
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE							:Dict										=    datasets.utils.logging.get_logger(__name__)





@dataclass
class 							UpperCAmelCase				(  datasets.BuilderConfig  ):




	'''simple docstring'''





	snake_case_    =					10000
	snake_case_    =					None
	snake_case_    =					None





class 							UpperCAmelCase				(  datasets.ArrowBasedBuilder  ):




	'''simple docstring'''





	snake_case_    =					ParquetConfig






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return datasets.DatasetInfo(features=self.config.features			)






	def     UpperCamelCase_  (	self :			Tuple							,A :			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}'''			)
					__A             =			dl_manager.download_and_extract(self.config.data_files			)
					if isinstance(A							,(str, list, tuple)			):
									__A             =			data_files
									if isinstance(A							,A			):
													__A             =			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A             =			[dl_manager.iter_files(A			) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={"files": files}			)]
					__A             =			[]
					for split_name, files in data_files.items():
									if isinstance(A							,A			):
													__A             =			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A             =			[dl_manager.iter_files(A			) for file in files]
									# Infer features is they are stoed in the arrow schema
									if self.info.features is None:
													for file in itertools.chain.from_iterable(A			):
																	with open(A							,"rb"			) as f:
																					__A             =			datasets.Features.from_arrow_schema(pq.read_schema(A			)			)
																	break
									splits.append(datasets.SplitGenerator(name=A							,gen_kwargs={"files": files}			)			)
					return splits






	def     UpperCamelCase_  (	self :			Dict							,A :			pa.Table			):
					if self.info.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
									__A             =			table_cast(A							,self.info.features.arrow_schema			)
					return pa_table







	def     UpperCamelCase_  (	self :			Any							,A :			List[str]			):
					__A             =			self.info.features.arrow_schema if self.info.features is not None else None
					if self.info.features is not None and self.config.columns is not None:
									if sorted(field.name for field in schema			) != sorted(self.config.columns			):
													raise ValueError(
													    f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''			)
					for file_idx, file in enumerate(itertools.chain.from_iterable(A			)			):
									with open(A							,"rb"			) as f:
													__A             =			pq.ParquetFile(A			)
													try:
																	for batch_idx, record_batch in enumerate(
																	    parquet_file.iter_batches(batch_size=self.config.batch_size							,columns=self.config.columns			)			):
																					__A             =			pa.Table.from_batches([record_batch]			)
																					# Uncomment for debugging (will print the Arrow table size and elements)
																					# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																					# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																					yield f'''{file_idx}_{batch_idx}''', self._cast_table(A			)
													except ValueError as e:
																	logger.error(f'''Failed to read file \'{file}\' with error {type(A			)}: {e}'''			)
																	raise


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 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			List[str]							,A :			List[Any]=7							,A :			Any=3							,A :			int=30							,A :			List[Any]=4_00							,A :			str=True							,A :			int=None							,A :			List[str]=0.9							,A :			Dict=None							,A :			int=True							,A :			Any=[0.5, 0.5, 0.5]							,A :			Optional[int]=[0.5, 0.5, 0.5]							,):
					__A             =			size if size is not None else {"shortest_edge": 30}
					__A             =			crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A             =			parent
					__A             =			batch_size
					__A             =			num_channels
					__A             =			min_resolution
					__A             =			max_resolution
					__A             =			do_resize_and_center_crop
					__A             =			size
					__A             =			crop_pct
					__A             =			crop_size
					__A             =			do_normalize
					__A             =			image_mean
					__A             =			image_std







	def     UpperCamelCase_  (	self :			int			):
					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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					PoolFormerImageProcessor if is_vision_available() else None






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			PoolFormerImageProcessingTester(self			)






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self.image_processor_tester.prepare_image_processor_dict()






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(A							,"do_resize_and_center_crop"			)			)
					self.assertTrue(hasattr(A							,"size"			)			)
					self.assertTrue(hasattr(A							,"crop_pct"			)			)
					self.assertTrue(hasattr(A							,"do_normalize"			)			)
					self.assertTrue(hasattr(A							,"image_mean"			)			)
					self.assertTrue(hasattr(A							,"image_std"			)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			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}			)

					__A             =			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     UpperCamelCase_  (	self :			List[str]			):
					pass






	def     UpperCamelCase_  (	self :			Optional[int]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,numpify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,np.ndarray			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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     UpperCamelCase_  (	self :			List[Any]			):
					# Initialize image_processing
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__A             =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,torchify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,torch.Tensor			)

					# Test not batched input
					__A             =			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
					__A             =			image_processing(A							,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"],
					    )							,)
