


from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
    from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         self.parent.assertTrue(hasattr(_lowercase		,						"embed_dim" ) )
         self.parent.assertTrue(hasattr(_lowercase		,						"num_heads" ) )




class     lowercase__  :






  def __init__(					self      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					List[str]=13		,						_lowercase      :					Dict=64		,						_lowercase      :					Optional[int]=3		,						_lowercase      :					Any=[16, 48, 96]		,						_lowercase      :					Tuple=[1, 3, 6]		,						_lowercase      :					List[str]=[1, 2, 10]		,						_lowercase      :					Dict=[7, 3, 3]		,						_lowercase      :					int=[4, 2, 2]		,						_lowercase      :					Dict=[2, 1, 1]		,						_lowercase      :					Dict=[2, 2, 2]		,						_lowercase      :					Optional[int]=[False, False, True]		,						_lowercase      :					str=[0.0, 0.0, 0.0]		,						_lowercase      :					List[Any]=0.0_2		,						_lowercase      :					int=1E-12		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					List[str]=2		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     patch_sizes
         UpperCAmelCase__										=     patch_stride
         UpperCAmelCase__										=     patch_padding
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     use_labels
         UpperCAmelCase__										=     num_labels
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     embed_dim
         UpperCAmelCase__										=     num_heads
         UpperCAmelCase__										=     stride_kv
         UpperCAmelCase__										=     depth
         UpperCAmelCase__										=     cls_token
         UpperCAmelCase__										=     attention_drop_rate
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     layer_norm_eps






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase__										=     None
         if self.use_labels:
                # create a random int32 tensor of given shape
                UpperCAmelCase__										=     ids_tensor([self.batch_size]		,						self.num_labels )

         UpperCAmelCase__										=     self.get_config()
         return config, pixel_values, labels






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         return CvtConfig(
             image_size=self.image_size		,						num_labels=self.num_labels		,						num_channels=self.num_channels		,						embed_dim=self.embed_dim		,						num_heads=self.num_heads		,						patch_sizes=self.patch_sizes		,						patch_padding=self.patch_padding		,						patch_stride=self.patch_stride		,						stride_kv=self.stride_kv		,						depth=self.depth		,						cls_token=self.cls_token		,						attention_drop_rate=self.attention_drop_rate		,						initializer_range=self.initializer_range		,						)






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtModel(config=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						training=_lowercase )
         UpperCAmelCase__										=     (self.image_size, self.image_size)
         UpperCAmelCase__						,			UpperCAmelCase__										=     image_size[0], image_size[1]
         for i in range(len(self.depth ) ):
                UpperCAmelCase__										=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
                UpperCAmelCase__										=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
         self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.embed_dim[-1], height, width) )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     TFCvtForImageClassification(_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						labels=_lowercase		,						training=_lowercase )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels) )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     config_and_inputs
         UpperCAmelCase__										=     {"pixel_values": pixel_values}
         return config, inputs_dict




@require_tf
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
  A__=  (
      {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
      if is_tf_available()
      else {}
  )
  A__=  False
  A__=  False
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtModelTester(self )
         UpperCAmelCase__										=     TFCvtConfigTester(self		,						config_class=_lowercase		,						has_text_modality=_lowercase		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         self.config_tester.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()






  @unittest.skip(reason="Cvt does not output attentions" )
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass






  @unittest.skip(reason="Cvt does not use inputs_embeds" )
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         pass






  @unittest.skip(reason="Cvt does not support input and output embeddings" )
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         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."		,						)
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         super().test_dataset_conversion()






  @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      :					Union[str, Any] ):




         """simple docstring"""







         super().test_keras_fit()






  @unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8" )
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     tf.keras.mixed_precision.Policy("mixed_float16" )
         tf.keras.mixed_precision.set_global_policy(_lowercase )
         super().test_keras_fit()
         tf.keras.mixed_precision.set_global_policy("float32" )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     model_class(_lowercase )
                UpperCAmelCase__										=     inspect.signature(model.call )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCAmelCase__										=     [*signature.parameters.keys()]

                UpperCAmelCase__										=     ["pixel_values"]
                self.assertListEqual(arg_names[:1]		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         def check_hidden_states_output(_lowercase      :					Optional[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any] ):
                UpperCAmelCase__										=     model_class(_lowercase )

                UpperCAmelCase__										=     model(**self._prepare_for_class(_lowercase		,						_lowercase ) )
                UpperCAmelCase__										=     outputs.hidden_states

                UpperCAmelCase__										=     len(self.model_tester.depth )
                self.assertEqual(len(_lowercase )		,						_lowercase )

                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-3:] )		,						[
                        self.model_tester.embed_dim[0],
                        self.model_tester.image_size // 4,
                        self.model_tester.image_size // 4,
                    ]		,						)

         UpperCAmelCase__						,			UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase__										=     True
                check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__										=     True

                check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFCvtModel.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )





def __UpperCAmelCase       (		)						->					Any:



       '''simple docstring'''





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




@require_tf
@require_vision
class     lowercase__  (					unittest.TestCase		):






  @cached_property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






  @slow
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

         UpperCAmelCase__										=     self.default_image_processor
         UpperCAmelCase__										=     prepare_img()
         UpperCAmelCase__										=     image_processor(images=_lowercase		,						return_tensors="tf" )

         # forward pass
         UpperCAmelCase__										=     model(**_lowercase )

         # verify the logits
         UpperCAmelCase__										=     tf.TensorShape((1, 10_00) )
         self.assertEqual(outputs.logits.shape		,						_lowercase )

         UpperCAmelCase__										=     tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] )
         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,						_lowercase		,						atol=1E-4 ) )









import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       if (
           (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
           or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
           or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
           or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
           or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
           or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
       ):  #
              return True

       return False





def __UpperCAmelCase       (		__A			)						->					List[str]:



       '''simple docstring'''





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





def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     set()

       for token in tokens:
              UpperCAmelCase__										=     len(__A			) > 1 and is_chinese(__A			)
              if chinese_word:
                     word_set.add(__A			)
       UpperCAmelCase__										=     list(__A			)
       return word_list





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





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

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





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
              UpperCAmelCase__										=     [get_chinese_word(__A			) for r in res]
              ltp_res.extend(__A			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     bert_tokenizer(lines[i : i + 1_0_0] ,						add_special_tokens=__A ,						truncation=__A ,						max_length=5_1_2			)
              bert_res.extend(res["input_ids"]			)
       assert len(__A			) == len(__A			)

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

       assert len(__A			) == len(__A			)

       return ref_ids





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





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

       UpperCAmelCase__										=     prepare_ref(__A ,						__A ,						__A			)

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


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)





