
"""simple docstring"""






import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
					import torch

					from transformers import AutoModelForImageClassification

if is_vision_available():
					from transformers import AutoImageProcessor

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



							'''simple docstring'''







							@slow
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"       )
										__lowercase       =       AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"       )
										model.to(_lowerCamelCase       )

										from datasets import load_dataset

										__lowercase       =       load_dataset("nielsr/rvlcdip-demo"       )

										__lowercase       =       dataset["train"][0]["image"].convert("RGB"       )

										__lowercase       =       image_processor(_lowerCamelCase  ,    return_tensors="pt"       ).to(_lowerCamelCase       )

										# forward pass
										with torch.no_grad():
													__lowercase       =       model(**_lowerCamelCase       )
													__lowercase       =       outputs.logits

										__lowercase       =       torch.Size((1, 16)       )
										self.assertEqual(logits.shape  ,    _lowerCamelCase       )

										__lowercase       =       torch.tensor(
										    [-0.4158, -0.4092, -0.4347]  ,    device=_lowerCamelCase  ,    dtype=torch.float  ,    )
										self.assertTrue(torch.allclose(logits[0, :3]  ,    _lowerCamelCase  ,    atol=1e-4       )       )







"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_lowercase							=				get_logger(__name__)

class 					__a     ( enum.Enum       ):



							'''simple docstring'''







							_lowerCamelCase					:			Tuple          =   """all_checks"""
							_lowerCamelCase					:			Optional[int]          =   """basic_checks"""
							_lowerCamelCase					:			str          =   """no_checks"""





class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__=None      )						->Union[str, Any]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedDownloadedFile(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowercase       =       " for " + verification_name if verification_name is not None else ""
			if len(__magic_name__      ) > 0:
						raise NonMatchingChecksumError(
						    F'''Checksums didn\'t match{for_verification_name}:\n'''
						    F'''{bad_urls}\n'''
						    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"      )
			logger.info("All the checksums matched successfully" + for_verification_name      )

class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Any:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(__magic_name__      ) > 0:
						raise NonMatchingSplitsSizesError(str(__magic_name__      )      )
			logger.info("All the splits matched successfully."      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = True      )						->dict:
			if record_checksum:
						__lowercase       =       shaaaa()
						with open(__magic_name__			,   "rb"      ) as f:
									for chunk in iter(lambda: f.read(1 << 2_0      )			,   b""      ):
												m.update(__magic_name__      )
									__lowercase       =       m.hexdigest()
			else:
						__lowercase       =       None
			return {"num_bytes": os.path.getsize(__magic_name__      ), "checksum": checksum}







def      lowerCAmelCase__			(		__magic_name__      )						->List[str]:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False





