

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
lowercase_   							=  logging.getLogger(__name__)
def 	a (     A__      :  str	)       -> List[Any]:





      """simple docstring"""



      _lowercase      			=git.Repo(search_parent_directories=A__	)
      _lowercase      			={
          'repo_id': str(A__	),
          'repo_sha': str(repo.head.object.hexsha	),
          'repo_branch': str(repo.active_branch	),
      }

      with open(os.path.join(A__ ,  'git_log.json'	) ,  'w'	) as f:
            json.dump(A__ ,  A__ ,  indent=4	)
def 	a (     A__      :  Dict	)       -> Optional[int]:





      """simple docstring"""



      if params.n_gpu <= 0:
            _lowercase      			=0
            _lowercase      			=-1
            _lowercase      			=True
            _lowercase      			=False
            return

      assert torch.cuda.is_available()

      logger.info('Initializing GPUs'	)
      if params.n_gpu > 1:
            assert params.local_rank != -1

            _lowercase      			=int(os.environ['WORLD_SIZE']	)
            _lowercase      			=int(os.environ['N_GPU_NODE']	)
            _lowercase      			=int(os.environ['RANK']	)

            # number of nodes / node ID
            _lowercase      			=params.world_size // params.n_gpu_per_node
            _lowercase      			=params.global_rank // params.n_gpu_per_node
            _lowercase      			=True

            assert params.n_nodes == int(os.environ['N_NODES']	)
            assert params.node_id == int(os.environ['NODE_RANK']	)

      # local job (single GPU)
      else:
            assert params.local_rank == -1

            _lowercase      			=1
            _lowercase      			=0
            _lowercase      			=0
            _lowercase      			=0
            _lowercase      			=1
            _lowercase      			=1
            _lowercase      			=False

      # sanity checks
      assert params.n_nodes >= 1
      assert 0 <= params.node_id < params.n_nodes
      assert 0 <= params.local_rank <= params.global_rank < params.world_size
      assert params.world_size == params.n_nodes * params.n_gpu_per_node

      # define whether this is the master process / if we are in multi-node distributed mode
      _lowercase      			=params.node_id == 0 and params.local_rank == 0
      _lowercase      			=params.n_nodes > 1

      # summary
      _lowercase      			=F'''--- Global rank: {params.global_rank} - '''
      logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes	)
      logger.info(PREFIX + 'Node ID        : %i' % params.node_id	)
      logger.info(PREFIX + 'Local rank     : %i' % params.local_rank	)
      logger.info(PREFIX + 'World size     : %i' % params.world_size	)
      logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node	)
      logger.info(PREFIX + 'Master         : %s' % str(params.is_master	)	)
      logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node	)	)
      logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu	)	)
      logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()	)

      # set GPU device
      torch.cuda.set_device(params.local_rank	)

      # initialize multi-GPU
      if params.multi_gpu:
            logger.info('Initializing PyTorch distributed'	)
            torch.distributed.init_process_group(
                init_method='env://' ,  backend='nccl' ,  )







def 	a (     A__      :  int	)       -> Any:





      """simple docstring"""



      np.random.seed(args.seed	)
      torch.manual_seed(args.seed	)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed	)





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()

        _lowercase      			=[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            '的',
            '价',
            '格',
            '是',
            '15',
            '便',
            'alex',
            '##andra',
            '，',
            '。',
            '-',
            't',
            'shirt',
        ]
        _lowercase      			=os.path.join(self.tmpdirname				,  VOCAB_FILES_NAMES['vocab_file']   )
        with open(self.vocab_file				,  'w'				,  encoding='utf-8'   ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

        _lowercase      			={
            'do_resize': True,
            'size': {'height': 224, 'width': 224},
            'do_center_crop': True,
            'crop_size': {'height': 18, 'width': 18},
            'do_normalize': True,
            'image_mean': [0.48145466, 0.4578275, 0.40821073],
            'image_std': [0.26862954, 0.26130258, 0.27577711],
            'do_convert_rgb': True,
        }
        _lowercase      			=os.path.join(self.tmpdirname				,  lowerCAmelCase   )
        with open(self.image_processor_file				,  'w'				,  encoding='utf-8'   ) as fp:
              json.dump(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






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

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

        return image_inputs




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()
        _lowercase      			=self.get_rust_tokenizer()
        _lowercase      			=self.get_image_processor()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_slow.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,  use_fast=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_fast.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname   )

        self.assertEqual(processor_slow.tokenizer.get_vocab()				,  tokenizer_slow.get_vocab()   )
        self.assertEqual(processor_fast.tokenizer.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertEqual(tokenizer_slow.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertIsInstance(processor_slow.tokenizer				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor_slow.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertEqual(processor_fast.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertIsInstance(processor_slow.image_processor				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.image_processor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,  image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        _lowercase      			=self.get_tokenizer(cls_token='(CLS)'				,  sep_token='(SEP)'   )
        _lowercase      			=self.get_image_processor(do_normalize=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname				,  cls_token='(CLS)'				,  sep_token='(SEP)'				,  do_normalize=lowerCAmelCase   )

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

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




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=image_processor(lowerCAmelCase				,  return_tensors='np'   )
        _lowercase      			=processor(images=lowerCAmelCase				,  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   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'

        _lowercase      			=processor(text=lowerCAmelCase   )

        _lowercase      			=tokenizer(lowerCAmelCase   )

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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

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

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




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=processor.batch_decode(lowerCAmelCase   )
        _lowercase      			=tokenizer.batch_decode(lowerCAmelCase   )

        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

        self.assertListEqual(list(inputs.keys()   )				,  processor.model_input_names   )


