
"""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


_A			:       Dict   		=       logging.get_logger(__name__)

_A			:       List[Any]   		=       {
    """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 		a__						(      a_						):
   __lowerCAmelCase            =				"""owlvit_text_model"""
   def __init__(			self	,    _a=49_408	,    _a=512	,    _a=2_048	,    _a=12	,    _a=8	,    _a=16	,    _a="quick_gelu"	,    _a=1E-5	,    _a=0.0	,    _a=0.0_2	,    _a=1.0	,    _a=0	,    _a=49_406	,    _a=49_407	,    **_a	,    ):
          super().__init__(pad_token_id=_a	,    bos_token_id=_a	,    eos_token_id=_a	,    **_a				)

          lowercase    :				Any       		=							vocab_size
          lowercase    :				Tuple       		=							hidden_size
          lowercase    :				Optional[Any]       		=							intermediate_size
          lowercase    :				Optional[int]       		=							num_hidden_layers
          lowercase    :				Any       		=							num_attention_heads
          lowercase    :				List[str]       		=							max_position_embeddings
          lowercase    :				str       		=							hidden_act
          lowercase    :				Optional[int]       		=							layer_norm_eps
          lowercase    :				Optional[int]       		=							attention_dropout
          lowercase    :				Optional[Any]       		=							initializer_range
          lowercase    :				Optional[Any]       		=							initializer_factor

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				Dict       		=							cls.get_config_dict(_a	,    **_a				)

          # get the text config dict if we are loading from OwlViTConfig
          if config_dict.get("model_type"				) == "owlvit":
                 lowercase    :				Any       		=							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(_a	,    **_a				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				"""owlvit_vision_model"""
   def __init__(			self	,    _a=768	,    _a=3_072	,    _a=12	,    _a=12	,    _a=3	,    _a=768	,    _a=32	,    _a="quick_gelu"	,    _a=1E-5	,    _a=0.0	,    _a=0.0_2	,    _a=1.0	,    **_a	,    ):
          super().__init__(**_a				)

          lowercase    :				int       		=							hidden_size
          lowercase    :				Optional[int]       		=							intermediate_size
          lowercase    :				Tuple       		=							num_hidden_layers
          lowercase    :				str       		=							num_attention_heads
          lowercase    :				Optional[Any]       		=							num_channels
          lowercase    :				int       		=							image_size
          lowercase    :				Optional[Any]       		=							patch_size
          lowercase    :				Optional[int]       		=							hidden_act
          lowercase    :				Union[str, Any]       		=							layer_norm_eps
          lowercase    :				str       		=							attention_dropout
          lowercase    :				Tuple       		=							initializer_range
          lowercase    :				Any       		=							initializer_factor

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				Tuple       		=							cls.get_config_dict(_a	,    **_a				)

          # get the vision config dict if we are loading from OwlViTConfig
          if config_dict.get("model_type"				) == "owlvit":
                 lowercase    :				List[str]       		=							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(_a	,    **_a				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				"""owlvit"""
   __lowerCAmelCase            =				True
   def __init__(			self	,    _a=None	,    _a=None	,    _a=512	,    _a=2.6_5_9_2	,    _a=True	,    **_a	,    ):
          super().__init__(**_a				)

          if text_config is None:
                 lowercase    :				List[str]       		=							{}
                 logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."				)

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

          lowercase    :				int       		=							OwlViTTextConfig(**_a				)
          lowercase    :				Optional[Any]       		=							OwlViTVisionConfig(**_a				)

          lowercase    :				Tuple       		=							projection_dim
          lowercase    :				str       		=							logit_scale_init_value
          lowercase    :				int       		=							return_dict
          lowercase    :				Dict       		=							1.0
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				Any       		=							cls.get_config_dict(_a	,    **_a				)

          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(_a	,    **_a				)
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    _a	,    **_a				):
          lowercase    :				Tuple       		=							{}
          lowercase    :				List[Any]       		=							text_config
          lowercase    :				Optional[Any]       		=							vision_config

          return cls.from_dict(_a	,    **_a				)

   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							copy.deepcopy(self.__dict__				)
          lowercase    :				Tuple       		=							self.text_config.to_dict()
          lowercase    :				Union[str, Any]       		=							self.vision_config.to_dict()
          lowercase    :				int       		=							self.__class__.model_type
          return output


class 		a__						(      a_						):
   @property
   def 			__magic_name__			(			self				):
          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 			__magic_name__			(			self				):
          return OrderedDict(
              [
                  ("logits_per_image", {0: "batch"}),
                  ("logits_per_text", {0: "batch"}),
                  ("text_embeds", {0: "batch"}),
                  ("image_embeds", {0: "batch"}),
              ]				)
   @property
   def 			__magic_name__			(			self				):
          return 1E-4
   def 			__magic_name__			(			self	,    _a	,    _a = -1	,    _a = -1	,    _a = None	,    ):
          lowercase    :				List[str]       		=							super().generate_dummy_inputs(
              processor.tokenizer	,    batch_size=_a	,    seq_length=_a	,    framework=_a				)
          lowercase    :				Optional[Any]       		=							super().generate_dummy_inputs(
              processor.image_processor	,    batch_size=_a	,    framework=_a				)
          return {**text_input_dict, **image_input_dict}

   @property
   def 			__magic_name__			(			self				):
          return 14




"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 for line in f:
                        if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                               if words:
                                      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 1:
                                      labels.append(splits[self.label_idx].replace("\n"	,    ""				)				)
                               else:
                                      # Examples could have no label for mode = "test"
                                      labels.append("O"				)
                 if words:
                        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[Any]       		=							["O"] + labels
                 return labels
          else:
                 return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["O"] + labels
                 return labels
          else:
                 return [
                     "O",
                     "B-ADVP",
                     "B-INTJ",
                     "B-LST",
                     "B-PRT",
                     "B-NP",
                     "B-SBAR",
                     "B-VP",
                     "B-ADJP",
                     "B-CONJP",
                     "B-PP",
                     "I-ADVP",
                     "I-INTJ",
                     "I-LST",
                     "I-PRT",
                     "I-NP",
                     "I-SBAR",
                     "I-VP",
                     "I-ADJP",
                     "I-CONJP",
                     "I-PP",
                 ]


class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        return f.read().splitlines()
          else:
                 return [
                     "ADJ",
                     "ADP",
                     "ADV",
                     "AUX",
                     "CCONJ",
                     "DET",
                     "INTJ",
                     "NOUN",
                     "NUM",
                     "PART",
                     "PRON",
                     "PROPN",
                     "PUNCT",
                     "SCONJ",
                     "SYM",
                     "VERB",
                     "X",
                 ]


