





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):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						List[Any]:



        '''simple docstring'''




        a__	:					int										=size if size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Dict										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[Any]										=num_channels
        a__	:					str										=image_size
        a__	:					Any										=min_resolution
        a__	:					Dict										=max_resolution
        a__	:					Optional[int]										=do_resize
        a__	:					List[str]										=size
        a__	:					Union[str, Any]										=do_normalize







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                    [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                ]							),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Dict						=			ImageGPTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Tuple										=ImageGPTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[int]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Optional[Any]										=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    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        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(lowerCAmelCase__		,      obj[key]							)							)
          else:
            self.assertEqual(obj[key]		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.image_processing_class(**self.image_processor_dict							)

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

        a__	:					Tuple										=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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Optional[int]										=self.image_processing_class(**self.image_processor_dict							)

        with tempfile.TemporaryDirectory() as tmpdirname:
          image_processor_first.save_pretrained(lowerCAmelCase__							)
          a__	:					List[Any]										=self.image_processing_class.from_pretrained(lowerCAmelCase__							).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(lowerCAmelCase__		,      image_processor_second[key]							)							)
          else:
            self.assertEqual(image_processor_first[key]		,      lowerCAmelCase__							)







      @unittest.skip("ImageGPT requires clusters at initialization"							)
      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        pass
def      _A       (		):





  """simple docstring"""



  a__	:					Optional[int]										=load_dataset("hf-internal-testing/fixtures_image_utils"       ,       split="test"						)

  a__	:					Union[str, Any]										=Image.open(dataset[4]["file"]						)
  a__	:					Any										=Image.open(dataset[5]["file"]						)

  a__	:					str										=[imagea, imagea]

  return images







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







      @slow
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Tuple										=ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"							)

        a__	:					List[Any]										=prepare_images()

        # test non-batched
        a__	:					List[str]										=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)							)

        a__	:					Any										=[3_0_6, 1_9_1, 1_9_1]
        self.assertEqual(encoding.input_ids[0, :3].tolist()		,      lowerCAmelCase__							)

        # test batched
        a__	:					Optional[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							)

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

        a__	:					Tuple										=[3_0_3, 1_3, 1_3]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()		,      lowerCAmelCase__							)










# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
