


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,
)
SCREAMING_SNAKE_CASE						:int			=  logging.getLogger(__name__)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->Union[str, Any]:
   '''simple docstring'''






   snake_case_            =       git.Repo(search_parent_directories=lowerCAmelCase_				)
   snake_case_            =       {
       "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       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->List[Any]:
   '''simple docstring'''






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

   assert torch.cuda.is_available()

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

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

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

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

   # summary
   snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]				)->List[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 tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor


class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"clusters"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_resize"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"size"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_normalize"	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       json.loads(image_processor.to_json_string()	)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					obj[key]	)	)
        else:
           self.assertEqual(obj[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"image_processor.json"	)
        image_processor_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_json_file(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



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




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  @unittest.skip("ImageGPT requires clusters at initialization"	)
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






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






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



@require_vision
@require_torch
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

     # test non-batched
     snake_case_            =       image_processing(images[0]     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




