import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__UpperCamelCase		:       List[str]									=     logging.get_logger(__name__)

__UpperCamelCase		:       str									=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase		:       Union[str, Any]									=     {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__UpperCamelCase		:       int									=     {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__UpperCamelCase		:       Dict									=     {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase		:       List[Any]									=     {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__UpperCamelCase		:       List[str]									=     {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__UpperCamelCase		:       Any									=     {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__UpperCamelCase		:       int									=     {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__UpperCamelCase		:       List[Any]									=     {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__UpperCamelCase		:       List[str]									=     {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_     =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_     =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






__UpperCamelCase		:       List[Any]									=     collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__UpperCamelCase		:       Any									=     collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__UpperCamelCase		:       Dict									=     R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '





@add_start_docstrings(UpperCamelCase_)
class 					lowercase__						:


						def __call__(       self	:				str    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[str] = None    ,		UpperCamelCase__	:				Optional[str] = None    ,		UpperCamelCase__	:				Union[bool, str] = False    ,		UpperCamelCase__	:				Union[bool, str] = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		**UpperCamelCase__	:				List[Any]    ,		):





												'''simple docstring'''





												if titles is None and texts is None:
																		return super().__call__(
																		    UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												elif titles is None or texts is None:
																		SCREAMING_SNAKE_CASE							:    str							=							titles if texts is None else texts
																		return super().__call__(
																		    UpperCamelCase__    ,		UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							titles if not isinstance(UpperCamelCase__    ,		UpperCamelCase__						) else [titles]
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							texts if not isinstance(UpperCamelCase__    ,		UpperCamelCase__						) else [texts]
												SCREAMING_SNAKE_CASE							:    Tuple							=							len(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							questions if not isinstance(UpperCamelCase__    ,		UpperCamelCase__						) else [questions] * n_passages
												if len(UpperCamelCase__						) != len(UpperCamelCase__						):
																		raise ValueError(
																		    f"""There should be as many titles than texts but got {len(UpperCamelCase__						)} titles and {len(UpperCamelCase__						)} texts."""						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							super().__call__(UpperCamelCase__    ,		UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__						)['''input_ids''']
												SCREAMING_SNAKE_CASE							:    List[str]							=							super().__call__(UpperCamelCase__    ,		add_special_tokens=UpperCamelCase__    ,		padding=UpperCamelCase__    ,		truncation=UpperCamelCase__						)['''input_ids''']
												SCREAMING_SNAKE_CASE							:    List[str]							=							{
												    '''input_ids''': [
												        (encoded_question_and_title + encoded_text)[:max_length]
												        if max_length is not None and truncation
												        else encoded_question_and_title + encoded_text
												        for encoded_question_and_title, encoded_text in zip(UpperCamelCase__    ,		UpperCamelCase__						)
												    ]
												}
												if return_attention_mask is not False:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							[]
																		for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							attention_mask
												return self.pad(UpperCamelCase__    ,		padding=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		return_tensors=UpperCamelCase__						)


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				BatchEncoding    ,		UpperCamelCase__	:				DPRReaderOutput    ,		UpperCamelCase__	:				int = 16    ,		UpperCamelCase__	:				int = 64    ,		UpperCamelCase__	:				int = 4    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							reader_input['''input_ids''']
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							reader_output[:3]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							len(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							sorted(range(UpperCamelCase__						)    ,		reverse=UpperCamelCase__    ,		key=relevance_logits.__getitem__						)
												SCREAMING_SNAKE_CASE							:    List[DPRReaderOutput]							=							[]
												for doc_id in sorted_docs:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							list(input_ids[doc_id]						)
																		# assuming question & title information is at the beginning of the sequence
																		SCREAMING_SNAKE_CASE							:    Any							=							sequence_ids.index(self.sep_token_id    ,		2						) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																								SCREAMING_SNAKE_CASE							:    Dict							=							sequence_ids.index(self.pad_token_id						)
																		else:
																								SCREAMING_SNAKE_CASE							:    str							=							len(UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self._get_best_spans(
																		    start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,		end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,		max_answer_length=UpperCamelCase__    ,		top_spans=UpperCamelCase__    ,		)
																		for start_index, end_index in best_spans:
																								start_index += passage_offset
																								end_index += passage_offset
																								nbest_spans_predictions.append(
																								    DPRSpanPrediction(
																								        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,		relevance_score=relevance_logits[doc_id]    ,		doc_id=UpperCamelCase__    ,		start_index=UpperCamelCase__    ,		end_index=UpperCamelCase__    ,		text=self.decode(sequence_ids[start_index : end_index + 1]						)    ,		)						)
																		if len(UpperCamelCase__						) >= num_spans:
																								break
												return nbest_spans_predictions[:num_spans]



						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							[]
												for start_index, start_score in enumerate(UpperCamelCase__						):
																		for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
																								scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
												SCREAMING_SNAKE_CASE							:    Dict							=							sorted(UpperCamelCase__    ,		key=lambda UpperCamelCase__						: x[1]    ,		reverse=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							[]
												for (start_index, end_index), score in scores:
																		if start_index > end_index:
																								raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""						)
																		SCREAMING_SNAKE_CASE							:    str							=							end_index - start_index + 1
																		if length > max_answer_length:
																								raise ValueError(f"""Span is too long: {length} > {max_answer_length}"""						)
																		if any(
																		    start_index <= prev_start_index <= prev_end_index <= end_index
																		    or prev_start_index <= start_index <= end_index <= prev_end_index
																		    for (prev_start_index, prev_end_index) in chosen_span_intervals						):
																								continue
																		chosen_span_intervals.append((start_index, end_index)						)

																		if len(UpperCamelCase__						) == top_spans:
																								break
												return chosen_span_intervals






@add_end_docstrings(UpperCamelCase_)
class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		READER_PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_     =		READER_PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_     =		["""input_ids""", """attention_mask"""]






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402





class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							0


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''						)
												self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    str							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		SCREAMING_SNAKE_CASE							:    Any							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Dict							=							CLIPConfig()

																		# Create a dummy config file with image_proceesor_type
																		SCREAMING_SNAKE_CASE							:    Dict							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																		json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																		# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						).to_dict()

																		config_dict.pop('''image_processor_type'''						)
																		SCREAMING_SNAKE_CASE							:    str							=							CLIPImageProcessor(**UpperCamelCase__						)

																		# save in new folder
																		model_config.save_pretrained(UpperCamelCase__						)
																		config.save_pretrained(UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)

																		# make sure private variable is not incorrectly saved
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							json.loads(config.to_json_string()						)
																		self.assertTrue('''_processor_class''' not in dict_as_saved						)

												self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																		json.dump(
																		    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)

																		SCREAMING_SNAKE_CASE							:    Dict							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																		self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		'''clip-base is not a local folder and is not a valid model identifier'''						):
																		SCREAMING_SNAKE_CASE							:    List[str]							=							AutoImageProcessor.from_pretrained('''clip-base'''						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							AutoImageProcessor.from_pretrained(UpperCamelCase__    ,		revision='''aaaaaa'''						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												with self.assertRaisesRegex(
												    UpperCamelCase__    ,		'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''    ,		):
																		SCREAMING_SNAKE_CASE							:    Dict							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												with self.assertRaises(UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''						)
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
												self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)

												# Test image processor can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																		image_processor.save_pretrained(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoImageProcessor.from_pretrained(UpperCamelCase__    ,		trust_remote_code=UpperCamelCase__						)
												self.assertEqual(reloaded_image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												try:
																		AutoConfig.register('''custom'''    ,		UpperCamelCase__						)
																		AutoImageProcessor.register(UpperCamelCase__    ,		UpperCamelCase__						)
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(UpperCamelCase__						):
																								AutoImageProcessor.register(UpperCamelCase__    ,		UpperCamelCase__						)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Path(UpperCamelCase__						) / '''preprocessor_config.json'''
																								SCREAMING_SNAKE_CASE							:    Any							=							Path(UpperCamelCase__						) / '''config.json'''
																								json.dump(
																								    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}    ,		open(UpperCamelCase__    ,		'''w'''						)    ,		)
																								json.dump({'''model_type''': '''clip'''}    ,		open(UpperCamelCase__    ,		'''w'''						)						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							CustomImageProcessor.from_pretrained(UpperCamelCase__						)

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		with tempfile.TemporaryDirectory() as tmp_dir:
																								image_processor.save_pretrained(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Any							=							AutoImageProcessor.from_pretrained(UpperCamelCase__						)
																								self.assertIsInstance(UpperCamelCase__    ,		UpperCamelCase__						)

												finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



						def        __A       (       self	:				Any						):





												'''simple docstring'''





												class 					lowercase__						(   UpperCamelCase_):
																		UpperCamelCase_     =		True

												try:
																		AutoConfig.register('''custom'''    ,		UpperCamelCase__						)
																		AutoImageProcessor.register(UpperCamelCase__    ,		UpperCamelCase__						)
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE							:    str							=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(image_processor.is_local						)

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(image_processor.is_local						)

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							AutoImageProcessor.from_pretrained(
																		    '''hf-internal-testing/test_dynamic_image_processor'''    ,		trust_remote_code=UpperCamelCase__						)
																		self.assertEqual(image_processor.__class__.__name__    ,		'''NewImageProcessor'''						)
																		self.assertTrue(not hasattr(UpperCamelCase__    ,		'''is_local'''						)						)

												finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





