
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor


class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def __init__(      self		:       Any				,       _snake_case		:       Tuple				,       _snake_case		:       int=7				,       _snake_case		:       Any=3				,       _snake_case		:       Dict=30				,       _snake_case		:       List[str]=400				,       _snake_case		:       Any=True				,       _snake_case		:       Optional[Any]=None				,       _snake_case		:       Union[str, Any]=0.9				,       _snake_case		:       Dict=None				,       _snake_case		:       Optional[Any]=True				,       _snake_case		:       Optional[Any]=[0.5, 0.5, 0.5]				,       _snake_case		:       List[str]=[0.5, 0.5, 0.5]				,       ):
          __lowercase		:			Optional[Any]	    =       size if size is not None else {'''shortest_edge''': 30}
          __lowercase		:			Tuple	    =       crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
          __lowercase		:			Optional[Any]	    =       parent
          __lowercase		:			List[Any]	    =       batch_size
          __lowercase		:			int	    =       num_channels
          __lowercase		:			Optional[Any]	    =       min_resolution
          __lowercase		:			Any	    =       max_resolution
          __lowercase		:			List[str]	    =       do_resize_and_center_crop
          __lowercase		:			Optional[int]	    =       size
          __lowercase		:			Any	    =       crop_pct
          __lowercase		:			Dict	    =       crop_size
          __lowercase		:			Tuple	    =       do_normalize
          __lowercase		:			int	    =       image_mean
          __lowercase		:			Union[str, Any]	    =       image_std




      def 		snake_case_			(      self		:       str				):
          return {
              "size": self.size,
              "do_resize_and_center_crop": self.do_resize_and_center_crop,
              "crop_pct": self.crop_pct,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
          }






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



      """simple docstring"""







      A__	:			Any        =    PoolFormerImageProcessor if is_vision_available() else None




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       PoolFormerImageProcessingTester(self				)




      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_snake_case				,       '''do_resize_and_center_crop'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''size'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''crop_pct'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''do_normalize'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''image_mean'''				)				)
          self.assertTrue(hasattr(_snake_case				,       '''image_std'''				)				)




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[str]	    =       self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size				,       {'''shortest_edge''': 30}				)
          self.assertEqual(image_processor.crop_size				,       {'''height''': 30, '''width''': 30}				)

          __lowercase		:			int	    =       self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84				)
          self.assertEqual(image_processor.size				,       {'''shortest_edge''': 42}				)
          self.assertEqual(image_processor.crop_size				,       {'''height''': 84, '''width''': 84}				)




      def 		snake_case_			(      self		:       List[str]				):
          pass




      def 		snake_case_			(      self		:       List[str]				):
          # Initialize image_processing
          __lowercase		:			List[Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          __lowercase		:			str	    =       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       Image.Image				)

          # Test not batched input
          __lowercase		:			Any	    =       image_processing(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values

          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			int	    =       image_processing(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )




      def 		snake_case_			(      self		:       int				):
          # Initialize image_processing
          __lowercase		:			Dict	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          __lowercase		:			Union[str, Any]	    =       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				,       numpify=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       np.ndarray				)

          # Test not batched input
          __lowercase		:			Tuple	    =       image_processing(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			Optional[Any]	    =       image_processing(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )




      def 		snake_case_			(      self		:       List[str]				):
          # Initialize image_processing
          __lowercase		:			Optional[Any]	    =       self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          __lowercase		:			List[Any]	    =       prepare_image_inputs(self.image_processor_tester				,       equal_resolution=_snake_case				,       torchify=_snake_case				)
          for image in image_inputs:
              self.assertIsInstance(_snake_case				,       torch.Tensor				)

          # Test not batched input
          __lowercase		:			Optional[int]	    =       image_processing(image_inputs[0]				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )

          # Test batched
          __lowercase		:			str	    =       image_processing(_snake_case				,       return_tensors='''pt'''				).pixel_values
          self.assertEqual(
              encoded_images.shape				,       (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )				,       )




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      A__	:			Optional[datasets.Features]        =    None






class 						__lowerCAmelCase	(	datasets.ArrowBasedBuilder      ):



      """simple docstring"""







      A__	:			List[str]        =    PandasConfig




      def 		snake_case_			(      self		:       int				):
          return datasets.DatasetInfo(features=self.config.features				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[int]				):
          if not self.config.data_files:
              raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'				)
          __lowercase		:			Any	    =       dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(_snake_case				,       (str, list, tuple)				):
              __lowercase		:			str	    =       data_files
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Any	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			Optional[int]	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}				)]
          __lowercase		:			str	    =       []
          for split_name, files in data_files.items():
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			str	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			int	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=_snake_case				,       gen_kwargs={'''files''': files}				)				)
          return splits




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       pa.Table				):
          if self.config.features is not None:
              # more expensive cast to support nested features with keys in a different order
              # allows str <-> int/float or str to Audio for example
              __lowercase		:			List[str]	    =       table_cast(_snake_case				,       self.config.features.arrow_schema				)
          return pa_table




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Union[str, Any]				):
          for i, file in enumerate(itertools.chain.from_iterable(_snake_case				)				):
              with open(_snake_case				,       '''rb'''				) as f:
                  __lowercase		:			Dict	    =       pa.Table.from_pandas(pd.read_pickle(_snake_case				)				)
                  yield i, self._cast_table(_snake_case				)


