import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class   lowercase_    :



	"""simple docstring"""





	def __init__(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE="resnet50"						,     __SCREAMING_SNAKE_CASE=3						,     __SCREAMING_SNAKE_CASE=32						,     __SCREAMING_SNAKE_CASE=3						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=True						,     )		->List[Any]:
								lowerCAmelCase      						=						parent
								lowerCAmelCase      						=						out_indices if out_indices is not None else [4]
								lowerCAmelCase      						=						stage_names
								lowerCAmelCase      						=						out_features
								lowerCAmelCase      						=						backbone
								lowerCAmelCase      						=						batch_size
								lowerCAmelCase      						=						image_size
								lowerCAmelCase      						=						num_channels
								lowerCAmelCase      						=						use_pretrained_backbone
								lowerCAmelCase      						=						is_training


	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								lowerCAmelCase      						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
								lowerCAmelCase      						=						self.get_config()

								return config, pixel_values


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[Any]:
								return TimmBackboneConfig(
								    image_size=self.image_size						,     num_channels=self.num_channels						,     out_features=self.out_features						,     out_indices=self.out_indices						,     stage_names=self.stage_names						,     use_pretrained_backbone=self.use_pretrained_backbone						,     backbone=self.backbone						,     )


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->Dict:
								lowerCAmelCase      						=						TimmBackbone(config=__lowerCamelCase							)
								model.to(__lowerCamelCase							)
								model.eval()
								with torch.no_grad():
															lowerCAmelCase      						=						model(__lowerCamelCase							)
								self.parent.assertEqual(
								    result.feature_map[-1].shape						,     (self.batch_size, model.channels[-1], 14, 14)						,     )




	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[Any]:
								lowerCAmelCase      						=						self.prepare_config_and_inputs()
								lowerCAmelCase						, lowerCAmelCase      						=						config_and_inputs
								lowerCAmelCase      						=						{'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_torch
@require_timm
class   lowercase_    (    __snake_case ,	__snake_case ,	__snake_case ,	unittest.TestCase		):



	"""simple docstring"""





	UpperCAmelCase_    :   int  =							(TimmBackbone,) if is_torch_available() else ()
	UpperCAmelCase_    :   Tuple  =							{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
	UpperCAmelCase_    :   int  =							False
	UpperCAmelCase_    :   List[str]  =							False
	UpperCAmelCase_    :   Optional[Any]  =							False
	UpperCAmelCase_    :   Optional[int]  =							False


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								lowerCAmelCase      						=						TimmBackboneModelTester(self							)
								lowerCAmelCase      						=						ConfigTester(self						,     config_class=__lowerCamelCase						,     has_text_modality=__lowerCamelCase							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->int:
								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()


	def      SCREAMING_SNAKE_CASE_							(				self							)		->str:
								lowerCAmelCase      						=						'''resnet18'''
								lowerCAmelCase      						=						'''microsoft/resnet-18'''

								lowerCAmelCase      						=						AutoBackbone.from_pretrained(__lowerCamelCase						,     use_timm_backbone=__lowerCamelCase							)
								lowerCAmelCase      						=						AutoBackbone.from_pretrained(__lowerCamelCase							)

								self.assertEqual(len(timm_model.out_features							)						,     len(transformers_model.out_features							)							)
								self.assertEqual(len(timm_model.stage_names							)						,     len(transformers_model.stage_names							)							)
								self.assertEqual(timm_model.channels						,     transformers_model.channels							)
								# Out indices are set to the last layer by default. For timm models, we don't know
								# the number of layers in advance, so we set it to (-1,), whereas for transformers
								# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
								self.assertEqual(timm_model.out_indices						,     (-1,)							)
								self.assertEqual(transformers_model.out_indices						,     [len(timm_model.stage_names							) - 1]							)

								lowerCAmelCase      						=						AutoBackbone.from_pretrained(__lowerCamelCase						,     use_timm_backbone=__lowerCamelCase						,     out_indices=[1, 2, 3]							)
								lowerCAmelCase      						=						AutoBackbone.from_pretrained(__lowerCamelCase						,     out_indices=[1, 2, 3]							)

								self.assertEqual(timm_model.out_indices						,     transformers_model.out_indices							)
								self.assertEqual(len(timm_model.out_features							)						,     len(transformers_model.out_features							)							)
								self.assertEqual(timm_model.channels						,     transformers_model.channels							)


	@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[Any]:
								pass


	@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								pass


	@unittest.skip('''TimmBackbone initialization is managed on the timm side'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								pass


	@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[Any]:
								pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->List[str]:
								pass


	@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								pass


	@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								pass


	@unittest.skip('''Safetensors is not supported by timm.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								pass


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								lowerCAmelCase						, lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs_for_common()

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

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


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								lowerCAmelCase						, lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase      						=						True
								lowerCAmelCase      						=						self.has_attentions

								# no need to test all models as different heads yield the same functionality
								lowerCAmelCase      						=						self.all_model_classes[0]
								lowerCAmelCase      						=						model_class(__lowerCamelCase							)
								model.to(__lowerCamelCase							)

								lowerCAmelCase      						=						self._prepare_for_class(__lowerCamelCase						,     __lowerCamelCase							)
								lowerCAmelCase      						=						model(**__lowerCamelCase							)
								lowerCAmelCase      						=						outputs[0][-1]

								# Encoder-/Decoder-only models
								lowerCAmelCase      						=						outputs.hidden_states[0]
								hidden_states.retain_grad()

								if self.has_attentions:
															lowerCAmelCase      						=						outputs.attentions[0]
															attentions.retain_grad()

								output.flatten()[0].backward(retain_graph=__lowerCamelCase							)

								self.assertIsNotNone(hidden_states.grad							)

								if self.has_attentions:
															self.assertIsNotNone(attentions.grad							)




	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								lowerCAmelCase						, lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase      						=						model_class(__lowerCamelCase							)
															model.to(__lowerCamelCase							)
															model.eval()
															lowerCAmelCase      						=						model(**__lowerCamelCase							)

															self.assertEqual(len(result.feature_maps							)						,     len(config.out_indices							)							)
															self.assertEqual(len(model.channels							)						,     len(config.out_indices							)							)

															# Check output of last stage is taken if out_features=None, out_indices=None
															lowerCAmelCase      						=						copy.deepcopy(__lowerCamelCase							)
															lowerCAmelCase      						=						None
															lowerCAmelCase      						=						model_class(__lowerCamelCase							)
															model.to(__lowerCamelCase							)
															model.eval()
															lowerCAmelCase      						=						model(**__lowerCamelCase							)

															self.assertEqual(len(result.feature_maps							)						,     1							)
															self.assertEqual(len(model.channels							)						,     1							)

															# Check backbone can be initialized with fresh weights
															lowerCAmelCase      						=						copy.deepcopy(__lowerCamelCase							)
															lowerCAmelCase      						=						False
															lowerCAmelCase      						=						model_class(__lowerCamelCase							)
															model.to(__lowerCamelCase							)
															model.eval()
															lowerCAmelCase      						=						model(**__lowerCamelCase							)







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[Any]:
   # This defines a "chinese character" as anything in the CJK Unicode block:
   #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
   #
   # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
   # despite its name. The modern Korean Hangul alphabet is a different block,
   # as is Japanese Hiragana and Katakana. Those alphabets are used to write
   # space-separated words, so they are not treated specially and handled
   # like the all of the other languages.
   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					(    lowerCAmelCase_ )    ->List[str]:
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCAmelCase      					=  ord(lowerCAmelCase_ )
      if not _is_chinese_char(lowerCAmelCase_ ):
         return 0
   return 1
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[int]:
   UpperCAmelCase      					=  set()

   for token in tokens:
      UpperCAmelCase      					=  len(lowerCAmelCase_ ) > 1 and is_chinese(lowerCAmelCase_ )
      if chinese_word:
         word_set.add(lowerCAmelCase_ )
   UpperCAmelCase      					=  list(lowerCAmelCase_ )
   return word_list
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Union[str, Any]:
   if not chinese_word_set:
      return bert_tokens
   UpperCAmelCase      					=  max([len(lowerCAmelCase_ ) for w in chinese_word_set] )

   UpperCAmelCase      					=  bert_tokens
   UpperCAmelCase					,	UpperCAmelCase      					=  0, len(lowerCAmelCase_ )
   while start < end:
      UpperCAmelCase      					=  True
      if is_chinese(bert_word[start] ):
         UpperCAmelCase      					=  min(end - start	,    lowerCAmelCase_ )
         for i in range(lowerCAmelCase_	,    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					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   UpperCAmelCase      					=  []

   for i in range(0	,    len(lowerCAmelCase_ )	,    1_0_0 ):
      UpperCAmelCase      					=  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]	,    tasks=["""cws"""] ).cws
      UpperCAmelCase      					=  [get_chinese_word(lowerCAmelCase_ ) for r in res]
      ltp_res.extend(lowerCAmelCase_ )
   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

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

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

   assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )

   return ref_ids
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
   # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
   with open(args.file_name	,    """r"""	,    encoding="""utf-8""" ) as f:
      UpperCAmelCase      					=  f.readlines()
   UpperCAmelCase      					=  [line.strip() for line in data if len(lowerCAmelCase_ ) > 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(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

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


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

 __a	 =      parser.parse_args()
 main(args)



