


"""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 _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	Any							  =  '''Pix2StructImageProcessor'''
   __UpperCAmelCase			:	Optional[int]							  =  ('''T5Tokenizer''', '''T5TokenizerFast''')







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   False
       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 2048						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''simple docstring'''




       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:
           snake_case   :				Optional[Any]				 =   self.tokenizer
           snake_case   :				str				 =   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
           snake_case   :				Tuple				 =   self.image_processor(
               UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     max_patches=UpperCamelCase__						,     **UpperCamelCase__						)
       else:
           # add pixel_values and bbox
           snake_case   :				int				 =   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:
           snake_case   :				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:
               snake_case   :				Optional[int]				 =   text_encoding.pop("attention_mask"						)
           if "input_ids" in text_encoding:
               snake_case   :				str				 =   text_encoding.pop("input_ids"						)
       else:
           snake_case   :				str				 =   None

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

       return encoding_image_processor







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   self.tokenizer.model_input_names
       snake_case   :				List[str]				 =   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)









"""simple docstring"""



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer







@require_tf
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
       for a, b in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertAlmostEqual(UpperCamelCase__						,     UpperCamelCase__						,     delta=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(UpperCamelCase__						):
           accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step						,     3						)
       self.assertEqual(len(accumulator.gradients						)						,     1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [-2.0, 5.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [0.0, 0.0]						,     tol=1e-2						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   None
       ops.enable_eager_execution_internal()
       snake_case   :				str				 =   tf.config.list_physical_devices("CPU"						)
       if len(UpperCamelCase__						) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]						,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       snake_case   :				Optional[int]				 =   tf.config.list_logical_devices(device_type="CPU"						)
       snake_case   :				List[str]				 =   tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
           snake_case   :				int				 =   GradientAccumulator()
           snake_case   :				int				 =   tf.Variable([4.0, 3.0]						)
           snake_case ,snake_case   :				Any				 =   create_optimizer(5e-5						,     10						,     5						)
           snake_case   :				Tuple				 =   tf.Variable([0.0, 0.0]						,     trainable=UpperCamelCase__						)

       def accumulate_on_replica(UpperCamelCase__						):
           accumulator([gradient]						)

       def apply_on_replica():
           optimizer.apply_gradients(list(zip(accumulator.gradients						,     [variable]						)						)						)

       @tf.function
       def accumulate(UpperCamelCase__						,     UpperCamelCase__						):
           with strategy.scope():
               snake_case   :				Union[str, Any]				 =   strategy.experimental_local_results(UpperCamelCase__						)
               local_variables[0].assign(UpperCamelCase__						)
               local_variables[1].assign(UpperCamelCase__						)
               strategy.run(UpperCamelCase__						,     args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
           with strategy.scope():
               strategy.run(UpperCamelCase__						)

       def _check_local_values(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				List[Any]				 =   strategy.experimental_local_results(accumulator._gradients[0]						)
           self.assertListAlmostEqual(values[0].value()						,     UpperCamelCase__						,     tol=1e-2						)
           self.assertListAlmostEqual(values[1].value()						,     UpperCamelCase__						,     tol=1e-2						)

       accumulate([1.0, 2.0]						,     [-1.0, 1.0]						)
       accumulate([3.0, -1.0]						,     [-1.0, -1.0]						)
       accumulate([-2.0, 2.0]						,     [3.0, -2.0]						)
       self.assertEqual(accumulator.step						,     3						)
       _check_local_values([2.0, 3.0]						,     [1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,     [4.0, 3.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       _check_local_values([0.0, 0.0]						,     [0.0, 0.0]						)





