


"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 		snake_case_(       a__			):
     __UpperCamelCase  =					['''image_processor''', '''tokenizer''']
     __UpperCamelCase  =					'''Pix2StructImageProcessor'''
     __UpperCamelCase  =					('''T5Tokenizer''', '''T5TokenizerFast''')






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	str       =   False
           super().__init__(UpperCamelCase_					,				UpperCamelCase_				)






     def __call__(	self  :					Tuple					,				UpperCamelCase_  :					Union[str, Any]=None					,				UpperCamelCase_  :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Union[bool, str, PaddingStrategy] = False					,				UpperCamelCase_  :					Union[bool, str, TruncationStrategy] = None					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					Optional[int] = 2_0_4_8					,				UpperCamelCase_  :					int = 0					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = False					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[Union[str, TensorType]] = None					,				**UpperCamelCase_  :					str					,				):
           if images is None and text is None:
                 raise ValueError('''You have to specify either images or text.'''				)

           # Get only text
           if images is None and not self.image_processor.is_vqa:
                 lowerCAmelCase					:	str       =   self.tokenizer
                 lowerCAmelCase					:	Optional[int]       =   self.tokenizer(
                     text=UpperCamelCase_					,				add_special_tokens=UpperCamelCase_					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_					,				max_length=UpperCamelCase_					,				stride=UpperCamelCase_					,				pad_to_multiple_of=UpperCamelCase_					,				return_attention_mask=UpperCamelCase_					,				return_overflowing_tokens=UpperCamelCase_					,				return_special_tokens_mask=UpperCamelCase_					,				return_offsets_mapping=UpperCamelCase_					,				return_token_type_ids=UpperCamelCase_					,				return_length=UpperCamelCase_					,				verbose=UpperCamelCase_					,				return_tensors=UpperCamelCase_					,				**UpperCamelCase_					,				)
                 return text_encoding

           if not self.image_processor.is_vqa:
                 # add pixel_values
                 lowerCAmelCase					:	Any       =   self.image_processor(
                     UpperCamelCase_					,				return_tensors=UpperCamelCase_					,				max_patches=UpperCamelCase_					,				**UpperCamelCase_				)
           else:
                 # add pixel_values and bbox
                 lowerCAmelCase					:	Tuple       =   self.image_processor(
                     UpperCamelCase_					,				return_tensors=UpperCamelCase_					,				max_patches=UpperCamelCase_					,				header_text=UpperCamelCase_					,				**UpperCamelCase_				)

           if text is not None and not self.image_processor.is_vqa:
                 lowerCAmelCase					:	Optional[int]       =   self.tokenizer(
                     text=UpperCamelCase_					,				add_special_tokens=UpperCamelCase_					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_					,				max_length=UpperCamelCase_					,				stride=UpperCamelCase_					,				pad_to_multiple_of=UpperCamelCase_					,				return_attention_mask=UpperCamelCase_					,				return_overflowing_tokens=UpperCamelCase_					,				return_special_tokens_mask=UpperCamelCase_					,				return_offsets_mapping=UpperCamelCase_					,				return_token_type_ids=UpperCamelCase_					,				return_length=UpperCamelCase_					,				verbose=UpperCamelCase_					,				return_tensors=UpperCamelCase_					,				**UpperCamelCase_					,				)

                 if "attention_mask" in text_encoding:
                       lowerCAmelCase					:	Any       =   text_encoding.pop('''attention_mask'''				)
                 if "input_ids" in text_encoding:
                       lowerCAmelCase					:	Tuple       =   text_encoding.pop('''input_ids'''				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   None

           if text_encoding is not None:
                 encoding_image_processor.update(UpperCamelCase_				)

           return encoding_image_processor






     def      lowerCamelCase__     (	self  :					str					,				*UpperCamelCase_  :					int					,				**UpperCamelCase_  :					Optional[Any]				):
           return self.tokenizer.batch_decode(*UpperCamelCase_					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple					,				*UpperCamelCase_  :					int					,				**UpperCamelCase_  :					Dict				):
           return self.tokenizer.decode(*UpperCamelCase_					,				**UpperCamelCase_				)






     @property
     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Any       =   self.tokenizer.model_input_names
           lowerCAmelCase					:	Any       =   self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






"""simple docstring"""





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )



@require_torch
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	str       =   3
           lowerCAmelCase					:	Tuple       =   2_5_0

           lowerCAmelCase					:	Optional[Any]       =   ids_tensor((batch_size, length)					,				UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   torch.ones((batch_size, length)					,				device=UpperCamelCase_					,				dtype=torch.float				) / length
           return input_ids, scores






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)

           lowerCAmelCase					:	Union[str, Any]       =   StoppingCriteriaList(
               [
                   MaxLengthCriteria(max_length=1_0				),
                   MaxTimeCriteria(max_time=0.1				),
               ]				)

           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxLengthCriteria(max_length=1_0				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[Any]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxNewTokensCriteria(start_length=5					,				max_new_tokens=5				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	Dict       =   StoppingCriteriaList([criteria]				)
           self.assertEqual(criteria_list.max_length					,				1_0				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   self._get_tensors(5				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1					,				initial_timestamp=time.time() - 0.2				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					str				):
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_0				)

           with self.assertWarns(UpperCamelCase_				):
                 validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_1				)

           lowerCAmelCase					:	str       =   validate_stopping_criteria(StoppingCriteriaList()					,				1_1				)

           self.assertEqual(len(UpperCamelCase_				)					,				1				)


