


'''simple docstring'''
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=3							,      a_=3_2							,      a_=3							,      a_=1_0							,      a_=[1_0, 2_0, 3_0, 4_0]							,      a_=[1, 1, 2, 1]							,      a_=True							,      a_=True							,      a_="relu"							,      a_=3							,      a_=None							,      )    ->  Optional[int]:
					lowercase						:		List[str]   					=		parent
					lowercase						:		List[Any]   					=		batch_size
					lowercase						:		str   					=		image_size
					lowercase						:		Any   					=		num_channels
					lowercase						:		int   					=		embeddings_size
					lowercase						:		List[str]   					=		hidden_sizes
					lowercase						:		List[str]   					=		depths
					lowercase						:		List[Any]   					=		is_training
					lowercase						:		int   					=		use_labels
					lowercase						:		Tuple   					=		hidden_act
					lowercase						:		List[str]   					=		num_labels
					lowercase						:		List[str]   					=		scope
					lowercase						:		List[str]   					=		len(a_							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Tuple   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		Tuple   					=		self.get_config()

					return config, pixel_values

				def 	a__		(     self							)    ->  Dict:
					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							,      image_size=self.image_size							,      )

				def 	a__		(     self							,      a_							,      a_							)    ->  str:
					lowercase						:		Optional[Any]   					=		FlaxRegNetModel(config=a_							)
					lowercase						:		Any   					=		model(a_							)

					# Output shape (b, c, h, w)
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,      )

				def 	a__		(     self							,      a_							,      a_							)    ->  Dict:
					lowercase						:		str   					=		self.num_labels
					lowercase						:		Dict   					=		FlaxRegNetForImageClassification(config=a_							)
					lowercase						:		int   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)


				def 	a__		(     self							)    ->  str:
					lowercase						:		int   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase						:		int   					=		config_and_inputs
					lowercase						:		Dict   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  None:
					lowercase						:		Any   					=		FlaxRegNetModelTester(self							)
					lowercase						:		Optional[int]   					=		ConfigTester(self							,      config_class=a_							,      has_text_modality=a_							)

				def 	a__		(     self							)    ->  List[str]:
					self.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()

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

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				@unittest.skip(reason="RegNet does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  str:
					pass

				@unittest.skip(reason="RegNet does not support input and output embeddings"							)
				def 	a__		(     self							)    ->  int:
					pass

				def 	a__		(     self							)    ->  List[str]:
					lowercase					,		lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Union[str, Any]   					=		model_class(a_							)
						lowercase						:		Any   					=		inspect.signature(model.__call__							)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowercase						:		int   					=		[*signature.parameters.keys()]

						lowercase						:		Any   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  str:
					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		List[str]   					=		model_class(a_							)

						lowercase						:		Union[str, Any]   					=		model(**self._prepare_for_class(a_							,      a_							)							)

						lowercase						:		Any   					=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase						:		Tuple   					=		self.model_tester.num_stages
						self.assertEqual(len(a_							)							,      expected_num_stages + 1							)

					lowercase					,		lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Dict   					=		True
						check_hidden_states_output(a_							,      a_							,      a_							)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						lowercase						:		Optional[Any]   					=		True

						check_hidden_states_output(a_							,      a_							,      a_							)


				def 	a__		(     self							)    ->  Dict:
					lowercase					,		lowercase						:		int   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__							):
							lowercase						:		str   					=		self._prepare_for_class(a_							,      a_							)
							lowercase						:		int   					=		model_class(a_							)

							@jax.jit
							def model_jitted(a_							,      **a_							):
								return model(pixel_values=a_							,      **a_							)

							with self.subTest("JIT Enabled"							):
								lowercase						:		Optional[int]   					=		model_jitted(**a_							).to_tuple()

							with self.subTest("JIT Disabled"							):
								with jax.disable_jit():
									lowercase						:		Union[str, Any]   					=		model_jitted(**a_							).to_tuple()

							self.assertEqual(len(a_							)							,      len(a_							)							)
							for jitted_output, output in zip(a_							,      a_							):
								self.assertEqual(jitted_output.shape							,      output.shape							)




def 	_A  (       )  ->				str:
	lowercase						:		Optional[Any]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  List[str]:
					return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"							) if is_vision_available() else None


				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"							)

					lowercase						:		int   					=		self.default_image_processor
					lowercase						:		Dict   					=		prepare_img()
					lowercase						:		str   					=		image_processor(images=a_							,      return_tensors="np"							)

					lowercase						:		List[Any]   					=		model(**a_							)

					# verify the logits
					lowercase						:		List[Any]   					=		(1, 1_0_0_0)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		Union[str, Any]   					=		jnp.array([-0.41_80, -1.50_51, -3.48_36]							)

					self.assertTrue(jnp.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4							)							)






'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def 	_A  (       A							)  ->				int:
	# 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 	_A  (       A							)  ->				List[Any]:
	# word like '180' or '身高' or '神'
	for char in word:
		lowercase						:		int   					=		ord(A							)
		if not _is_chinese_char(A							):
			return 0
	return 1




def 	_A  (       A							)  ->				Dict:
	lowercase						:		Any   					=		set()

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




def 	_A  (       A							,A							)  ->				Tuple:
	if not chinese_word_set:
		return bert_tokens
	lowercase						:		Optional[Any]   					=		max([len(A							) for w in chinese_word_set]							)

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




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		[]

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

	lowercase						:		List[str]   					=		[]
	for i in range(0							,len(A							)							,1_0_0							):
		lowercase						:		int   					=		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							)

	lowercase						:		Optional[Any]   					=		[]
	for input_ids, chinese_word in zip(A							,A							):
		lowercase						:		str   					=		[]
		for id in input_ids:
			lowercase						:		Any   					=		bert_tokenizer._convert_id_to_token(A							)
			input_tokens.append(A							)
		lowercase						:		Tuple   					=		add_sub_symbol(A							,A							)
		lowercase						:		Tuple   					=		[]
		# 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] == "##":
				lowercase						:		Any   					=		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 	_A  (       A							)  ->				Union[str, Any]:
	# 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:
		lowercase						:		List[str]   					=		f.readlines()
	lowercase						:		Tuple   					=		[line.strip() for line in data if len(A							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
	lowercase						:		Any   					=		LTP(args.ltp							)  # faster in GPU device
	lowercase						:		int   					=		BertTokenizer.from_pretrained(args.bert							)

	lowercase						:		List[str]   					=		prepare_ref(A							,A							,A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : List[Any]             =				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""")

					lowerCAmelCase   : Union[str, Any]             =				parser.parse_args()
					main(args)


