
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


snake_case :		Tuple							      =  get_tests_dir("fixtures")





class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# A mock response for an HTTP head request to emulate server down
				__magic_name__		:		Union[str, Any]	   =     mock.Mock()
				__magic_name__		:		str	   =     500
				__magic_name__		:		str	   =     {}
				__magic_name__		:		Dict	   =     HTTPError
				__magic_name__		:		Dict	   =     {}

				# Download this model to make sure it's in the cache.
				__magic_name__		:		List[Any]	   =     ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit"		)
				# Under the mock environment we get a 500 error when trying to reach the model.
				with mock.patch("requests.Session.request"						,   return_value=_a		) as mock_head:
					__magic_name__		:		int	   =     ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit"		)
					# This check we did call the fake head request
					mock_head.assert_called()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# This test is for deprecated behavior and can be removed in v5
				__magic_name__		:		str	   =     ViTImageProcessor.from_pretrained(
				    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with self.assertRaises(_a		):
					# config is in subfolder, the following should not work without specifying the subfolder
					__magic_name__		:		Optional[Any]	   =     AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants"		)

				__magic_name__		:		Optional[int]	   =     AutoImageProcessor.from_pretrained(
				    "hf-internal-testing/stable-diffusion-all-variants"						,   subfolder="feature_extractor"		)

				self.assertIsNotNone(_a		)







@is_staging_test
class       _snake_case					(     unittest.TestCase				):







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				__magic_name__		:		str	   =     TOKEN
				HfFolder.save_token(_a		)







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				try:
					delete_repo(token=cls._token						,   repo_id="test-image-processor"		)
				except HTTPError:
					pass

				try:
					delete_repo(token=cls._token						,   repo_id="valid_org/test-image-processor-org"		)
				except HTTPError:
					pass

				try:
					delete_repo(token=cls._token						,   repo_id="test-dynamic-image-processor"		)
				except HTTPError:
					pass







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     ViTImageProcessor.from_pretrained(_a		)
				image_processor.push_to_hub("test-image-processor"						,   use_auth_token=self._token		)

				__magic_name__		:		int	   =     ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''		)
				for k, v in image_processor.__dict__.items():
					self.assertEqual(_a						,   getattr(_a						,   _a		)		)

				# Reset repo
				delete_repo(token=self._token						,   repo_id="test-image-processor"		)

				# Push to hub via save_pretrained
				with tempfile.TemporaryDirectory() as tmp_dir:
					image_processor.save_pretrained(
					    _a						,   repo_id="test-image-processor"						,   push_to_hub=_a						,   use_auth_token=self._token		)

				__magic_name__		:		Optional[Any]	   =     ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''		)
				for k, v in image_processor.__dict__.items():
					self.assertEqual(_a						,   getattr(_a						,   _a		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ViTImageProcessor.from_pretrained(_a		)
				image_processor.push_to_hub("valid_org/test-image-processor"						,   use_auth_token=self._token		)

				__magic_name__		:		List[str]	   =     ViTImageProcessor.from_pretrained("valid_org/test-image-processor"		)
				for k, v in image_processor.__dict__.items():
					self.assertEqual(_a						,   getattr(_a						,   _a		)		)

				# Reset repo
				delete_repo(token=self._token						,   repo_id="valid_org/test-image-processor"		)

				# Push to hub via save_pretrained
				with tempfile.TemporaryDirectory() as tmp_dir:
					image_processor.save_pretrained(
					    _a						,   repo_id="valid_org/test-image-processor-org"						,   push_to_hub=_a						,   use_auth_token=self._token		)

				__magic_name__		:		Union[str, Any]	   =     ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org"		)
				for k, v in image_processor.__dict__.items():
					self.assertEqual(_a						,   getattr(_a						,   _a		)		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				CustomImageProcessor.register_for_auto_class()
				__magic_name__		:		List[str]	   =     CustomImageProcessor.from_pretrained(_a		)

				image_processor.push_to_hub("test-dynamic-image-processor"						,   use_auth_token=self._token		)

				# This has added the proper auto_map field to the config
				self.assertDictEqual(
				    image_processor.auto_map						,   {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}						,   )

				__magic_name__		:		List[str]	   =     AutoImageProcessor.from_pretrained(
				    f'''{USER}/test-dynamic-image-processor'''						,   trust_remote_code=_a		)
				# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
				self.assertEqual(new_image_processor.__class__.__name__						,   "CustomImageProcessor"		)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
