


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


__snake_case        =   logging.get_logger(__name__)

__snake_case        =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__snake_case        =   {
    "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"
        ),
    },
}
__snake_case        =   {
    "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"
        ),
    },
}
__snake_case        =   {
    "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"
        ),
    },
}

__snake_case        =   {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__snake_case        =   {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__snake_case        =   {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


__snake_case        =   {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__snake_case        =   {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__snake_case        =   {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase						= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase						= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


__snake_case        =   collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__snake_case        =   collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__snake_case        =   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(__snake_case    )
class    UpperCAmelCase					:




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







						"""simple docstring"""







						if titles is None and texts is None:
									return super().__call__(
									    __magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       return_tensors=__magic_name__       ,       return_attention_mask=__magic_name__       ,       **__magic_name__       ,       )
						elif titles is None or texts is None:
									UpperCamelCase         =					titles if texts is None else texts
									return super().__call__(
									    __magic_name__       ,       __magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       return_tensors=__magic_name__       ,       return_attention_mask=__magic_name__       ,       **__magic_name__       ,       )
						UpperCamelCase         =					titles if not isinstance(__magic_name__       ,       __magic_name__						) else [titles]
						UpperCamelCase         =					texts if not isinstance(__magic_name__       ,       __magic_name__						) else [texts]
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					questions if not isinstance(__magic_name__       ,       __magic_name__						) else [questions] * n_passages
						if len(__magic_name__						) != len(__magic_name__						):
									raise ValueError(
									    F'There should be as many titles than texts but got {len(__magic_name__						)} titles and {len(__magic_name__						)} texts.'						)
						UpperCamelCase         =					super().__call__(__magic_name__       ,       __magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__						)["""input_ids"""]
						UpperCamelCase         =					super().__call__(__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__						)["""input_ids"""]
						UpperCamelCase         =					{
						    """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(__magic_name__       ,       __magic_name__						)
						    ]
						}
						if return_attention_mask is not False:
									UpperCamelCase         =					[]
									for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
									UpperCamelCase         =					attention_mask
						return self.pad(__magic_name__       ,       padding=__magic_name__       ,       max_length=__magic_name__       ,       return_tensors=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		BatchEncoding       ,       __magic_name__							:		DPRReaderOutput       ,       __magic_name__							:		int = 1_6       ,       __magic_name__							:		int = 6_4       ,       __magic_name__							:		int = 4       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					reader_input["""input_ids"""]
						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					reader_output[:3]
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					sorted(range(__magic_name__						)       ,       reverse=__magic_name__       ,       key=relevance_logits.__getitem__						)
						UpperCamelCase         =					[]
						for doc_id in sorted_docs:
									UpperCamelCase         =					list(input_ids[doc_id]						)
									# assuming question & title information is at the beginning of the sequence
									UpperCamelCase         =					sequence_ids.index(self.sep_token_id       ,       2						) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												UpperCamelCase         =					sequence_ids.index(self.pad_token_id						)
									else:
												UpperCamelCase         =					len(__magic_name__						)

									UpperCamelCase         =					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=__magic_name__       ,       top_spans=__magic_name__       ,       )
									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=__magic_name__       ,       start_index=__magic_name__       ,       end_index=__magic_name__       ,       text=self.decode(sequence_ids[start_index : end_index + 1]						)       ,       )						)
									if len(__magic_name__						) >= num_spans:
												break
						return nbest_spans_predictions[:num_spans]





			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		int       ,       __magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					[]
						for start_index, start_score in enumerate(__magic_name__						):
									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)						)
						UpperCamelCase         =					sorted(__magic_name__       ,       key=lambda __magic_name__						: x[1]       ,       reverse=__magic_name__						)
						UpperCamelCase         =					[]
						for (start_index, end_index), score in scores:
									if start_index > end_index:
												raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'						)
									UpperCamelCase         =					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(__magic_name__						) == top_spans:
												break
						return chosen_span_intervals




@add_end_docstrings(__snake_case    )
class    UpperCAmelCase					(       __snake_case      , __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= READER_PRETRAINED_VOCAB_FILES_MAP
			lowercase						= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= READER_PRETRAINED_INIT_CONFIGURATION
			lowercase						= ["""input_ids""", """attention_mask"""]








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import OwlViTImageProcessor, OwlViTProcessor




@require_vision
class    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					tempfile.mkdtemp()

						# fmt: off
						UpperCamelCase         =					["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
						# fmt: on
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
						UpperCamelCase         =					{"""unk_token""": """<unk>"""}

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write(json.dumps(__magic_name__						) + """\n"""						)
						with open(self.merges_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)

						UpperCamelCase         =					{
						    """do_resize""": True,
						    """size""": 2_0,
						    """do_center_crop""": True,
						    """crop_size""": 1_8,
						    """do_normalize""": True,
						    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
						    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
						}
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       __magic_name__						)
						with open(self.image_processor_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									json.dump(__magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		str       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		str						):







						"""simple docstring"""







						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					[np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta						)]

						UpperCamelCase         =					[Image.fromarray(np.moveaxis(__magic_name__       ,       0       ,       -1						)						) for x in image_inputs]

						return image_inputs




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_tokenizer()
						UpperCamelCase         =					self.get_rust_tokenizer()
						UpperCamelCase         =					self.get_image_processor()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_slow.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname       ,       use_fast=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_fast.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor_slow.tokenizer.get_vocab()       ,       tokenizer_slow.get_vocab()						)
						self.assertEqual(processor_fast.tokenizer.get_vocab()       ,       tokenizer_fast.get_vocab()						)
						self.assertEqual(tokenizer_slow.get_vocab()       ,       tokenizer_fast.get_vocab()						)
						self.assertIsInstance(processor_slow.tokenizer       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor_slow.image_processor.to_json_string()       ,       image_processor.to_json_string()						)
						self.assertEqual(processor_fast.image_processor.to_json_string()       ,       image_processor.to_json_string()						)
						self.assertIsInstance(processor_slow.image_processor       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.image_processor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					OwlViTProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						UpperCamelCase         =					self.get_tokenizer(bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""						)
						UpperCamelCase         =					self.get_image_processor(do_normalize=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor.from_pretrained(
						    self.tmpdirname       ,       bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""       ,       do_normalize=__magic_name__						)

						self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor.image_processor.to_json_string()       ,       image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					image_processor(__magic_name__       ,       return_tensors="""np"""						)
						UpperCamelCase         =					processor(images=__magic_name__       ,       return_tensors="""np"""						)

						for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()       ,       input_processor[key].sum()       ,       delta=1e-2						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""

						UpperCamelCase         =					processor(text=__magic_name__       ,       return_tensors="""np"""						)

						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""np"""						)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()       ,       encoded_processor[key][0].tolist()						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(text=__magic_name__       ,       images=__magic_name__						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask""", """pixel_values"""]						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					[["""cat""", """nasa badge"""], ["""person"""]]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					max([len(__magic_name__						) for texts in input_texts]						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (batch_size * num_max_text_queries, seq_length)						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					inputs["""input_ids"""]
						UpperCamelCase         =					[
						    [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						]

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)
						self.assertListEqual(list(input_ids[0]						)       ,       predicted_ids[0]						)
						self.assertListEqual(list(input_ids[1]						)       ,       predicted_ids[1]						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(images=__magic_name__       ,       query_images=__magic_name__						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""query_pixel_values""", """pixel_values"""]						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase         =					processor.batch_decode(__magic_name__						)
						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__						)

						self.assertListEqual(__magic_name__       ,       __magic_name__						)




