snake_case:   Union[str, Any]  					=		None
											_snake_case:   List[str]  					=		torch.from_numpy(np.arange(0       ,     __snake_case       )[::-1].copy()       )

											_snake_case:   str  					=		variance_type


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  Optional[int] = None       ):





											'''simple docstring'''





											return sample


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  int       ,     __snake_case	:  Union[str, torch.device] = None       ):





											'''simple docstring'''





											_snake_case:   str  					=		num_inference_steps
											_snake_case:   List[str]  					=		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											_snake_case:   str  					=		(np.arange(0       ,     __snake_case       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
											_snake_case:   Union[str, Any]  					=		torch.from_numpy(__snake_case       ).to(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Dict       ,     __snake_case	:  List[Any]=None       ,     __snake_case	:  Any=None       ,     __snake_case	:  str=None       ):





											'''simple docstring'''





											if prev_timestep is None:
																	_snake_case:   str  					=		t - 1

											_snake_case:   Union[str, Any]  					=		self.alphas_cumprod[t]
											_snake_case:   Dict  					=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											_snake_case:   Union[str, Any]  					=		1 - alpha_prod_t
											_snake_case:   Optional[int]  					=		1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																	_snake_case:   List[Any]  					=		self.betas[t]
											else:
																	_snake_case:   Union[str, Any]  					=		1 - alpha_prod_t / alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											_snake_case:   Optional[int]  					=		beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																	_snake_case:   List[Any]  					=		self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																	_snake_case:   Optional[Any]  					=		torch.log(torch.clamp(__snake_case       ,     min=1e-20       )       )
																	_snake_case:   str  					=		torch.exp(0.5 * variance       )
											elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	_snake_case:   Union[str, Any]  					=		variance.log()
																	_snake_case:   Any  					=		beta.log()

																	_snake_case:   List[str]  					=		(predicted_variance + 1) / 2
																	_snake_case:   Union[str, Any]  					=		frac * max_log + (1 - frac) * min_log

											return variance


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  int       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Any=None       ,     __snake_case	:  bool = True       ,     ):





											'''simple docstring'''





											_snake_case:   int  					=		timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																	_snake_case					,						_snake_case:   List[str]  					=		torch.split(__snake_case       ,     sample.shape[1]       ,     dim=1       )
											else:
																	_snake_case:   Union[str, Any]  					=		None

											# 1. compute alphas, betas
											if prev_timestep is None:
																	_snake_case:   Dict  					=		t - 1

											_snake_case:   Optional[Any]  					=		self.alphas_cumprod[t]
											_snake_case:   int  					=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											_snake_case:   List[str]  					=		1 - alpha_prod_t
											_snake_case:   Union[str, Any]  					=		1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																	_snake_case:   Tuple  					=		self.betas[t]
																	_snake_case:   Optional[int]  					=		self.alphas[t]
											else:
																	_snake_case:   List[Any]  					=		1 - alpha_prod_t / alpha_prod_t_prev
																	_snake_case:   Dict  					=		1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	_snake_case:   List[str]  					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	_snake_case:   int  					=		model_output
											else:
																	raise ValueError(
																	    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
																	    ' for the UnCLIPScheduler.'       )

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	_snake_case:   int  					=		torch.clamp(
																	    __snake_case       ,     -self.config.clip_sample_range       ,     self.config.clip_sample_range       )

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											_snake_case:   Tuple  					=		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											_snake_case:   Union[str, Any]  					=		alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											_snake_case:   Optional[int]  					=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											_snake_case:   Any  					=		0
											if t > 0:
																	_snake_case:   int  					=		randn_tensor(
																	    model_output.shape       ,     dtype=model_output.dtype       ,     generator=__snake_case       ,     device=model_output.device       )

																	_snake_case:   Union[str, Any]  					=		self._get_variance(
																	    __snake_case       ,     predicted_variance=__snake_case       ,     prev_timestep=__snake_case       ,     )

																	if self.variance_type == "fixed_small_log":
																							_snake_case:   str  					=		variance
																	elif self.variance_type == "learned_range":
																							_snake_case:   str  					=		(0.5 * variance).exp()
																	else:
																							raise ValueError(
																							    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
																							    ' for the UnCLIPScheduler.'       )

																	_snake_case:   List[str]  					=		variance * variance_noise

											_snake_case:   Optional[Any]  					=		pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=__snake_case       ,     pred_original_sample=__snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  torch.FloatTensor       ,     __snake_case	:  torch.IntTensor       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.alphas_cumprod.to(device=original_samples.device       ,     dtype=original_samples.dtype       )
											_snake_case:   int  					=		timesteps.to(original_samples.device       )

											_snake_case:   Optional[Any]  					=		alphas_cumprod[timesteps] ** 0.5
											_snake_case:   List[str]  					=		sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
																	_snake_case:   int  					=		sqrt_alpha_prod.unsqueeze(-1       )

											_snake_case:   Any  					=		(1 - alphas_cumprod[timesteps]) ** 0.5
											_snake_case:   List[Any]  					=		sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape       ) < len(original_samples.shape       ):
																	_snake_case:   str  					=		sqrt_one_minus_alpha_prod.unsqueeze(-1       )

											_snake_case:   int  					=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples












'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		tempfile.mkdtemp()

											# fmt: off
											_snake_case:   Union[str, Any]  					=		['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
											# fmt: on
											_snake_case:   str  					=		dict(zip(__snake_case       ,     range(len(__snake_case       )       )       )       )
											_snake_case:   str  					=		['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
											_snake_case:   Optional[int]  					=		{'unk_token': '<unk>'}

											_snake_case:   int  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file']       )
											_snake_case:   Optional[int]  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write(json.dumps(__snake_case       ) + '\n'       )
											with open(self.merges_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write('\n'.join(__snake_case       )       )

											_snake_case:   Dict  					=		{
											    'do_resize': True,
											    'size': 20,
											    'do_center_crop': True,
											    'crop_size': 18,
											    'do_normalize': True,
											    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											_snake_case:   Union[str, Any]  					=		os.path.join(self.tmpdirname       ,     __snake_case       )
											with open(self.image_processor_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	json.dump(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											shutil.rmtree(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		[np.random.randint(2_55       ,     size=(3, 30, 4_00)       ,     dtype=np.uinta       )]

											_snake_case:   Any  					=		[Image.fromarray(np.moveaxis(__snake_case       ,     0       ,     -1       )       ) for x in image_inputs]

											return image_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_tokenizer()
											_snake_case:   List[str]  					=		self.get_rust_tokenizer()
											_snake_case:   List[str]  					=		self.get_image_processor()

											_snake_case:   Union[str, Any]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(self.tmpdirname       ,     use_fast=__snake_case       )

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											_snake_case:   int  					=		CLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,     tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer       ,     __snake_case       )
											self.assertIsInstance(processor_fast.tokenizer       ,     __snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor       ,     __snake_case       )
											self.assertIsInstance(processor_fast.image_processor       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		CLIPProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											_snake_case:   List[Any]  					=		self.get_tokenizer(bos_token='(BOS)'       ,     eos_token='(EOS)'       )
											_snake_case:   List[Any]  					=		self.get_image_processor(do_normalize=__snake_case       ,     padding_value=1.0       )

											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(
											    self.tmpdirname       ,     bos_token='(BOS)'       ,     eos_token='(EOS)'       ,     do_normalize=__snake_case       ,     padding_value=1.0       )

											self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
											self.assertIsInstance(processor.tokenizer       ,     __snake_case       )

											self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   List[str]  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   List[str]  					=		self.prepare_image_inputs()

											_snake_case:   List[str]  					=		image_processor(__snake_case       ,     return_tensors='np'       )
											_snake_case:   Dict  					=		processor(images=__snake_case       ,     return_tensors='np'       )

											for key in input_image_proc.keys():
																	self.assertAlmostEqual(input_image_proc[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   List[str]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   int  					=		'lower newer'

											_snake_case:   str  					=		processor(text=__snake_case       )

											_snake_case:   List[Any]  					=		tokenizer(__snake_case       )

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.get_image_processor()
											_snake_case:   List[Any]  					=		self.get_tokenizer()

											_snake_case:   Tuple  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Optional[int]  					=		'lower newer'
											_snake_case:   Union[str, Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

											self.assertListEqual(list(inputs.keys()       )       ,     ['input_ids', 'attention_mask', 'pixel_values']       )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case       ):
																	processor()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_image_processor()
											_snake_case:   Dict  					=		self.get_tokenizer()

											_snake_case:   str  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   str  					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_snake_case:   List[Any]  					=		processor.batch_decode(__snake_case       )
											_snake_case:   Any  					=		tokenizer.batch_decode(__snake_case       )

											self.assertListEqual(__snake_case       ,     __snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Any  					=		'lower newer'
											_snake_case:   Optional[Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

											self.assertListEqual(list(inputs.keys()       )       ,     processor.model_input_names       )





