




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,
     BertTokenizerFast,
     BlipImageProcessor,
     GPTaTokenizer,
     InstructBlipProcessor,
     PreTrainedTokenizerFast,
 )







@require_vision
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    Optional[int]    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       tempfile.mkdtemp()

       lowerCamelCase_							=       BlipImageProcessor()
       lowerCamelCase_							=       GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'    )
       lowerCamelCase_							=       BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'    )

       lowerCamelCase_							=       InstructBlipProcessor(A_		,				A_		,				A_    )

       processor.save_pretrained(self.tmpdirname    )

 def 							a__					(							self   :    Optional[int]		,				**A_   :    Optional[int]    )  ->      Dict:

       """simple docstring"""






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

 def 							a__					(							self   :    List[str]		,				**A_   :    str    )  ->      Optional[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    Tuple		,				**A_   :    Any    )  ->      Optional[int]:

       """simple docstring"""






       return AutoProcessor.from_pretrained(self.tmpdirname		,				**A_    ).qformer_tokenizer

 def 							a__					(							self   :    str    )  ->      str:

       """simple docstring"""






       shutil.rmtree(self.tmpdirname    )

 def 							a__					(							self   :    Dict    )  ->      Dict:

       """simple docstring"""






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

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

       return image_inputs

 def 							a__					(							self   :    Tuple    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
       processor.save_pretrained(self.tmpdirname    )

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

       lowerCamelCase_							=       InstructBlipProcessor.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_    )
       self.assertIsInstance(processor.qformer_tokenizer		,				A_    )

 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.get_image_processor()
       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       self.get_qformer_tokenizer()

       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=A_		,				image_processor=A_		,				qformer_tokenizer=A_    )

       lowerCamelCase_							=       self.prepare_image_inputs()

       lowerCamelCase_							=       image_processor(A_		,				return_tensors='np'    )
       lowerCamelCase_							=       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 							a__					(							self   :    Union[str, Any]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.get_image_processor()
       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       self.get_qformer_tokenizer()

       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=A_		,				image_processor=A_		,				qformer_tokenizer=A_    )

       lowerCamelCase_							=       'lower newer'

       lowerCamelCase_							=       processor(text=A_    )

       lowerCamelCase_							=       tokenizer(A_		,				return_token_type_ids=A_    )
       lowerCamelCase_							=       qformer_tokenizer(A_		,				return_token_type_ids=A_    )

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

       for key in encoded_tokens_qformer.keys():
             self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor['qformer_' + key]    )

 def 							a__					(							self   :    Tuple    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.get_image_processor()
       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       self.get_qformer_tokenizer()

       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=A_		,				image_processor=A_		,				qformer_tokenizer=A_    )

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

       lowerCamelCase_							=       processor(text=A_		,				images=A_    )

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

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

 def 							a__					(							self   :    Optional[int]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       self.get_image_processor()
       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       self.get_qformer_tokenizer()

       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=A_		,				image_processor=A_		,				qformer_tokenizer=A_    )

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

       lowerCamelCase_							=       processor.batch_decode(A_    )
       lowerCamelCase_							=       tokenizer.batch_decode(A_    )

       self.assertListEqual(A_		,				A_    )





 def 							a__					(							self   :    Tuple    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.get_image_processor()
       lowerCamelCase_							=       self.get_tokenizer()
       lowerCamelCase_							=       self.get_qformer_tokenizer()

       lowerCamelCase_							=       InstructBlipProcessor(
           tokenizer=A_		,				image_processor=A_		,				qformer_tokenizer=A_    )

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

       lowerCamelCase_							=       processor(text=A_		,				images=A_    )

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





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__magic_name__  							=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
     import torch

     print('''Torch version:''', torch.__version__)
     print('''Cuda available:''', torch.cuda.is_available())
     print('''Cuda version:''', torch.version.cuda)
     print('''CuDNN version:''', torch.backends.cudnn.version())
     print('''Number of GPUs available:''', torch.cuda.device_count())
     print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
     print('''Torch version:''', None)

try:
     import deepspeed

     print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
     print('''DeepSpeed version:''', None)

try:
     import tensorflow as tf

     print('''TensorFlow version:''', tf.__version__)
     print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
     print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
     print('''TensorFlow version:''', None)



