





from typing import List, Optional, Union

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

class  __A(						__UpperCAmelCase ):
     __A	  =       ["image_processor", "tokenizer"]
     __A	  =       "BlipImageProcessor"
     __A	  =       "AutoTokenizer"

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						False
            super().__init__(A,       A      )
            _UpperCamelCase              =						self.image_processor

     def __call__(      self,       A = None,       A = None,       A = True,       A = False,       A = None,       A = None,       A = 0,       A = None,       A = None,       A = False,       A = False,       A = False,       A = False,       A = False,       A = True,       A = None,       **A,       ):





            """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:
                   _UpperCamelCase              =						self.tokenizer
                   _UpperCamelCase              =						self.tokenizer(
                       text=A,       add_special_tokens=A,       padding=A,       truncation=A,       max_length=A,       stride=A,       pad_to_multiple_of=A,       return_attention_mask=A,       return_overflowing_tokens=A,       return_special_tokens_mask=A,       return_offsets_mapping=A,       return_token_type_ids=A,       return_length=A,       verbose=A,       return_tensors=A,       **A,       )
                   return text_encoding

            # add pixel_values
            _UpperCamelCase              =						self.image_processor(A,       return_tensors=A      )

            if text is not None:
                   _UpperCamelCase              =						self.tokenizer(
                       text=A,       add_special_tokens=A,       padding=A,       truncation=A,       max_length=A,       stride=A,       pad_to_multiple_of=A,       return_attention_mask=A,       return_overflowing_tokens=A,       return_special_tokens_mask=A,       return_offsets_mapping=A,       return_token_type_ids=A,       return_length=A,       verbose=A,       return_tensors=A,       **A,       )
            else:
                   _UpperCamelCase              =						None

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

            return encoding_image_processor

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       *A,       **A      ):





            """simple docstring"""

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



     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.tokenizer.model_input_names
            _UpperCamelCase              =						self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )













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  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

            with self.assertWarns(A      ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       11      )

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






