





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						tempfile.mkdtemp()

            _UpperCamelCase              =						BlipImageProcessor()
            _UpperCamelCase              =						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''      )

            _UpperCamelCase              =						BlipProcessor(A,       A      )

            processor.save_pretrained(self.tmpdirname      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,       **A      ).tokenizer

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            return AutoProcessor.from_pretrained(self.tmpdirname,       **A      ).image_processor

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						[np.random.randint(255,       size=(3, 30, 400),       dtype=np.uinta      )]

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

            return image_inputs

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						BlipProcessor(tokenizer=self.get_tokenizer(),       image_processor=self.get_image_processor()      )
            processor.save_pretrained(self.tmpdirname      )

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

            _UpperCamelCase              =						BlipProcessor.from_pretrained(
                self.tmpdirname,       bos_token='''(BOS)''',       eos_token='''(EOS)''',       do_normalize=A,       padding_value=1.0      )

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

            self.assertEqual(processor.image_processor.to_json_string(),       image_processor_add_kwargs.to_json_string()      )
            self.assertIsInstance(processor.image_processor,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						image_processor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						processor(images=A,       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      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A,       return_token_type_ids=A      )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key],       encoded_processor[key]      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(text=A,       images=A      )

            self.assertListEqual(list(inputs.keys()      ),       ['''pixel_values''', '''input_ids''', '''attention_mask''']      )

            # test if it raises when no input is passed
            with pytest.raises(A      ):
                   processor()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

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

            _UpperCamelCase              =						processor.batch_decode(A      )
            _UpperCamelCase              =						tokenizer.batch_decode(A      )

            self.assertListEqual(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_image_processor()
            _UpperCamelCase              =						self.get_tokenizer()

            _UpperCamelCase              =						BlipProcessor(tokenizer=A,       image_processor=A      )

            _UpperCamelCase              =						'''lower newer'''
            _UpperCamelCase              =						self.prepare_image_inputs()

            _UpperCamelCase              =						processor(text=A,       images=A      )

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













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      )






