



'''simple docstring'''





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32  ,__UpperCAmelCase=3  ,__UpperCAmelCase=10  ,__UpperCAmelCase=[10, 20, 30, 40]  ,__UpperCAmelCase=[1, 1, 2, 1]  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase="relu"  ,__UpperCAmelCase=3  ,__UpperCAmelCase=None  ,)    ->		int:
      lowerCAmelCase__ :  List[Any]							      =					parent
      lowerCAmelCase__ :  List[Any]							      =					batch_size
      lowerCAmelCase__ :  Tuple							      =					image_size
      lowerCAmelCase__ :  List[str]							      =					num_channels
      lowerCAmelCase__ :  int							      =					embeddings_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Any							      =					depths
      lowerCAmelCase__ :  Optional[Any]							      =					is_training
      lowerCAmelCase__ :  Optional[int]							      =					use_labels
      lowerCAmelCase__ :  Union[str, Any]							      =					hidden_act
      lowerCAmelCase__ :  Union[str, Any]							      =					num_labels
      lowerCAmelCase__ :  Union[str, Any]							      =					scope
      lowerCAmelCase__ :  Optional[int]							      =					len(__lowerCamelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Union[str, Any]							      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      lowerCAmelCase__ :  List[Any]							      =					None
      if self.use_labels:
          lowerCAmelCase__ :  Dict							      =					ids_tensor([self.batch_size]  ,self.num_labels      )

      lowerCAmelCase__ :  List[str]							      =					self.get_config()
      return config, pixel_values, labels




  def 							UpperCAmelCase_	(    self      )    ->		int:
      return RegNetConfig(
          num_channels=self.num_channels  ,embeddings_size=self.embeddings_size  ,hidden_sizes=self.hidden_sizes  ,depths=self.depths  ,hidden_act=self.hidden_act  ,num_labels=self.num_labels  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		int:
      lowerCAmelCase__ :  int							      =					TFRegNetModel(config=__lowerCamelCase      )
      lowerCAmelCase__ :  List[Any]							      =					model(__lowerCamelCase  ,training=__lowerCamelCase      )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.num_labels
      lowerCAmelCase__ :  Any							      =					TFRegNetForImageClassification(__lowerCamelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					model(__lowerCamelCase  ,labels=__lowerCamelCase  ,training=__lowerCamelCase      )
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)      )






  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Dict							      =					self.prepare_config_and_inputs()
      lowerCAmelCase__ :  Optional[Any]							      =					config_and_inputs
      lowerCAmelCase__ :  str							      =					{'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_tf
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      str				=      (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
  __lowercase							:      List[Any]				=      (
      {'''feature-extraction''': TFRegNetModel, '''image-classification''': TFRegNetForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowercase							:      List[Any]				=      False
  __lowercase							:      Tuple				=      False
  __lowercase							:      Optional[int]				=      False
  __lowercase							:      Dict				=      False
  __lowercase							:      int				=      False




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Dict							      =					TFRegNetModelTester(self      )
      lowerCAmelCase__ :  Optional[Any]							      =					ConfigTester(self  ,config_class=__lowerCamelCase  ,has_text_modality=__lowerCamelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return




  @unittest.skip(reason="""RegNet does not use inputs_embeds"""      )
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      pass




  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0  ,reason="""TF does not support backprop for grouped convolutions on CPU."""  ,)
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      super().test_keras_fit()




  @unittest.skip(reason="""RegNet does not support input and output embeddings"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      pass




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__ :  List[str]							      =					model_class(__lowerCamelCase      )
          lowerCAmelCase__ :  List[str]							      =					inspect.signature(model.call      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase__ :  Optional[Any]							      =					[*signature.parameters.keys()]

          lowerCAmelCase__ :  Tuple							      =					['''pixel_values''']
          self.assertListEqual(arg_names[:1]  ,__lowerCamelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  int							      =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:




      def check_hidden_states_output(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  List[str]							      =					model_class(__lowerCamelCase      )
          lowerCAmelCase__ :  Tuple							      =					model(**self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase      )  ,training=__lowerCamelCase      )

          lowerCAmelCase__ :  Dict							      =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase__ :  List[Any]							      =					self.model_tester.num_stages
          self.assertEqual(len(__lowerCamelCase      )  ,expected_num_stages + 1      )

          # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]      )  ,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]  ,)

      lowerCAmelCase__ :  List[Any]							      =					self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase__ :  Tuple							      =					['''basic''', '''bottleneck''']
      for model_class in self.all_model_classes:
          for layer_type in layers_type:
              lowerCAmelCase__ :  Union[str, Any]							      =					layer_type
              lowerCAmelCase__ :  Dict							      =					True
              check_hidden_states_output(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase      )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase__ :  str							      =					True

              check_hidden_states_output(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Any							      =					self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase={}      ):
          lowerCAmelCase__ :  int							      =					model(__lowerCamelCase  ,return_dict=__lowerCamelCase  ,**__lowerCamelCase      )
          lowerCAmelCase__ :  int							      =					model(__lowerCamelCase  ,return_dict=__lowerCamelCase  ,**__lowerCamelCase      ).to_tuple()

          def recursive_check(__UpperCAmelCase  ,__UpperCAmelCase      ):
              if isinstance(__lowerCamelCase  ,(List, Tuple)      ):
                  for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase  ,__lowerCamelCase      ):
                      recursive_check(__lowerCamelCase  ,__lowerCamelCase      )
              elif tuple_object is None:
                  return
              else:
                  self.assertTrue(
                      all(tf.equal(__lowerCamelCase  ,__lowerCamelCase      )      )  ,msg=(
                          """Tuple and dict output are not equal. Difference:"""
                          F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object      )      )}"""
                      )  ,)

              recursive_check(__lowerCamelCase  ,__lowerCamelCase      )

      for model_class in self.all_model_classes:
          lowerCAmelCase__ :  Union[str, Any]							      =					model_class(__lowerCamelCase      )

          lowerCAmelCase__ :  int							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase      )
          lowerCAmelCase__ :  int							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase      )
          check_equivalence(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase      )

          lowerCAmelCase__ :  Optional[Any]							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase  ,return_labels=__lowerCamelCase      )
          lowerCAmelCase__ :  Optional[Any]							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase  ,return_labels=__lowerCamelCase      )
          check_equivalence(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase      )

          lowerCAmelCase__ :  Optional[int]							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase      )
          lowerCAmelCase__ :  Tuple							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase      )
          check_equivalence(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase  ,{"""output_hidden_states""": True}      )

          lowerCAmelCase__ :  Optional[int]							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase  ,return_labels=__lowerCamelCase      )
          lowerCAmelCase__ :  str							      =					self._prepare_for_class(__lowerCamelCase  ,__lowerCamelCase  ,return_labels=__lowerCamelCase      )
          check_equivalence(__lowerCamelCase  ,__lowerCamelCase  ,__lowerCamelCase  ,{"""output_hidden_states""": True}      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  str							      =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:




      for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__ :  int							      =					TFRegNetModel.from_pretrained(__lowerCamelCase      )
          self.assertIsNotNone(__lowerCamelCase      )







def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Dict							      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
    return image






@require_tf
@require_vision
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      return (
          AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
          if is_vision_available()
          else None
      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      lowerCAmelCase__ :  int							      =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

      lowerCAmelCase__ :  List[str]							      =					self.default_image_processor
      lowerCAmelCase__ :  str							      =					prepare_img()
      lowerCAmelCase__ :  Optional[int]							      =					image_processor(images=__lowerCamelCase  ,return_tensors="""tf"""      )

      # forward pass
      lowerCAmelCase__ :  Dict							      =					model(**__lowerCamelCase  ,training=__lowerCamelCase      )

      # verify the logits
      lowerCAmelCase__ :  Optional[Any]							      =					tf.TensorShape((1, 1000)      )
      self.assertEqual(outputs.logits.shape  ,__lowerCamelCase      )

      lowerCAmelCase__ :  int							      =					tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]      )

      tf.debugging.assert_near(outputs.logits[0, :3]  ,__lowerCamelCase  ,atol=1E-4      )









"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		__lowercase     (   snake_case_  :  int						)   ->Tuple:



    '''simple docstring'''







    if (
        (cp >= 0x4e00 and cp <= 0x9fff)
        or (cp >= 0x3400 and cp <= 0x4dbf)  #
        or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
        or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
        or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
        or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
        or (cp >= 0xf900 and cp <= 0xfaff)
        or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
    ):  #
        return True

    return False







def 		__lowercase     (   snake_case_  :  str						)   ->Dict:



    '''simple docstring'''







    for char in word:
        __A    :						int						 =     ord(snake_case_						)
        if not _is_chinese_char(snake_case_						):
            return 0
    return 1







def 		__lowercase     (   snake_case_  :  List[str]						)   ->List[Any]:



    '''simple docstring'''







    __A    :						str						 =     set()

    for token in tokens:
        __A    :						List[Any]						 =     len(snake_case_						) > 1 and is_chinese(snake_case_						)
        if chinese_word:
            word_set.add(snake_case_						)
    __A    :						Any						 =     list(snake_case_						)
    return word_list







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  set()						)   ->Any:



    '''simple docstring'''







    if not chinese_word_set:
        return bert_tokens
    __A    :						List[Any]						 =     max([len(snake_case_						) for w in chinese_word_set]						)

    __A    :						List[str]						 =     bert_tokens
    __A ,       __A    :						Any						 =     0, len(snake_case_						)
    while start < end:
        __A    :						str						 =     True
        if is_chinese(bert_word[start]						):
            __A    :						int						 =     min(end - start				,snake_case_						)
            for i in range(snake_case_				,1				,-1						):
                __A    :						Any						 =     ''''''.join(bert_word[start : start + i]						)
                if whole_word in chinese_word_set:
                    for j in range(start + 1				,start + i						):
                        __A    :						Any						 =     '''##''' + bert_word[j]
                    __A    :						Optional[int]						 =     start + i
                    __A    :						str						 =     False
                    break
        if single_word:
            start += 1
    return bert_word







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  LTP				,snake_case_  :  BertTokenizer						)   ->Dict:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     []

    for i in range(0				,len(snake_case_						)				,100						):
        __A    :						int						 =     ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        __A    :						List[Any]						 =     [get_chinese_word(snake_case_						) for r in res]
        ltp_res.extend(snake_case_						)
    assert len(snake_case_						) == len(snake_case_						)

    __A    :						Any						 =     []
    for i in range(0				,len(snake_case_						)				,100						):
        __A    :						Tuple						 =     bert_tokenizer(lines[i : i + 100]				,add_special_tokens=snake_case_				,truncation=snake_case_				,max_length=512						)
        bert_res.extend(res['''input_ids''']						)
    assert len(snake_case_						) == len(snake_case_						)

    __A    :						Optional[int]						 =     []
    for input_ids, chinese_word in zip(snake_case_				,snake_case_						):
        __A    :						List[str]						 =     []
        for id in input_ids:
            __A    :						Tuple						 =     bert_tokenizer._convert_id_to_token(snake_case_						)
            input_tokens.append(snake_case_						)
        __A    :						Optional[int]						 =     add_sub_symbol(snake_case_				,snake_case_						)
        __A    :						Optional[Any]						 =     []
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(snake_case_						):
            if token[:2] == "##":
                __A    :						Optional[Any]						 =     token[2:]
                # save chinese tokens' pos
                if len(snake_case_						) == 1 and _is_chinese_char(ord(snake_case_						)						):
                    ref_id.append(snake_case_						)
        ref_ids.append(snake_case_						)

    assert len(snake_case_						) == len(snake_case_						)

    return ref_ids







def 		__lowercase     (   snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







    with open(args.file_name				,'''r'''				,encoding='''utf-8'''						) as f:
        __A    :						List[str]						 =     f.readlines()
    __A    :						Optional[Any]						 =     [line.strip() for line in data if len(snake_case_						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    __A    :						str						 =     LTP(args.ltp						)  # faster in GPU device
    __A    :						Optional[int]						 =     BertTokenizer.from_pretrained(args.bert						)

    __A    :						Optional[Any]						 =     prepare_ref(snake_case_				,snake_case_				,snake_case_						)

    with open(args.save_path				,'''w'''				,encoding='''utf-8'''						) as f:
        __A    :						int						 =     [json.dumps(snake_case_						) + '''\n''' for ref in ref_ids]
        f.writelines(snake_case_						)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser(description="""prepare_chinese_ref""")
       parser.add_argument(
           """--file_name""",
           type=str,
           default="""./resources/chinese-demo.txt""",
           help="""file need process, same as training data in lm""",
       )
       parser.add_argument(
           """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
       )
       parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
       parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

       a_ =			parser.parse_args()
       main(args)




