




'''simple docstring'''



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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	tempfile.mkdtemp()

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

            __snake_case            =	BlipaProcessor(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            processor.save_pretrained(self.tmpdirname	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




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

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

            return image_inputs


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	BlipaProcessor(tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()	)
            processor.save_pretrained(self.tmpdirname	)

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

            __snake_case            =	BlipaProcessor.from_pretrained(
                self.tmpdirname     ,	bos_token='''(BOS)'''     ,	eos_token='''(EOS)'''     ,	do_normalize=__SCREAMING_SNAKE_CASE     ,	padding_value=1.0	)

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

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


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.prepare_image_inputs()

            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)
            __snake_case            =	processor(images=__SCREAMING_SNAKE_CASE     ,	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    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''lower newer'''

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer(__SCREAMING_SNAKE_CASE     ,	return_token_type_ids=__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE     ,	images=__SCREAMING_SNAKE_CASE	)

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

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


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



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	processor.batch_decode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE     ,	images=__SCREAMING_SNAKE_CASE	)

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








# 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)



