





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
      from test_module.custom_tokenization_fast import CustomTokenizerFast







class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Optional[int]   =						0




       @slow
       def 							UpperCAmelCase__ (  self :    str		):
         for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
           __snake_case:						Any   =						AutoTokenizer.from_pretrained(A		)
           self.assertIsNotNone(A		)
           self.assertIsInstance(A							,				(BertTokenizer, BertTokenizerFast)		)
           self.assertGreater(len(A		)							,				0		)

         for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
           __snake_case:						Any   =						AutoTokenizer.from_pretrained(A		)
           self.assertIsNotNone(A		)
           self.assertIsInstance(A							,				(GPTaTokenizer, GPTaTokenizerFast)		)
           self.assertGreater(len(A		)							,				0		)




       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A		)
         self.assertIsInstance(A							,				(BertTokenizer, BertTokenizerFast)		)
         self.assertEqual(tokenizer.vocab_size							,				12		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A		)
         self.assertIsInstance(A							,				(RobertaTokenizer, RobertaTokenizerFast)		)
         self.assertEqual(tokenizer.vocab_size							,				20		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         self.assertIsInstance(A							,				A		)
         # Check that tokenizer_type ≠ model_type
         __snake_case:						List[Any]   =						AutoTokenizer.from_pretrained(A							,				config=A		)
         self.assertIsInstance(A							,				(BertTokenizer, BertTokenizerFast)		)
         self.assertEqual(tokenizer.vocab_size							,				12		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         with tempfile.TemporaryDirectory() as tmp_dir:
           shutil.copy("""./tests/fixtures/vocab.txt"""							,				os.path.join(A							,				"""vocab.txt"""		)		)

           __snake_case:						List[Any]   =						AutoTokenizer.from_pretrained(A							,				tokenizer_type="""bert"""							,				use_fast=A		)
           self.assertIsInstance(A							,				A		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           shutil.copy("""./tests/fixtures/vocab.json"""							,				os.path.join(A							,				"""vocab.json"""		)		)
           shutil.copy("""./tests/fixtures/merges.txt"""							,				os.path.join(A							,				"""merges.txt"""		)		)

           __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A							,				tokenizer_type="""gpt2"""							,				use_fast=A		)
           self.assertIsInstance(A							,				A		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    str		):
         with tempfile.TemporaryDirectory() as tmp_dir:
           shutil.copy("""./tests/fixtures/vocab.txt"""							,				os.path.join(A							,				"""vocab.txt"""		)		)

           __snake_case:						int   =						AutoTokenizer.from_pretrained(A							,				tokenizer_type="""bert"""		)
           self.assertIsInstance(A							,				A		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           shutil.copy("""./tests/fixtures/vocab.json"""							,				os.path.join(A							,				"""vocab.json"""		)		)
           shutil.copy("""./tests/fixtures/merges.txt"""							,				os.path.join(A							,				"""merges.txt"""		)		)

           __snake_case:						Optional[int]   =						AutoTokenizer.from_pretrained(A							,				tokenizer_type="""gpt2"""		)
           self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Any		):
         with pytest.raises(A		):
           AutoTokenizer.from_pretrained("""./"""							,				tokenizer_type="""xxx"""		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    str		):
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
           __snake_case:						Union[str, Any]   =						tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""		)
           self.assertIsInstance(A							,				(BertTokenizer, BertTokenizerFast)		)

           if isinstance(A							,				A		):
             self.assertEqual(tokenizer.basic_tokenizer.do_lower_case							,				A		)
           else:
             self.assertEqual(tokenizer.do_lower_case							,				A		)

           self.assertEqual(tokenizer.model_max_length							,				512		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    str		):
         for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
           with self.assertRaisesRegex(
               A							,				"""julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"""							,				):
             __snake_case:						Any   =						tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         # tests: https://github.com/huggingface/transformers/pull/13251
         # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
         # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
         __snake_case:						int   =						TOKENIZER_MAPPING.values()
         __snake_case:						List[str]   =						[]

         for slow_tok, fast_tok in tokenizers:
           if slow_tok is not None:
             tokenizer_names.append(slow_tok.__name__		)

           if fast_tok is not None:
             tokenizer_names.append(fast_tok.__name__		)

         for tokenizer_name in tokenizer_names:
           # must find the right class
           tokenizer_class_from_name(A		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    List[str]		):
         self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""							,				use_fast=A		)							,				A		)
         self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""		)							,				A		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Optional[int]   =						AutoTokenizer.from_pretrained("""distilbert-base-uncased"""							,				do_lower_case=A		)
         __snake_case:						Tuple   =						"""Hello, world. How are you?"""
         __snake_case:						List[str]   =						tokenizer.tokenize(A		)
         self.assertEqual("""[UNK]"""							,				tokens[0]		)

         __snake_case:						Union[str, Any]   =						AutoTokenizer.from_pretrained("""microsoft/mpnet-base"""							,				do_lower_case=A		)
         __snake_case:						Tuple   =						tokenizer.tokenize(A		)
         self.assertEqual("""[UNK]"""							,				tokens[0]		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Union[str, Any]   =						AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""		)
         self.assertEqual(type(A		)							,				A		)
         self.assertEqual(tokenizer.model_max_length							,				512		)
         self.assertEqual(tokenizer.vocab_size							,				30_000		)
         self.assertEqual(tokenizer.unk_token							,				"""[UNK]"""		)
         self.assertEqual(tokenizer.padding_side							,				"""right"""		)
         self.assertEqual(tokenizer.truncation_side							,				"""right"""		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						AutoTokenizer.from_pretrained(A		)
         self.assertIsInstance(A							,				(BertTokenizer, BertTokenizerFast)		)
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(A		)
           __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A		)

         self.assertIsInstance(A							,				tokenizer.__class__		)
         self.assertEqual(tokenizera.vocab_size							,				12		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained("""ctrl"""		)
         # There is no fast CTRL so this always gives us a slow tokenizer.
         self.assertIsInstance(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         # Check we can load the tokenizer config of an online model.
         __snake_case:						str   =						get_tokenizer_config("""bert-base-cased"""		)
         __snake_case:						Optional[int]   =						config.pop("""_commit_hash"""							,				A		)
         # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
         self.assertEqual(A							,				{"""do_lower_case""": False}		)

         # This model does not have a tokenizer_config so we get back an empty dict.
         __snake_case:						Tuple   =						get_tokenizer_config(A		)
         self.assertDictEqual(A							,				{}		)

         # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
         __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A		)
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(A		)
           __snake_case:						Dict   =						get_tokenizer_config(A		)

         # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
         self.assertEqual(config["""tokenizer_class"""]							,				"""BertTokenizer"""		)




       def 							UpperCAmelCase__ (  self :    Any		):
         try:
           AutoConfig.register("""custom"""							,				A		)

           AutoTokenizer.register(A							,				slow_tokenizer_class=A		)
           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(A		):
             AutoTokenizer.register(A							,				slow_tokenizer_class=A		)

           __snake_case:						List[str]   =						CustomTokenizer.from_pretrained(A		)
           with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A		)

             __snake_case:						str   =						AutoTokenizer.from_pretrained(A		)
             self.assertIsInstance(A							,				A		)

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




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    str		):
         try:
           AutoConfig.register("""custom"""							,				A		)

           # Can register in two steps
           AutoTokenizer.register(A							,				slow_tokenizer_class=A		)
           self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,				(CustomTokenizer, None)		)
           AutoTokenizer.register(A							,				fast_tokenizer_class=A		)
           self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,				(CustomTokenizer, CustomTokenizerFast)		)

           del TOKENIZER_MAPPING._extra_content[CustomConfig]
           # Can register in one step
           AutoTokenizer.register(
               A							,				slow_tokenizer_class=A							,				fast_tokenizer_class=A		)
           self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,				(CustomTokenizer, CustomTokenizerFast)		)

           # Trying to register something existing in the Transformers library will raise an error
           with self.assertRaises(A		):
             AutoTokenizer.register(A							,				fast_tokenizer_class=A		)

           # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
           # and that model does not have a tokenizer.json
           with tempfile.TemporaryDirectory() as tmp_dir:
             __snake_case:						int   =						BertTokenizerFast.from_pretrained(A		)
             bert_tokenizer.save_pretrained(A		)
             __snake_case:						Tuple   =						CustomTokenizerFast.from_pretrained(A		)

           with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A		)

             __snake_case:						Tuple   =						AutoTokenizer.from_pretrained(A		)
             self.assertIsInstance(A							,				A		)

             __snake_case:						Tuple   =						AutoTokenizer.from_pretrained(A							,				use_fast=A		)
             self.assertIsInstance(A							,				A		)

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




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(A		):
           __snake_case:						List[str]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		)
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(A		):
           __snake_case:						List[str]   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A		)

         __snake_case:						Optional[int]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A		)
         self.assertTrue(tokenizer.special_attribute_present		)
         # Test tokenizer can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(A		)
           __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A							,				trust_remote_code=A		)
         self.assertTrue(reloaded_tokenizer.special_attribute_present		)

         if is_tokenizers_available():
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)
           self.assertEqual(reloaded_tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)

           # Test we can also load the slow version
           __snake_case:						Dict   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A							,				use_fast=A		)
           self.assertTrue(tokenizer.special_attribute_present		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           # Test tokenizer can be reloaded.
           with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(A		)
             __snake_case:						Optional[Any]   =						AutoTokenizer.from_pretrained(A							,				trust_remote_code=A							,				use_fast=A		)
           self.assertEqual(reloaded_tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           self.assertTrue(reloaded_tokenizer.special_attribute_present		)
         else:
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           self.assertEqual(reloaded_tokenizer.__class__.__name__							,				"""NewTokenizer"""		)




       @require_tokenizers
       def 							UpperCAmelCase__ (  self :    List[str]		):







         class     __snake_case       (							__lowerCamelCase			):





                '''simple docstring'''







                lowerCAmelCase__      		=     False







         class     __snake_case       (							__lowerCamelCase			):





                '''simple docstring'''







                lowerCAmelCase__      		=     NewTokenizer
                lowerCAmelCase__      		=     False

         try:
           AutoConfig.register("""custom"""							,				A		)
           AutoTokenizer.register(A							,				slow_tokenizer_class=A		)
           AutoTokenizer.register(A							,				fast_tokenizer_class=A		)
           # If remote code is not set, the default is to use local
           __snake_case:						Any   =						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)
           self.assertFalse(tokenizer.special_attribute_present		)
           __snake_case:						List[Any]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							,				use_fast=A		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           self.assertFalse(tokenizer.special_attribute_present		)

           # If remote code is disabled, we load the local one.
           __snake_case:						List[Any]   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)
           self.assertFalse(tokenizer.special_attribute_present		)
           __snake_case:						Optional[int]   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A							,				use_fast=A		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           self.assertFalse(tokenizer.special_attribute_present		)

           # If remote is enabled, we load from the Hub
           __snake_case:						Any   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)
           self.assertTrue(tokenizer.special_attribute_present		)
           __snake_case:						Dict   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer"""							,				trust_remote_code=A							,				use_fast=A		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
           self.assertTrue(tokenizer.special_attribute_present		)

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




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						int   =						AutoTokenizer.from_pretrained(
             """hf-internal-testing/test_dynamic_tokenizer_legacy"""							,				trust_remote_code=A		)
         self.assertTrue(tokenizer.special_attribute_present		)
         if is_tokenizers_available():
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizerFast"""		)

           # Test we can also load the slow version
           __snake_case:						int   =						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer_legacy"""							,				trust_remote_code=A							,				use_fast=A		)
           self.assertTrue(tokenizer.special_attribute_present		)
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)
         else:
           self.assertEqual(tokenizer.__class__.__name__							,				"""NewTokenizer"""		)




       def 							UpperCAmelCase__ (  self :    Any		):
         with self.assertRaisesRegex(
             A							,				"""bert-base is not a local folder and is not a valid model identifier"""		):
           __snake_case:						Dict   =						AutoTokenizer.from_pretrained("""bert-base"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         with self.assertRaisesRegex(
             A							,				r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""		):
           __snake_case:						List[str]   =						AutoTokenizer.from_pretrained(A							,				revision="""aaaaaa"""		)






       def 							UpperCAmelCase__ (  self :    int		):
         # Make sure we have cached the tokenizer.
         __snake_case:						List[str]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
         with RequestCounter() as counter:
           __snake_case:						List[str]   =						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
           self.assertEqual(counter.get_request_count							,				0		)
           self.assertEqual(counter.head_request_count							,				1		)
           self.assertEqual(counter.other_request_count							,				0		)













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase       :   Any				       =							logging.get_logger(__name__)


__UpperCAmelCase       :   str				       =							Dict[str, Any]
__UpperCAmelCase       :   int				       =							List[Prediction]







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    int							,				*A :    Optional[int]							,				**A :    Optional[int]		):
         super().__init__(*A							,				**A		)

         if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)

         requires_backends(self							,				"""vision"""		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				**A :    Tuple		):
         __snake_case:						List[str]   =						{}
         if "threshold" in kwargs:
           __snake_case:						Optional[Any]   =						kwargs["""threshold"""]
         return {}, {}, postprocess_kwargs




       def __call__(  self :    int							,				*A :    Optional[Any]							,				**A :    Tuple		):
         return super().__call__(*A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str		):
         __snake_case:						Optional[Any]   =						load_image(A		)
         __snake_case:						Dict   =						torch.IntTensor([[image.height, image.width]]		)
         __snake_case:						str   =						self.image_processor(images=[image]							,				return_tensors="""pt"""		)
         if self.tokenizer is not None:
           __snake_case:						Optional[Any]   =						self.tokenizer(text=inputs["""words"""]							,				boxes=inputs["""boxes"""]							,				return_tensors="""pt"""		)
         __snake_case:						Any   =						target_size
         return inputs




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Dict		):
         __snake_case:						int   =						model_inputs.pop("""target_size"""		)
         __snake_case:						int   =						self.model(**A		)
         __snake_case:						Any   =						outputs.__class__({"""target_size""": target_size, **outputs}		)
         if self.tokenizer is not None:
           __snake_case:						Optional[int]   =						model_inputs["""bbox"""]
         return model_outputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]							,				A :    Union[str, Any]=0.9		):
         __snake_case:						Optional[Any]   =						model_outputs["""target_size"""]
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           __snake_case  ,   __snake_case:						Union[str, Any]   =						target_size[0].tolist()

           def unnormalize(A :    Tuple		):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1_000),
                         (height * bbox[1] / 1_000),
                         (width * bbox[2] / 1_000),
                         (height * bbox[3] / 1_000),
                     ]		)		)

           __snake_case  ,   __snake_case:						Optional[int]   =						model_outputs["""logits"""].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
           __snake_case:						List[Any]   =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           __snake_case:						int   =						[unnormalize(A		) for bbox in model_outputs["""bbox"""].squeeze(0		)]
           __snake_case:						int   =						["""score""", """label""", """box"""]
           __snake_case:						List[Any]   =						[dict(zip(A							,				A		)		) for vals in zip(scores.tolist()							,				A							,				A		) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           __snake_case:						Tuple   =						self.image_processor.post_process_object_detection(A							,				A							,				A		)
           __snake_case:						Optional[Any]   =						raw_annotations[0]
           __snake_case:						int   =						raw_annotation["""scores"""]
           __snake_case:						int   =						raw_annotation["""labels"""]
           __snake_case:						Optional[Any]   =						raw_annotation["""boxes"""]

           __snake_case:						Union[str, Any]   =						scores.tolist()
           __snake_case:						List[str]   =						[self.model.config.idalabel[label.item()] for label in labels]
           __snake_case:						List[str]   =						[self._get_bounding_box(A		) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           __snake_case:						List[Any]   =						["""score""", """label""", """box"""]
           __snake_case:						Dict   =						[
               dict(zip(A							,				A		)		)
               for vals in zip(raw_annotation["""scores"""]							,				raw_annotation["""labels"""]							,				raw_annotation["""boxes"""]		)
           ]

         return annotation






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    "torch.Tensor"		):
         if self.framework != "pt":
           raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						box.int().tolist()
         __snake_case:						Optional[Any]   =						{
             """xmin""": xmin,
             """ymin""": ymin,
             """xmax""": xmax,
             """ymax""": ymax,
         }
         return bbox






