



'''simple docstring'''






import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase:      str		 =		logging.get_logger(__name__)

lowerCAmelCase:      Dict		 =		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase:      Tuple		 =		{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase:      Optional[Any]		 =		{
    'Salesforce/codegen-350M-mono': 2_0_4_8,
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					["""input_ids""", """attention_mask"""]
    lowercase__				=					CodeGenTokenizer







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						List[str]=None		,    __snake_case				:						List[str]=None		,    __snake_case				:						List[Any]="<|endoftext|>"		,    __snake_case				:						List[str]="<|endoftext|>"		,    __snake_case				:						Dict="<|endoftext|>"		,    __snake_case				:						Union[str, Any]=False		,    **__snake_case				:						str		,    ):
      super().__init__(
          __snake_case		,    __snake_case		,    tokenizer_file=__snake_case		,    unk_token=__snake_case		,    bos_token=__snake_case		,    eos_token=__snake_case		,    add_prefix_space=__snake_case		,    **__snake_case		,    )

      if kwargs.pop('add_bos_token'		,    __snake_case			):
        a		:      List[Any]       =  kwargs.pop('name_or_path'		,    ''			)
        raise ValueError(
            'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
            'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
            F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
            F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
            'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
            ' so that the fast tokenizer works correctly.'			)

      a		:      Any       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
      if pre_tok_state.get('add_prefix_space'		,    __snake_case			) != add_prefix_space:
        a		:      Union[str, Any]       =  getattr(__snake_case		,    pre_tok_state.pop('type'			)			)
        a		:      str       =  add_prefix_space
        a		:      Union[str, Any]       =  pre_tok_class(**__snake_case			)

      a		:      Optional[int]       =  add_prefix_space







    def     lowercase_ (   self				:						Optional[Any]		,    *__snake_case				:						List[Any]		,    **__snake_case				:						int			):
      a		:      Any       =  kwargs.get('is_split_into_words'		,    __snake_case			)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						int		,    *__snake_case				:						Dict		,    **__snake_case				:						int			):
      a		:      List[str]       =  kwargs.get('is_split_into_words'		,    __snake_case			)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      a		:      Tuple       =  self._tokenizer.model.save(__snake_case		,    name=__snake_case			)
      return tuple(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]		,    __snake_case				:						bool = False		,    __snake_case				:						bool = None		,    __snake_case				:						Optional[List[str]] = None		,    **__snake_case				:						Dict		,    ):
      a		:      Optional[Any]       =  super().decode(
          token_ids=__snake_case		,    skip_special_tokens=__snake_case		,    clean_up_tokenization_spaces=__snake_case		,    **__snake_case		,    )

      if truncate_before_pattern is not None and len(__snake_case			) > 0:
        a		:      Dict       =  self.truncate(__snake_case		,    __snake_case			)

      return decoded_text







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Dict			):
      def find_re(__snake_case				:						Any		,    __snake_case				:						List[str]		,    __snake_case				:						int			):
        a		:      Optional[int]       =  pattern.search(__snake_case		,    __snake_case			)
        return m.start() if m else -1

      a		:      Optional[Any]       =  [re.compile(__snake_case		,    re.MULTILINE			) for pattern in truncate_before_pattern]

      a		:      Any       =  list(re.finditer('^print'		,    __snake_case		,    re.MULTILINE			)			)

      if len(__snake_case			) > 1:
        a		:      Union[str, Any]       =  completion[: prints[1].start()]

      a		:      Any       =  list(re.finditer('^def'		,    __snake_case		,    re.MULTILINE			)			)

      if len(__snake_case			) > 1:
        a		:      Union[str, Any]       =  completion[: defs[1].start()]

      a		:      Dict       =  0

      a		:      Optional[Any]       =  [
          pos for pos in [find_re(__snake_case		,    __snake_case		,    __snake_case			) for terminal in terminals] if pos != -1
      ]

      if len(__snake_case			) > 0:
        return completion[: min(__snake_case			)]
      else:
        return completion




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:

     class 							a__:







         @staticmethod
         def     lowercase_ (   *__snake_case				:						int		,    **__snake_case				:						str			):
           pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							a__(      unittest.TestCase			):
    lowercase__				=					MODEL_FOR_OBJECT_DETECTION_MAPPING







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Tuple			):
      a		:      Tuple       =  ObjectDetectionPipeline(model=__snake_case		,    image_processor=__snake_case			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[Any]			):
      a		:      Any       =  object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'		,    threshold=0.0			)

      self.assertGreater(len(__snake_case			)		,    0			)
      for detected_object in outputs:
        self.assertEqual(
            __snake_case		,    {
                'score': ANY(__snake_case			),
                'label': ANY(__snake_case			),
                'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
            }		,    )

      import datasets

      a		:      Any       =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,    'image'		,    split='test'			)

      a		:      Tuple       =  [
          Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			),
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          # RGBA
          dataset[0]['file'],
          # LA
          dataset[1]['file'],
          # L
          dataset[2]['file'],
      ]
      a		:      List[Any]       =  object_detector(__snake_case		,    threshold=0.0			)

      self.assertEqual(len(__snake_case			)		,    len(__snake_case			)			)
      for outputs in batch_outputs:
        self.assertGreater(len(__snake_case			)		,    0			)
        for detected_object in outputs:
          self.assertEqual(
              __snake_case		,    {
                  'score': ANY(__snake_case			),
                  'label': ANY(__snake_case			),
                  'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
              }		,    )







    @require_tf
    @unittest.skip('Object detection not implemented in TF'			)
    def     lowercase_ (   self				:						List[str]			):
      pass







    @require_torch
    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  'hf-internal-testing/tiny-detr-mobilenetsv3'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      str       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      Any       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      str       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=0.0			)

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
          ]		,    )

      a		:      Dict       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]		,    threshold=0.0		,    )

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Union[str, Any]       =  'facebook/detr-resnet-50'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      List[Any]       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      str       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      List[Any]       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      List[str]       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Any			):
      a		:      Any       =  'facebook/detr-resnet-50'

      a		:      int       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Dict       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      int       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[Any]       =  0.9985
      a		:      Optional[int]       =  'facebook/detr-resnet-50'

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Any       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=__snake_case			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )







    @require_torch
    @require_pytesseract
    @slow
    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  'Narsil/layoutlmv3-finetuned-funsd'
      a		:      Optional[int]       =  0.9993

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case		,    threshold=__snake_case			)

      a		:      Union[str, Any]       =  object_detector(
          'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
          ]		,    )