
"""simple docstring"""


import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import YolosForObjectDetection, YolosModel
							from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class        A_      :



							"""simple docstring"""






							def __init__(    self    :Optional[int]	,				lowerCamelCase_    :int	,				lowerCamelCase_    :List[Any]=13	,				lowerCamelCase_    :Tuple=[30, 30]	,				lowerCamelCase_    :Optional[int]=2	,				lowerCamelCase_    :Optional[Any]=3	,				lowerCamelCase_    :List[Any]=True	,				lowerCamelCase_    :str=True	,				lowerCamelCase_    :Union[str, Any]=32	,				lowerCamelCase_    :Any=5	,				lowerCamelCase_    :Tuple=4	,				lowerCamelCase_    :Tuple=37	,				lowerCamelCase_    :Any="gelu"	,				lowerCamelCase_    :List[str]=0.1	,				lowerCamelCase_    :Tuple=0.1	,				lowerCamelCase_    :Any=10	,				lowerCamelCase_    :Union[str, Any]=0.02	,				lowerCamelCase_    :Optional[Any]=3	,				lowerCamelCase_    :int=None	,				lowerCamelCase_    :Union[str, Any]=8	,				lowerCamelCase_    :int=10	,				):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=parent
										lowerCamelCase__      :	Union[str, Any]						=batch_size
										lowerCamelCase__      :	Optional[Any]						=image_size
										lowerCamelCase__      :	int						=patch_size
										lowerCamelCase__      :	Any						=num_channels
										lowerCamelCase__      :	int						=is_training
										lowerCamelCase__      :	Optional[int]						=use_labels
										lowerCamelCase__      :	Union[str, Any]						=hidden_size
										lowerCamelCase__      :	str						=num_hidden_layers
										lowerCamelCase__      :	List[str]						=num_attention_heads
										lowerCamelCase__      :	Any						=intermediate_size
										lowerCamelCase__      :	List[str]						=hidden_act
										lowerCamelCase__      :	Union[str, Any]						=hidden_dropout_prob
										lowerCamelCase__      :	List[str]						=attention_probs_dropout_prob
										lowerCamelCase__      :	List[str]						=type_sequence_label_size
										lowerCamelCase__      :	Union[str, Any]						=initializer_range
										lowerCamelCase__      :	Any						=num_labels
										lowerCamelCase__      :	Optional[Any]						=scope
										lowerCamelCase__      :	Optional[int]						=n_targets
										lowerCamelCase__      :	Any						=num_detection_tokens
										# we set the expected sequence length (which is used in several tests)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
										lowerCamelCase__      :	str						=(image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase__      :	Any						=num_patches + 1 + self.num_detection_tokens





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										lowerCamelCase__      :	Union[str, Any]						=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)

										lowerCamelCase__      :	List[str]						=None
										if self.use_labels:
													# labels is a list of Dict (each Dict being the labels for a given example in the batch)
													lowerCamelCase__      :	Any						=[]
													for i in range(self.batch_size			):
																lowerCamelCase__      :	List[str]						={}
																lowerCamelCase__      :	Union[str, Any]						=torch.randint(
																    high=self.num_labels	,				size=(self.n_targets,)	,				device=a_			)
																lowerCamelCase__      :	Dict						=torch.rand(self.n_targets	,				4	,				device=a_			)
																labels.append(a_			)

										lowerCamelCase__      :	str						=self.get_config()

										return config, pixel_values, labels





							def  UpperCAmelCase__				(    self    :str			):





										"""simple docstring"""

										return YolosConfig(
										    image_size=self.image_size	,				patch_size=self.patch_size	,				num_channels=self.num_channels	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				is_decoder=a_	,				initializer_range=self.initializer_range	,				num_detection_tokens=self.num_detection_tokens	,				num_labels=self.num_labels	,				)





							def  UpperCAmelCase__				(    self    :Optional[int]	,				lowerCamelCase_    :str	,				lowerCamelCase_    :Tuple	,				lowerCamelCase_    :Dict			):





										"""simple docstring"""

										lowerCamelCase__      :	Dict						=YolosModel(config=a_			)
										model.to(a_			)
										model.eval()
										lowerCamelCase__      :	Optional[Any]						=model(a_			)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,				(self.batch_size, self.expected_seq_len, self.hidden_size)			)





							def  UpperCAmelCase__				(    self    :str	,				lowerCamelCase_    :Optional[int]	,				lowerCamelCase_    :List[Any]	,				lowerCamelCase_    :Optional[Any]			):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=YolosForObjectDetection(a_			)
										model.to(a_			)
										model.eval()

										lowerCamelCase__      :	Any						=model(pixel_values=a_			)
										lowerCamelCase__      :	Tuple						=model(a_			)

										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)			)
										self.parent.assertEqual(result.pred_boxes.shape	,				(self.batch_size, self.num_detection_tokens, 4)			)

										lowerCamelCase__      :	Tuple						=model(pixel_values=a_	,				labels=a_			)

										self.parent.assertEqual(result.loss.shape	,				()			)
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)			)
										self.parent.assertEqual(result.pred_boxes.shape	,				(self.batch_size, self.num_detection_tokens, 4)			)





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=self.prepare_config_and_inputs()
										lowerCamelCase__      :	int						=config_and_inputs
										lowerCamelCase__      :	Dict						={"pixel_values": pixel_values}
										return config, inputs_dict






@require_torch
class        A_      (	A__ ,   A__ ,   unittest.TestCase  ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_       =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE_       =  (
							    {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
							)

							SCREAMING_SNAKE_CASE_       =  False
							SCREAMING_SNAKE_CASE_       =  False
							SCREAMING_SNAKE_CASE_       =  False
							SCREAMING_SNAKE_CASE_       =  False





							def  UpperCAmelCase__				(    self    :Dict	,				lowerCamelCase_    :List[str]	,				lowerCamelCase_    :List[Any]	,				lowerCamelCase_    :int=False			):





										"""simple docstring"""

										lowerCamelCase__      :	Optional[int]						=super()._prepare_for_class(a_	,				a_	,				return_labels=a_			)

										if return_labels:
													if model_class.__name__ == "YolosForObjectDetection":
																lowerCamelCase__      :	List[str]						=[]
																for i in range(self.model_tester.batch_size			):
																			lowerCamelCase__      :	Optional[int]						={}
																			lowerCamelCase__      :	Optional[Any]						=torch.ones(
																			    size=(self.model_tester.n_targets,)	,				device=a_	,				dtype=torch.long			)
																			lowerCamelCase__      :	List[Any]						=torch.ones(
																			    self.model_tester.n_targets	,				4	,				device=a_	,				dtype=torch.float			)
																			labels.append(a_			)
																lowerCamelCase__      :	Union[str, Any]						=labels

										return inputs_dict





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										lowerCamelCase__      :	Union[str, Any]						=YolosModelTester(self			)
										lowerCamelCase__      :	int						=ConfigTester(self	,				config_class=a_	,				has_text_modality=a_	,				hidden_size=37			)





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										self.config_tester.run_common_tests()





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										pass





							def  UpperCAmelCase__				(    self    :Dict			):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__      :	str						=model_class(a_			)
													self.assertIsInstance(model.get_input_embeddings()	,				(nn.Module)			)
													lowerCamelCase__      :	Dict						=model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(a_	,				nn.Linear			)			)





							def  UpperCAmelCase__				(    self    :Any			):





										"""simple docstring"""

										lowerCamelCase__      :	Optional[int]						=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__      :	Optional[Any]						=model_class(a_			)
													lowerCamelCase__      :	Optional[int]						=inspect.signature(model.forward			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase__      :	Tuple						=[*signature.parameters.keys()]

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





							def  UpperCAmelCase__				(    self    :Optional[int]			):





										"""simple docstring"""

										lowerCamelCase__      :	Dict						=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a_			)





							def  UpperCAmelCase__				(    self    :int			):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__      :	int						=True

										# in YOLOS, the seq_len is different
										lowerCamelCase__      :	int						=self.model_tester.expected_seq_len
										for model_class in self.all_model_classes:
													lowerCamelCase__      :	List[str]						=True
													lowerCamelCase__      :	Optional[Any]						=False
													lowerCamelCase__      :	Optional[Any]						=True
													lowerCamelCase__      :	List[Any]						=model_class(a_			)
													model.to(a_			)
													model.eval()
													with torch.no_grad():
																lowerCamelCase__      :	str						=model(**self._prepare_for_class(a_	,				a_			)			)
													lowerCamelCase__      :	List[Any]						=outputs.attentions
													self.assertEqual(len(a_			)	,				self.model_tester.num_hidden_layers			)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase__      :	Optional[Any]						=True
													lowerCamelCase__      :	Optional[int]						=model_class(a_			)
													model.to(a_			)
													model.eval()
													with torch.no_grad():
																lowerCamelCase__      :	Optional[Any]						=model(**self._prepare_for_class(a_	,				a_			)			)
													lowerCamelCase__      :	List[str]						=outputs.attentions
													self.assertEqual(len(a_			)	,				self.model_tester.num_hidden_layers			)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]			)	,				[self.model_tester.num_attention_heads, seq_len, seq_len]	,				)
													lowerCamelCase__      :	Optional[int]						=len(a_			)

													# Check attention is always last and order is fine
													lowerCamelCase__      :	str						=True
													lowerCamelCase__      :	List[Any]						=True
													lowerCamelCase__      :	Any						=model_class(a_			)
													model.to(a_			)
													model.eval()
													with torch.no_grad():
																lowerCamelCase__      :	str						=model(**self._prepare_for_class(a_	,				a_			)			)

													lowerCamelCase__      :	Dict						=1
													self.assertEqual(out_len + added_hidden_states	,				len(a_			)			)

													lowerCamelCase__      :	Optional[int]						=outputs.attentions

													self.assertEqual(len(a_			)	,				self.model_tester.num_hidden_layers			)
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]			)	,				[self.model_tester.num_attention_heads, seq_len, seq_len]	,				)





							def  UpperCAmelCase__				(    self    :List[str]			):





										"""simple docstring"""

										def check_hidden_states_output(lowerCamelCase_    :List[Any]	,				lowerCamelCase_    :List[Any]	,				lowerCamelCase_    :Dict			):
													lowerCamelCase__      :	int						=model_class(a_			)
													model.to(a_			)
													model.eval()

													with torch.no_grad():
																lowerCamelCase__      :	Optional[Any]						=model(**self._prepare_for_class(a_	,				a_			)			)

													lowerCamelCase__      :	Tuple						=outputs.hidden_states

													lowerCamelCase__      :	str						=getattr(
													    self.model_tester	,				'expected_num_hidden_layers'	,				self.model_tester.num_hidden_layers + 1			)
													self.assertEqual(len(a_			)	,				a_			)

													# YOLOS has a different seq_length
													lowerCamelCase__      :	str						=self.model_tester.expected_seq_len

													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]			)	,				[seq_length, self.model_tester.hidden_size]	,				)

										lowerCamelCase__      :	List[str]						=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__      :	Any						=True
													check_hidden_states_output(a_	,				a_	,				a_			)

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

													check_hidden_states_output(a_	,				a_	,				a_			)





							def  UpperCAmelCase__				(    self    :Optional[Any]			):





										"""simple docstring"""

										lowerCamelCase__      :	Optional[int]						=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*a_			)





							@slow
							def  UpperCAmelCase__				(    self    :List[Any]			):





										"""simple docstring"""

										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase__      :	Optional[Any]						=YolosModel.from_pretrained(a_			)
													self.assertIsNotNone(a_			)






def   lowerCAmelCase_  (						)  ->str:
			lowerCamelCase__      :	Any						=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
			return image

@require_torch
@require_vision
class        A_      (	unittest.TestCase  ):



							"""simple docstring"""






							@cached_property
							def  UpperCAmelCase__				(    self    :Optional[Any]			):





										"""simple docstring"""

										return AutoImageProcessor.from_pretrained('hustvl/yolos-small'			) if is_vision_available() else None





							@slow
							def  UpperCAmelCase__				(    self    :str			):





										"""simple docstring"""

										lowerCamelCase__      :	str						=YolosForObjectDetection.from_pretrained('hustvl/yolos-small'			).to(a_			)

										lowerCamelCase__      :	str						=self.default_image_processor
										lowerCamelCase__      :	Union[str, Any]						=prepare_img()
										lowerCamelCase__      :	Dict						=image_processor(images=a_	,				return_tensors='pt'			).to(a_			)

										# forward pass
										with torch.no_grad():
													lowerCamelCase__      :	List[str]						=model(inputs.pixel_values			)

										# verify outputs
										lowerCamelCase__      :	Tuple						=torch.Size((1, 100, 92)			)
										self.assertEqual(outputs.logits.shape	,				a_			)

										lowerCamelCase__      :	Optional[int]						=torch.tensor(
										    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]	,				device=a_	,				)
										lowerCamelCase__      :	Dict						=torch.tensor(
										    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]	,				device=a_			)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,				a_	,				atol=1e-4			)			)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,				a_	,				atol=1e-4			)			)

										# verify postprocessing
										lowerCamelCase__      :	Optional[int]						=image_processor.post_process_object_detection(
										    a_	,				threshold=0.3	,				target_sizes=[image.size[::-1]]			)[0]
										lowerCamelCase__      :	Dict						=torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]			).to(a_			)
										lowerCamelCase__      :	Any						=[75, 75, 17, 63, 17]
										lowerCamelCase__      :	Optional[Any]						=torch.tensor([335.0_609, 79.38_48, 375.4_216, 187.2_495]			).to(a_			)

										self.assertEqual(len(results['scores']			)	,				5			)
										self.assertTrue(torch.allclose(results['scores']	,				a_	,				atol=1e-4			)			)
										self.assertSequenceEqual(results['labels'].tolist()	,				a_			)
										self.assertTrue(torch.allclose(results['boxes'][0, :]	,				a_			)			)



'''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)


