






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:
 __snake_case							 =  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       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Union[str, Any]     							=       tempfile.mkdtemp()

          UpperCamelCase	:List[str]     							=       PixaStructImageProcessor()
          UpperCamelCase	:Tuple     							=       TaTokenizer.from_pretrained('''t5-small'''			)

          UpperCamelCase	:str     							=       PixaStructProcessor(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          processor.save_pretrained(self.tmpdirname			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          return AutoProcessor.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			).tokenizer
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				str:
          return AutoProcessor.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			).image_processor
      def    UpperCAmelCase						(    self			)    ->				str:
          shutil.rmtree(self.tmpdirname			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	:Any     							=       [np.random.randint(255   ,    size=(3, 30, 400)   ,    dtype=np.uinta			)]

          UpperCamelCase	:List[str]     							=       [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_   ,    0   ,    -1			)			) for x in image_inputs]

          return image_inputs
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          UpperCamelCase	:Union[str, Any]     							=       PixaStructProcessor(tokenizer=self.get_tokenizer()   ,    image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase	:Union[str, Any]     							=       self.get_tokenizer(bos_token='''(BOS)'''   ,    eos_token='''(EOS)'''			)
          UpperCamelCase	:Optional[int]     							=       self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_   ,    padding_value=1.0			)

          UpperCamelCase	:str     							=       PixaStructProcessor.from_pretrained(
              self.tmpdirname   ,    bos_token='''(BOS)'''   ,    eos_token='''(EOS)'''   ,    do_normalize=SCREAMING_SNAKE_CASE_   ,    padding_value=1.0			)

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

          self.assertEqual(processor.image_processor.to_json_string()   ,    image_processor_add_kwargs.to_json_string()			)
          self.assertIsInstance(processor.image_processor   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Tuple     							=       self.get_image_processor()
          UpperCamelCase	:Any     							=       self.get_tokenizer()

          UpperCamelCase	:Optional[Any]     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Any     							=       self.prepare_image_inputs()

          UpperCamelCase	:Union[str, Any]     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''np'''			)
          UpperCamelCase	:Any     							=       processor(images=SCREAMING_SNAKE_CASE_   ,    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			)    ->				Tuple:
          UpperCamelCase	:int     							=       self.get_image_processor()
          UpperCamelCase	:Optional[Any]     							=       self.get_tokenizer()

          UpperCamelCase	:Any     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:List[Any]     							=       '''lower newer'''

          UpperCamelCase	:str     							=       processor(text=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer(SCREAMING_SNAKE_CASE_   ,    return_token_type_ids=SCREAMING_SNAKE_CASE_   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]   ,    encoded_processor[key]			)
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:str     							=       self.get_image_processor()
          UpperCamelCase	:List[str]     							=       self.get_tokenizer()

          UpperCamelCase	:int     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:str     							=       '''lower newer'''
          UpperCamelCase	:Tuple     							=       self.prepare_image_inputs()

          UpperCamelCase	:Optional[int]     							=       processor(text=SCREAMING_SNAKE_CASE_   ,    images=SCREAMING_SNAKE_CASE_			)

          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(SCREAMING_SNAKE_CASE_			):
              processor()
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	:List[str]     							=       self.get_image_processor()
          UpperCamelCase	:List[str]     							=       self.get_tokenizer()

          UpperCamelCase	:List[Any]     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Tuple     							=       '''lower newer'''
          UpperCamelCase	:int     							=       self.prepare_image_inputs()

          UpperCamelCase	:Optional[int]     							=       processor(text=SCREAMING_SNAKE_CASE_   ,    images=SCREAMING_SNAKE_CASE_			)

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

          # without text input
          for i, max_patch in enumerate(SCREAMING_SNAKE_CASE_			):
              UpperCamelCase	:Union[str, Any]     							=       processor(images=SCREAMING_SNAKE_CASE_   ,    max_patches=SCREAMING_SNAKE_CASE_			)
              self.assertEqual(inputs['''flattened_patches'''][0].shape[0]   ,    SCREAMING_SNAKE_CASE_			)
              self.assertEqual(inputs['''flattened_patches'''][0].shape[1]   ,    expected_hidden_size[i]			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:List[str]     							=       self.get_image_processor()
          UpperCamelCase	:List[str]     							=       self.get_tokenizer()

          UpperCamelCase	:Optional[int]     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:Union[str, Any]     							=       processor.batch_decode(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Tuple     							=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

          self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)




      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Optional[int]     							=       self.get_image_processor()
          UpperCamelCase	:Dict     							=       self.get_tokenizer()

          UpperCamelCase	:Optional[int]     							=       PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    image_processor=SCREAMING_SNAKE_CASE_			)

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

          UpperCamelCase	:List[str]     							=       processor(text=SCREAMING_SNAKE_CASE_   ,    images=SCREAMING_SNAKE_CASE_			)

          # 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''']			)

          UpperCamelCase	:Optional[Any]     							=       processor(text=SCREAMING_SNAKE_CASE_			)

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













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





