



"""simple docstring"""




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       UpperCAmelCase_    (							unittest.TestCase		):



      def __init__(	self	:      int	,      A	:      List[Any]	,      A	:      Optional[Any]=7	,      A	:      Tuple=3	,      A	:      Dict=1_8	,      A	:      Optional[int]=3_0	,      A	:      List[str]=4_0_0	,      A	:      Any=True	,      A	:      Tuple=None	,      A	:      Optional[Any]=True	,      ):
           _UpperCAmelCase     :      List[str]														=       size if size is not None else {"height": 1_8, "width": 1_8}
           _UpperCAmelCase     :      Tuple														=       parent
           _UpperCAmelCase     :      Optional[int]														=       batch_size
           _UpperCAmelCase     :      Dict														=       num_channels
           _UpperCAmelCase     :      List[str]														=       image_size
           _UpperCAmelCase     :      List[Any]														=       min_resolution
           _UpperCAmelCase     :      Union[str, Any]														=       max_resolution
           _UpperCAmelCase     :      Dict														=       do_resize
           _UpperCAmelCase     :      Optional[Any]														=       size
           _UpperCAmelCase     :      Tuple														=       do_normalize



      def 		snake_case_   (	self	:      Any		):
           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       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					ImageGPTImageProcessor if is_vision_available() else None



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      str														=       ImageGPTImageProcessingTester(self		)



      @property
      def 		snake_case_   (	self	:      List[str]		):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[Any]														=       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 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       self.image_processing_class.from_dict(self.image_processor_dict		)
           self.assertEqual(image_processor.size	,      {"height": 1_8, "width": 1_8}		)

           _UpperCAmelCase     :      Union[str, 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 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      int														=       self.image_processing_class(**self.image_processor_dict		)
           _UpperCAmelCase     :      Dict														=       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 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Dict														=       self.image_processing_class(**self.image_processor_dict		)

           with tempfile.TemporaryDirectory() as tmpdirname:
                _UpperCAmelCase     :      Any														=       os.path.join(A	,      "image_processor.json"		)
                image_processor_first.to_json_file(A		)
                _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_class.from_json_file(A		).to_dict()

           _UpperCAmelCase     :      Optional[int]														=       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 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Tuple														=       self.image_processing_class(**self.image_processor_dict		)

           with tempfile.TemporaryDirectory() as tmpdirname:
                image_processor_first.save_pretrained(A		)
                _UpperCAmelCase     :      Any														=       self.image_processing_class.from_pretrained(A		).to_dict()

           _UpperCAmelCase     :      Dict														=       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 		snake_case_   (	self	:      Optional[Any]		):
           pass







def 		__snake_case		( )    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"							)

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

     _UpperCAmelCase     :      List[Any]														=       [imagea, imagea]

     return images






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



      @slow
      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"		)

           _UpperCAmelCase     :      Optional[Any]														=       prepare_images()

           # test non-batched
           _UpperCAmelCase     :      Any														=       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)		)

           _UpperCAmelCase     :      Optional[Any]														=       [3_0_6, 1_9_1, 1_9_1]
           self.assertEqual(encoding.input_ids[0, :3].tolist()	,      A		)

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

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

           _UpperCAmelCase     :      List[Any]														=       [3_0_3, 1_3, 1_3]
           self.assertEqual(encoding.input_ids[1, -3:].tolist()	,      A		)





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
