



"""simple docstring"""





import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


lowerCamelCase      =		datasets.logging.get_logger(__name__)


lowerCamelCase      =		"""\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

lowerCamelCase      =		"""\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

lowerCamelCase      =		"""
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=False			,    lowerCAmelCase__=False			,    lowerCAmelCase__=True			,    lowerCAmelCase__=False			,    lowerCAmelCase__="dummy_doc"   ):
						UpperCAmelCase_    				=							{doc: key_lines}
						UpperCAmelCase_    				=							{doc: sys_lines}

						UpperCAmelCase_    				=							{}

						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0

						UpperCAmelCase_				,					UpperCAmelCase_    				=							reader.get_doc_mentions(lowerCAmelCase__			,    key_doc_lines[doc]			,    lowerCAmelCase__   )
						key_singletons_num += singletons_num

						if NP_only or min_span:
												UpperCAmelCase_    				=							reader.set_annotated_parse_trees(lowerCAmelCase__			,    key_doc_lines[doc]			,    lowerCAmelCase__			,    lowerCAmelCase__   )

						UpperCAmelCase_				,					UpperCAmelCase_    				=							reader.get_doc_mentions(lowerCAmelCase__			,    sys_doc_lines[doc]			,    lowerCAmelCase__   )
						sys_singletons_num += singletons_num

						if NP_only or min_span:
												UpperCAmelCase_    				=							reader.set_annotated_parse_trees(lowerCAmelCase__			,    key_doc_lines[doc]			,    lowerCAmelCase__			,    lowerCAmelCase__   )

						if remove_nested:
												UpperCAmelCase_				,					UpperCAmelCase_    				=							reader.remove_nested_coref_mentions(lowerCAmelCase__			,    lowerCAmelCase__   )
												key_nested_coref_num += nested_mentions
												key_removed_nested_clusters += removed_clusters

												UpperCAmelCase_				,					UpperCAmelCase_    				=							reader.remove_nested_coref_mentions(lowerCAmelCase__			,    lowerCAmelCase__   )
												sys_nested_coref_num += nested_mentions
												sys_removed_nested_clusters += removed_clusters

						UpperCAmelCase_    				=							reader.get_mention_assignments(lowerCAmelCase__			,    lowerCAmelCase__   )
						UpperCAmelCase_    				=							reader.get_mention_assignments(lowerCAmelCase__			,    lowerCAmelCase__   )

						UpperCAmelCase_    				=							(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

						if remove_nested:
												logger.info(
												    "Number of removed nested coreferring mentions in the key "
												    f"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""   )
												logger.info(
												    "Number of resulting singleton clusters in the key "
												    f"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""   )

						if not keep_singletons:
												logger.info(
												    f"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
												    "files, respectively"   )

						return doc_coref_infos
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
						UpperCAmelCase_    				=							get_coref_infos(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

						UpperCAmelCase_    				=							{}
						UpperCAmelCase_    				=							0
						UpperCAmelCase_    				=							0

						for name, metric in metrics:
												UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							evaluator.evaluate_documents(lowerCAmelCase__			,    lowerCAmelCase__			,    beta=1   )
												if name in ["muc", "bcub", "ceafe"]:
																		conll += fa
																		conll_subparts_num += 1
												output_scores.update({f"""{name}/recall""": recall, f"""{name}/precision""": precision, f"""{name}/f1""": fa}   )

												logger.info(
												    name.ljust(10   )			,    f"""Recall: {recall * 100:.2f}"""			,    f""" Precision: {precision * 100:.2f}"""			,    f""" F1: {fa * 100:.2f}"""			,    )

						if conll_subparts_num == 3:
												UpperCAmelCase_    				=							(conll / 3) * 100
												logger.info(f"""CoNLL score: {conll:.2f}"""   )
												output_scores.update({"conll_score": conll}   )

						return output_scores
def      a__			(   lowerCAmelCase__   ):
						UpperCAmelCase_    				=							False
						for line in key_lines:
												if not line.startswith("#"   ):
																		if len(line.split()   ) > 6:
																								UpperCAmelCase_    				=							line.split()[5]
																								if not parse_col == "-":
																														UpperCAmelCase_    				=							True
																														break
																								else:
																														break
						return has_gold_parse


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,      _KWARGS_DESCRIPTION   )
class    lowercase__     (					datasets.Metric   ):







							'''simple docstring'''


							def   lowercase__       (							self    :						List[str]					)				->		Tuple:


													'''simple docstring'''




													return datasets.MetricInfo(
													    description=_DESCRIPTION	,       citation=_CITATION	,       inputs_description=_KWARGS_DESCRIPTION	,       features=datasets.Features(
													        {
													            "predictions": datasets.Sequence(datasets.Value("string"					)					),
													            "references": datasets.Sequence(datasets.Value("string"					)					),
													        }					)	,       codebase_urls=["https://github.com/ns-moosavi/coval"]	,       reference_urls=[
													        "https://github.com/ns-moosavi/coval",
													        "https://www.aclweb.org/anthology/P16-1060",
													        "http://www.conll.cemantix.org/2012/data.html",
													    ]	,       )







							def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						str=True	,       _UpperCAmelCase    :						Optional[Any]=False	,       _UpperCAmelCase    :						List[str]=False	,       _UpperCAmelCase    :						Dict=False					)				->		Optional[Any]:


													'''simple docstring'''




													UpperCAmelCase_    				=							[
													    ("mentions", evaluator.mentions),
													    ("muc", evaluator.muc),
													    ("bcub", evaluator.b_cubed),
													    ("ceafe", evaluator.ceafe),
													    ("lea", evaluator.lea),
													]

													if min_span:
																			UpperCAmelCase_    				=							util.check_gold_parse_annotation(_UpperCAmelCase					)
																			if not has_gold_parse:
																									raise NotImplementedError("References should have gold parse annotation to use 'min_span'."					)
																									# util.parse_key_file(key_file)
																									# key_file = key_file + ".parsed"

													UpperCAmelCase_    				=							evaluate(
													    key_lines=_UpperCAmelCase	,       sys_lines=_UpperCAmelCase	,       metrics=_UpperCAmelCase	,       NP_only=_UpperCAmelCase	,       remove_nested=_UpperCAmelCase	,       keep_singletons=_UpperCAmelCase	,       min_span=_UpperCAmelCase	,       )

													return score









"""simple docstring"""





import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit_text_model'''



       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						str=49408	,       _UpperCAmelCase    :						str=512	,       _UpperCAmelCase    :						Optional[Any]=2048	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Tuple=8	,       _UpperCAmelCase    :						List[str]=16	,       _UpperCAmelCase    :						List[str]="quick_gelu"	,       _UpperCAmelCase    :						Dict=1e-5	,       _UpperCAmelCase    :						Union[str, Any]=0.0	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						Optional[int]=1.0	,       _UpperCAmelCase    :						Dict=0	,       _UpperCAmelCase    :						Dict=49406	,       _UpperCAmelCase    :						Union[str, Any]=49407	,       **_UpperCAmelCase    :						List[str]	,       )				->		List[str]:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase					)

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							attention_dropout
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							initializer_factor







       @classmethod
       def   lowercase__       (							cls    :						int	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						List[str]					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             # get the text config dict if we are loading from OwlViTConfig
             if config_dict.get("model_type"					) == "owlvit":
                   UpperCAmelCase_    				=							config_dict["text_config"]

             if "model_type" in config_dict and hasattr(cls	,       "model_type"					) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                       F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

             return cls.from_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit_vision_model'''



       def __init__(							self    :						str	,       _UpperCAmelCase    :						List[str]=768	,       _UpperCAmelCase    :						Optional[Any]=3072	,       _UpperCAmelCase    :						Tuple=12	,       _UpperCAmelCase    :						Optional[Any]=12	,       _UpperCAmelCase    :						List[Any]=3	,       _UpperCAmelCase    :						List[str]=768	,       _UpperCAmelCase    :						int=32	,       _UpperCAmelCase    :						Dict="quick_gelu"	,       _UpperCAmelCase    :						Optional[Any]=1e-5	,       _UpperCAmelCase    :						Optional[int]=0.0	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[str]=1.0	,       **_UpperCAmelCase    :						List[str]	,       )				->		Dict:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							patch_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							attention_dropout
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							initializer_factor







       @classmethod
       def   lowercase__       (							cls    :						Any	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						Union[str, Any]					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             # get the vision config dict if we are loading from OwlViTConfig
             if config_dict.get("model_type"					) == "owlvit":
                   UpperCAmelCase_    				=							config_dict["vision_config"]

             if "model_type" in config_dict and hasattr(cls	,       "model_type"					) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                       F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

             return cls.from_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''owlvit'''
       UpperCamelCase		=  True



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Any=None	,       _UpperCAmelCase    :						Dict=None	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Any=2.6592	,       _UpperCAmelCase    :						Union[str, Any]=True	,       **_UpperCAmelCase    :						Union[str, Any]	,       )				->		Tuple:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

             if text_config is None:
                   UpperCAmelCase_    				=							{}
                   logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."					)

             if vision_config is None:
                   UpperCAmelCase_    				=							{}
                   logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."					)

             UpperCAmelCase_    				=							OwlViTTextConfig(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							OwlViTVisionConfig(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							projection_dim
             UpperCAmelCase_    				=							logit_scale_init_value
             UpperCAmelCase_    				=							return_dict
             UpperCAmelCase_    				=							1.0



       @classmethod
       def   lowercase__       (							cls    :						Dict	,       _UpperCAmelCase    :						Union[str, os.PathLike]	,       **_UpperCAmelCase    :						Tuple					)				->		"PretrainedConfig":


             '''simple docstring'''




             cls._set_token_in_kwargs(_UpperCAmelCase					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							cls.get_config_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)

             if "model_type" in config_dict and hasattr(cls	,       "model_type"					) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                       F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

             return cls.from_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)



       @classmethod
       def   lowercase__       (							cls    :						List[str]	,       _UpperCAmelCase    :						Dict	,       _UpperCAmelCase    :						Dict	,       **_UpperCAmelCase    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{}
             UpperCAmelCase_    				=							text_config
             UpperCAmelCase_    				=							vision_config

             return cls.from_dict(_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						str					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							copy.deepcopy(self.__dict__					)
             UpperCAmelCase_    				=							self.text_config.to_dict()
             UpperCAmelCase_    				=							self.vision_config.to_dict()
             UpperCAmelCase_    				=							self.__class__.model_type
             return output



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "sequence"}),
                     ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                     ("attention_mask", {0: "batch", 1: "sequence"}),
                 ]					)



       @property
       def   lowercase__       (							self    :						Tuple					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict(
                 [
                     ("logits_per_image", {0: "batch"}),
                     ("logits_per_text", {0: "batch"}),
                     ("text_embeds", {0: "batch"}),
                     ("image_embeds", {0: "batch"}),
                 ]					)



       @property
       def   lowercase__       (							self    :						Any					)				->		float:


             '''simple docstring'''




             return 1e-4



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						"ProcessorMixin"	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						int = -1	,       _UpperCAmelCase    :						Optional["TensorType"] = None	,       )				->		Mapping[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							super().generate_dummy_inputs(
                 processor.tokenizer	,       batch_size=_UpperCAmelCase	,       seq_length=_UpperCAmelCase	,       framework=_UpperCAmelCase					)
             UpperCAmelCase_    				=							super().generate_dummy_inputs(
                 processor.image_processor	,       batch_size=_UpperCAmelCase	,       framework=_UpperCAmelCase					)
             return {**text_input_dict, **image_input_dict}







       @property
       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             return 14




