



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,
)
__A       =						logging.getLogger(__name__)



def 	__a					(   lowerCAmelCase_       : str    )				->			Tuple:






      '''simple docstring'''



      UpperCAmelCase_=       git.Repo(search_parent_directories=lowerCAmelCase_    )
      UpperCAmelCase_=       {
          """repo_id""": str(lowerCAmelCase_    ),
          """repo_sha""": str(repo.head.object.hexsha    ),
          """repo_branch""": str(repo.active_branch    ),
      }

      with open(os.path.join(lowerCAmelCase_      ,"""git_log.json"""    )      ,"""w"""    ) as f:
            json.dump(lowerCAmelCase_      ,lowerCAmelCase_      ,indent=4    )



def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			Union[str, Any]:






      '''simple docstring'''



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

      assert torch.cuda.is_available()

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

            UpperCAmelCase_=       int(os.environ["""WORLD_SIZE"""]    )
            UpperCAmelCase_=       int(os.environ["""N_GPU_NODE"""]    )
            UpperCAmelCase_=       int(os.environ["""RANK"""]    )

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

            UpperCAmelCase_=       1
            UpperCAmelCase_=       0
            UpperCAmelCase_=       0
            UpperCAmelCase_=       0
            UpperCAmelCase_=       1
            UpperCAmelCase_=       1
            UpperCAmelCase_=       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
      UpperCAmelCase_=       params.node_id == 0 and params.local_rank == 0
      UpperCAmelCase_=       params.n_nodes > 1

      # summary
      UpperCAmelCase_=       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					(   lowerCAmelCase_       : Dict    )				->			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 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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

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

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(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=__UpperCAmelCase						,  padding_value=1.0					)

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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




