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   lowercase      (       unittest.TestCase       ):




       def __init__(  self				:   List[str]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]=7		,							_UpperCamelCase				:   List[str]=3		,							_UpperCamelCase				:   Optional[int]=18		,							_UpperCamelCase				:   Union[str, Any]=30		,							_UpperCamelCase				:   Tuple=400		,							_UpperCamelCase				:   List[str]=True		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   Any=True		,							)   ->			Tuple:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    size if size is not None else {"height": 18, "width": 18}
            SCREAMING_SNAKE_CASE           =    parent
            SCREAMING_SNAKE_CASE           =    batch_size
            SCREAMING_SNAKE_CASE           =    num_channels
            SCREAMING_SNAKE_CASE           =    image_size
            SCREAMING_SNAKE_CASE           =    min_resolution
            SCREAMING_SNAKE_CASE           =    max_resolution
            SCREAMING_SNAKE_CASE           =    do_resize
            SCREAMING_SNAKE_CASE           =    size
            SCREAMING_SNAKE_CASE           =    do_normalize





       def 		__snake_case(  self				:   Any )   ->			Dict:



            '''simple docstring'''

            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   lowercase      (       snake_case__			,    unittest.TestCase       ):
       lowercase__  :    Any									=							ImageGPTImageProcessor if is_vision_available() else None




       def 		__snake_case(  self				:   Tuple )   ->			Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    ImageGPTImageProcessingTester(self )




       @property
       def 		__snake_case(  self				:   str )   ->			Optional[Any]:



            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(UpperCAmelCase_		,							"clusters" ) )
            self.assertTrue(hasattr(UpperCAmelCase_		,							"do_resize" ) )
            self.assertTrue(hasattr(UpperCAmelCase_		,							"size" ) )
            self.assertTrue(hasattr(UpperCAmelCase_		,							"do_normalize" ) )




       def 		__snake_case(  self				:   Optional[Any] )   ->			Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size		,							{"height": 18, "width": 18} )

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




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
            SCREAMING_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(UpperCAmelCase_		,							obj[key] ) )
                 else:
                      self.assertEqual(obj[key]		,							UpperCAmelCase_ )




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )

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

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




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )

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

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





       @unittest.skip("ImageGPT requires clusters at initialization" )
       def 		__snake_case(  self				:   Tuple )   ->			Any:



            '''simple docstring'''

            pass





def 		__lowerCamelCase							():
     SCREAMING_SNAKE_CASE           =    load_dataset("hf-internal-testing/fixtures_image_utils" ,	split="test"					)

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

     SCREAMING_SNAKE_CASE           =    [imagea, imagea]

     return images


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




       @slow
       def 		__snake_case(  self				:   str )   ->			Any:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

            SCREAMING_SNAKE_CASE           =    prepare_images()

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

            self.assertIsInstance(encoding.input_ids		,							torch.LongTensor )
            self.assertEqual(encoding.input_ids.shape		,							(1, 1_024) )

            SCREAMING_SNAKE_CASE           =    [306, 191, 191]
            self.assertEqual(encoding.input_ids[0, :3].tolist()		,							UpperCAmelCase_ )

            # test batched
            SCREAMING_SNAKE_CASE           =    image_processing(UpperCAmelCase_		,							return_tensors="pt" )

            self.assertIsInstance(encoding.input_ids		,							torch.LongTensor )
            self.assertEqual(encoding.input_ids.shape		,							(2, 1_024) )

            SCREAMING_SNAKE_CASE           =    [303, 13, 13]
            self.assertEqual(encoding.input_ids[1, -3:].tolist()		,							UpperCAmelCase_ )





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_UpperCamelCase		,							token_ids_a=_UpperCamelCase		,							already_has_special_tokens=_UpperCamelCase )

												if token_ids_a is None:
																	return [1] + ([0] * len(_UpperCamelCase )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




