





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class      lowercase__    (							unittest.TestCase  ):







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		tempfile.mkdtemp()

       # fmt: off
       lowerCAmelCase_					:       Union[str, Any]    =		["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
       # fmt: on
       lowerCAmelCase_					:       Dict    =		os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]						)
       with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""						) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

       lowerCAmelCase_					:       int    =		{
           "do_resize": True,
           "size": {"height": 18, "width": 18},
           "do_normalize": True,
           "image_mean": [0.5, 0.5, 0.5],
           "image_std": [0.5, 0.5, 0.5],
       }
       lowerCAmelCase_					:       List[str]    =		os.path.join(self.tmpdirname    ,						lowercase_						)
       with open(self.image_processor_file    ,						"""w"""    ,						encoding="""utf-8"""						) as fp:
            json.dump(lowercase_    ,						lowercase_						)







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       return BertTokenizer.from_pretrained(self.tmpdirname    ,						**lowercase_						)







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       return ViTImageProcessor.from_pretrained(self.tmpdirname    ,						**lowercase_						)







  def 	UpperCAmelCase__		(	self						):
       shutil.rmtree(self.tmpdirname						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		[np.random.randint(255    ,						size=(3, 30, 400)    ,						dtype=np.uinta						)]

       lowerCAmelCase_					:       Tuple    =		[Image.fromarray(np.moveaxis(lowercase_    ,						0    ,						-1						)						) for x in image_inputs]

       return image_inputs







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		self.get_tokenizer()
       lowerCAmelCase_					:       Optional[Any]    =		self.get_image_processor()

       lowerCAmelCase_					:       Dict    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       processor.save_pretrained(self.tmpdirname						)
       lowerCAmelCase_					:       Tuple    =		VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

       self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab()						)
       self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast)						)

       self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor.to_json_string()						)
       self.assertIsInstance(processor.image_processor    ,						lowercase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       lowerCAmelCase_					:       Any    =		self.get_tokenizer(bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""						)
       lowerCAmelCase_					:       List[Any]    =		self.get_image_processor(do_normalize=lowercase_    ,						padding_value=1.0						)

       lowerCAmelCase_					:       int    =		VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname    ,						bos_token="""(BOS)"""    ,						eos_token="""(EOS)"""    ,						do_normalize=lowercase_    ,						padding_value=1.0						)

       self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast)						)

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		self.get_image_processor()
       lowerCAmelCase_					:       int    =		self.get_tokenizer()

       lowerCAmelCase_					:       Tuple    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       lowerCAmelCase_					:       int    =		self.prepare_image_inputs()

       lowerCAmelCase_					:       Tuple    =		image_processor(lowercase_    ,						return_tensors="""np"""						)
       lowerCAmelCase_					:       Optional[int]    =		processor(images=lowercase_    ,						return_tensors="""np"""						)

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[Any]    =		self.get_image_processor()
       lowerCAmelCase_					:       Any    =		self.get_tokenizer()

       lowerCAmelCase_					:       Optional[int]    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       lowerCAmelCase_					:       Dict    =		"lower newer"

       lowerCAmelCase_					:       Tuple    =		processor(text=lowercase_						)

       lowerCAmelCase_					:       str    =		tokenizer(lowercase_						)

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		self.get_image_processor()
       lowerCAmelCase_					:       int    =		self.get_tokenizer()

       lowerCAmelCase_					:       int    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       lowerCAmelCase_					:       Optional[Any]    =		"lower newer"
       lowerCAmelCase_					:       Tuple    =		self.prepare_image_inputs()

       lowerCAmelCase_					:       Optional[int]    =		processor(text=lowercase_    ,						images=lowercase_						)

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

       # test if it raises when no input is passed
       with self.assertRaises(lowercase_						):
            processor()







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		self.get_image_processor()
       lowerCAmelCase_					:       List[str]    =		self.get_tokenizer()

       lowerCAmelCase_					:       List[Any]    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       lowerCAmelCase_					:       Optional[int]    =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCAmelCase_					:       str    =		processor.batch_decode(lowercase_						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.batch_decode(lowercase_						)

       self.assertListEqual(lowercase_    ,						lowercase_						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		self.get_image_processor()
       lowerCAmelCase_					:       List[str]    =		self.get_tokenizer()

       lowerCAmelCase_					:       str    =		VisionTextDualEncoderProcessor(tokenizer=lowercase_    ,						image_processor=lowercase_						)

       lowerCAmelCase_					:       List[Any]    =		"lower newer"
       lowerCAmelCase_					:       Dict    =		self.prepare_image_inputs()

       lowerCAmelCase_					:       str    =		processor(text=lowercase_    ,						images=lowercase_						)

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







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
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
