

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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=7   ,	A__=3   ,	A__=18   ,	A__=30   ,	A__=400   ,	A__=True   ,	A__=None   ,	A__=True   ,	):
						A__							:   Optional[int]					       =		size if size is not None else {"""height""": 18, """width""": 18}
						A__							:   Optional[Any]					       =		parent
						A__							:   List[str]					       =		batch_size
						A__							:   Any					       =		num_channels
						A__							:   Tuple					       =		image_size
						A__							:   Optional[Any]					       =		min_resolution
						A__							:   List[str]					       =		max_resolution
						A__							:   Tuple					       =		do_resize
						A__							:   Any					       =		size
						A__							:   str					       =		do_normalize






					def   __A      (			self      ):
						return {
						    # here we create 2 clusters for the sake of simplicity
						    "clusters": np.asarray(
						        [
						            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
						            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
						        ]      ),
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						}



@require_torch
@require_vision
class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[str]  							=	ImageGPTImageProcessor if is_vision_available() else None






					def   __A      (			self      ):
						A__							:   List[Any]					       =		ImageGPTImageProcessingTester(self      )






					@property
					def   __A      (			self      ):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(A__   ,	"""clusters"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_resize"""      )      )
						self.assertTrue(hasattr(A__   ,	"""size"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_normalize"""      )      )






					def   __A      (			self      ):
						A__							:   str					       =		self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size   ,	{"""height""": 18, """width""": 18}      )

						A__							:   Any					       =		self.image_processing_class.from_dict(self.image_processor_dict   ,	size=42      )
						self.assertEqual(image_processor.size   ,	{"""height""": 42, """width""": 42}      )






					def   __A      (			self      ):
						A__							:   Dict					       =		self.image_processing_class(**self.image_processor_dict      )
						A__							:   Optional[Any]					       =		json.loads(image_processor.to_json_string()      )
						for key, value in self.image_processor_dict.items():
							if key == "clusters":
								self.assertTrue(np.array_equal(A__   ,	obj[key]      )      )
							else:
								self.assertEqual(obj[key]   ,	A__      )






					def   __A      (			self      ):
						A__							:   str					       =		self.image_processing_class(**self.image_processor_dict      )

						with tempfile.TemporaryDirectory() as tmpdirname:
							A__							:   Union[str, Any]					       =		os.path.join(A__   ,	"""image_processor.json"""      )
							image_processor_first.to_json_file(A__      )
							A__							:   List[Any]					       =		self.image_processing_class.from_json_file(A__      ).to_dict()

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






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.image_processing_class(**self.image_processor_dict      )

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

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






					@unittest.skip("""ImageGPT requires clusters at initialization"""      )
					def   __A      (			self      ):
						pass



def 						UpperCamelCase ()					->				Union[str, Any]:
	A__							:   Optional[Any]					       =		load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,				split="""test""" )

	A__							:   List[Any]					       =		Image.open(dataset[4]["""file"""] )
	A__							:   int					       =		Image.open(dataset[5]["""file"""] )

	A__							:   str					       =		[imagea, imagea]

	return images



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


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""      )

						A__							:   Optional[Any]					       =		prepare_images()

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

						self.assertIsInstance(encoding.input_ids   ,	torch.LongTensor      )
						self.assertEqual(encoding.input_ids.shape   ,	(1, 1024)      )

						A__							:   Optional[Any]					       =		[306, 191, 191]
						self.assertEqual(encoding.input_ids[0, :3].tolist()   ,	A__      )

						# test batched
						A__							:   int					       =		image_processing(A__   ,	return_tensors="""pt"""      )

						self.assertIsInstance(encoding.input_ids   ,	torch.LongTensor      )
						self.assertEqual(encoding.input_ids.shape   ,	(2, 1024)      )

						A__							:   List[str]					       =		[303, 13, 13]
						self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,	A__      )






import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


A_	:			Any  	=      threading.Lock()
A_	:			Optional[logging.Handler]  	=      None

A_	:			Any  	=      {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

A_	:			Optional[int]  	=      logging.WARNING

A_	:			Tuple  	=      True

def 						UpperCamelCase ()					->				List[Any]:
	A__							:   List[str]					       =		os.getenv("""TRANSFORMERS_VERBOSITY"""   ,				lowercase_ )
	if env_level_str:
		if env_level_str in log_levels:
			return log_levels[env_level_str]
		else:
			logging.getLogger().warning(
			    f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
			    f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
	return _default_log_level

def 						UpperCamelCase ()					->				str:
	return __name__.split(""".""" )[0]

def 						UpperCamelCase ()					->				logging.Logger:
	return logging.getLogger(_get_library_name() )

def 						UpperCamelCase ()					->				None:
	global _default_handler

	with _lock:
		if _default_handler:
			# This library has already configured the library root logger.
			return
		A__							:   Tuple					       =		logging.StreamHandler()  # Set sys.stderr as stream.
		A__							:   Union[str, Any]					       =		sys.stderr.flush

		# Apply our default configuration to the library root logger.
		A__							:   Optional[int]					       =		_get_library_root_logger()
		library_root_logger.addHandler(_default_handler )
		library_root_logger.setLevel(_get_default_logging_level() )
		A__							:   str					       =		False

def 						UpperCamelCase ()					->				None:
	global _default_handler

	with _lock:
		if not _default_handler:
			return

		A__							:   Tuple					       =		_get_library_root_logger()
		library_root_logger.removeHandler(_default_handler )
		library_root_logger.setLevel(logging.NOTSET )
		A__							:   Dict					       =		None

def 						UpperCamelCase ()					->				Dict:
	return log_levels

def 						UpperCamelCase (lowercase_:			Optional[str] = None )					->				logging.Logger:

	if name is None:
		A__							:   List[Any]					       =		_get_library_name()

	_configure_library_root_logger()
	return logging.getLogger(lowercase_ )

def 						UpperCamelCase ()					->				int:
	_configure_library_root_logger()
	return _get_library_root_logger().getEffectiveLevel()

def 						UpperCamelCase (lowercase_:			int )					->				None:
	_configure_library_root_logger()
	_get_library_root_logger().setLevel(lowercase_ )

def 						UpperCamelCase ()					->				Union[str, Any]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				List[str]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				Any:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				List[str]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()

	assert _default_handler is not None
	_get_library_root_logger().removeHandler(_default_handler )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()

	assert _default_handler is not None
	_get_library_root_logger().addHandler(_default_handler )

def 						UpperCamelCase (lowercase_:			logging.Handler )					->				None:
	_configure_library_root_logger()

	assert handler is not None
	_get_library_root_logger().addHandler(lowercase_ )

def 						UpperCamelCase (lowercase_:			logging.Handler )					->				None:
	_configure_library_root_logger()

	assert handler is not None and handler not in _get_library_root_logger().handlers
	_get_library_root_logger().removeHandler(lowercase_ )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()
	A__							:   Dict					       =		False

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()
	A__							:   List[str]					       =		True

def 						UpperCamelCase ()					->				None:
	A__							:   List[str]					       =		_get_library_root_logger().handlers

	for handler in handlers:
		A__							:   Union[str, Any]					       =		logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s""" )
		handler.setFormatter(lowercase_ )

def 						UpperCamelCase ()					->				None:
	A__							:   Dict					       =		_get_library_root_logger().handlers

	for handler in handlers:
		handler.setFormatter(lowercase_ )

def 						UpperCamelCase (self:			Tuple   ,				*lowercase_:			int   ,				**lowercase_:			List[Any] )					->				Optional[Any]:
	A__							:   int					       =		os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS"""   ,				lowercase_ )
	if no_advisory_warnings:
		return
	self.warning(*lowercase_   ,				**lowercase_ )


A_	:			int  	=      warning_advice

@functools.lru_cache(lowercase_ )
def 						UpperCamelCase (self:			Any   ,				*lowercase_:			List[str]   ,				**lowercase_:			Dict )					->				Optional[int]:
	self.warning(*lowercase_   ,				**lowercase_ )


A_	:			Tuple  	=      warning_once



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	*A__   ,	**A__      ):  # pylint: disable=unused-argument
						A__							:   int					       =		args[0] if args else None






					def __iter__(			self      ):
						return iter(self._iterator      )






					def __getattr__(			self   ,	A__      ):

						def empty_fn(*A__   ,	**A__      ):  # pylint: disable=unused-argument
							return

						return empty_fn






					def __enter__(			self      ):
						return self






					def __exit__(			self   ,	A__   ,	A__   ,	A__      ):
						return



class    _a						:


					'''simple docstring'''






					def __call__(			self   ,	*A__   ,	**A__      ):
						if _tqdm_active:
							return tqdm_lib.tqdm(*A__   ,	**A__      )
						else:
							return EmptyTqdm(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						A__							:   List[Any]					       =		None
						if _tqdm_active:
							return tqdm_lib.tqdm.set_lock(*A__   ,	**A__      )






					def   __A      (			self      ):
						if _tqdm_active:
							return tqdm_lib.tqdm.get_lock()



A_	:			List[Any]  	=      _tqdm_cls()

def 						UpperCamelCase ()					->				bool:
	global _tqdm_active
	return bool(_tqdm_active )

def 						UpperCamelCase ()					->				List[str]:
	global _tqdm_active
	A__							:   int					       =		True
	hf_hub_utils.enable_progress_bars()


def 						UpperCamelCase ()					->				Optional[Any]:
	global _tqdm_active
	A__							:   Tuple					       =		False
	hf_hub_utils.disable_progress_bars()



