

import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 _lowerCamelCase							=False

if is_vision_available():
 from PIL import Image

 from transformers import (
     AutoProcessor,
     PixaStructImageProcessor,
     PixaStructProcessor,
     PreTrainedTokenizerFast,
     TaTokenizer,
 )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					,     )
@require_vision
@require_torch
class 						a_			(				unittest.TestCase	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE 		=PixaStructImageProcessor()
           SCREAMING_SNAKE_CASE 		=TaTokenizer.from_pretrained('t5-small'							)

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(_lowerCamelCase     ,_lowerCamelCase							)

           processor.save_pretrained(self.tmpdirname							)
      def 			_lowerCAmelCase	( self	:				Dict     ,**snake_case	:				Tuple							):
           return AutoProcessor.from_pretrained(self.tmpdirname     ,**_lowerCamelCase							).tokenizer
      def 			_lowerCAmelCase	( self	:				Dict     ,**snake_case	:				Any							):
           return AutoProcessor.from_pretrained(self.tmpdirname     ,**_lowerCamelCase							).image_processor
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           shutil.rmtree(self.tmpdirname							)
      def 			_lowerCAmelCase	( self	:				Optional[Any]							):
           SCREAMING_SNAKE_CASE 		=[np.random.randint(255     ,size=(3, 30, 400)     ,dtype=np.uinta							)]

           SCREAMING_SNAKE_CASE 		=[Image.fromarray(np.moveaxis(_lowerCamelCase     ,0     ,-1							)							) for x in image_inputs]

           return image_inputs
      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=self.get_tokenizer()     ,image_processor=self.get_image_processor()							)
           processor.save_pretrained(self.tmpdirname							)

           SCREAMING_SNAKE_CASE 		=self.get_tokenizer(bos_token='(BOS)'     ,eos_token='(EOS)'							)
           SCREAMING_SNAKE_CASE 		=self.get_image_processor(do_normalize=_lowerCamelCase     ,padding_value=1.0							)

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor.from_pretrained(
               self.tmpdirname     ,bos_token='(BOS)'     ,eos_token='(EOS)'     ,do_normalize=_lowerCamelCase     ,padding_value=1.0							)

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

           self.assertEqual(processor.image_processor.to_json_string()     ,image_processor_add_kwargs.to_json_string()							)
           self.assertIsInstance(processor.image_processor     ,_lowerCamelCase							)
      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE 		=image_processor(_lowerCamelCase     ,return_tensors='np'							)
           SCREAMING_SNAKE_CASE 		=processor(images=_lowerCamelCase     ,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 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		="""lower newer"""

           SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		=tokenizer(_lowerCamelCase     ,return_token_type_ids=_lowerCamelCase     ,add_special_tokens=_lowerCamelCase							)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]     ,encoded_processor[key]							)
      def 			_lowerCAmelCase	( self	:				Any							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		="""lower newer"""
           SCREAMING_SNAKE_CASE 		=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase     ,images=_lowerCamelCase							)

           self.assertListEqual(
               list(inputs.keys()							)     ,['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']							)

           # test if it raises when no input is passed
           with pytest.raises(_lowerCamelCase							):
                processor()
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		="""lower newer"""
           SCREAMING_SNAKE_CASE 		=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase     ,images=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		=[512, 1024, 2048, 4096]
           SCREAMING_SNAKE_CASE 		=[770, 770, 770, 770]
           # with text
           for i, max_patch in enumerate(_lowerCamelCase							):
                SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase     ,images=_lowerCamelCase     ,max_patches=_lowerCamelCase							)
                self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,_lowerCamelCase							)
                self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,expected_hidden_size[i]							)

           # without text input
           for i, max_patch in enumerate(_lowerCamelCase							):
                SCREAMING_SNAKE_CASE 		=processor(images=_lowerCamelCase     ,max_patches=_lowerCamelCase							)
                self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,_lowerCamelCase							)
                self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,expected_hidden_size[i]							)
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE 		=processor.batch_decode(_lowerCamelCase							)
           SCREAMING_SNAKE_CASE 		=tokenizer.batch_decode(_lowerCamelCase							)

           self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase							)


      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=self.get_image_processor()
           SCREAMING_SNAKE_CASE 		=self.get_tokenizer()

           SCREAMING_SNAKE_CASE 		=PixaStructProcessor(tokenizer=_lowerCamelCase     ,image_processor=_lowerCamelCase							)

           SCREAMING_SNAKE_CASE 		="""lower newer"""
           SCREAMING_SNAKE_CASE 		=self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase     ,images=_lowerCamelCase							)

           # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
           self.assertListEqual(
               list(inputs.keys()							)     ,['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']							)

           SCREAMING_SNAKE_CASE 		=processor(text=_lowerCamelCase							)

           # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
           self.assertListEqual(list(inputs.keys()							)     ,['input_ids', 'attention_mask']							)






'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
