
"""simple docstring"""

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   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__="resnet50"   ,		lowercase__=3   ,		lowercase__=32   ,		lowercase__=3   ,		lowercase__=True   ,		lowercase__=True   ,		):
											snake_case_					:		Union[str, Any] 					=					parent
											snake_case_					:		Optional[Any] 					=					out_indices if out_indices is not None else [4]
											snake_case_					:		int 					=					stage_names
											snake_case_					:		str 					=					out_features
											snake_case_					:		Optional[Any] 					=					backbone
											snake_case_					:		Tuple 					=					batch_size
											snake_case_					:		Union[str, Any] 					=					image_size
											snake_case_					:		List[str] 					=					num_channels
											snake_case_					:		str 					=					use_pretrained_backbone
											snake_case_					:		List[str] 					=					is_training







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
											snake_case_					:		List[str] 					=					self.get_config()

											return config, pixel_values







						def 						__UpperCamelCase							(self       ):
											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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					TimmBackbone(config=lowercase__       )
											model.to(lowercase__       )
											model.eval()
											with torch.no_grad():
																snake_case_					:		str 					=					model(lowercase__       )
											self.parent.assertEqual(
											    result.feature_map[-1].shape   ,		(self.batch_size, model.channels[-1], 14, 14)   ,		)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.prepare_config_and_inputs()
											snake_case_					:		str 					=					config_and_inputs
											snake_case_					:		str 					=					{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_torch
@require_timm
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					TimmBackboneModelTester(self       )
											snake_case_					:		int 					=					ConfigTester(self   ,		config_class=lowercase__   ,		has_text_modality=lowercase__       )







						def 						__UpperCamelCase							(self       ):
											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 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					"""resnet18"""
											snake_case_					:		Union[str, Any] 					=					"""microsoft/resnet-18"""

											snake_case_					:		Optional[Any] 					=					AutoBackbone.from_pretrained(lowercase__   ,		use_timm_backbone=lowercase__       )
											snake_case_					:		Optional[Any] 					=					AutoBackbone.from_pretrained(lowercase__       )

											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]       )

											snake_case_					:		Tuple 					=					AutoBackbone.from_pretrained(lowercase__   ,		use_timm_backbone=lowercase__   ,		out_indices=[1, 2, 3]       )
											snake_case_					:		List[Any] 					=					AutoBackbone.from_pretrained(lowercase__   ,		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 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone initialization is managed on the timm side"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""model weights aren't tied in TimmBackbone."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""model weights aren't tied in TimmBackbone."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't support output_attentions."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Safetensors is not supported by timm."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case_					:		Dict 					=					["""pixel_values"""]
																self.assertListEqual(arg_names[:1]   ,		lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:		Union[str, Any] 					=					True
											snake_case_					:		List[str] 					=					self.has_attentions

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

											snake_case_					:		Optional[Any] 					=					self._prepare_for_class(lowercase__   ,		lowercase__       )
											snake_case_					:		List[Any] 					=					model(**lowercase__       )
											snake_case_					:		str 					=					outputs[0][-1]

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

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

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

											self.assertIsNotNone(hidden_states.grad       )

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																snake_case_					:		Any 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		Tuple 					=					model(**lowercase__       )

																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
																snake_case_					:		str 					=					copy.deepcopy(lowercase__       )
																snake_case_					:		List[str] 					=					None
																snake_case_					:		Tuple 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		str 					=					model(**lowercase__       )

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

																# Check backbone can be initialized with fresh weights
																snake_case_					:		Dict 					=					copy.deepcopy(lowercase__       )
																snake_case_					:		Any 					=					False
																snake_case_					:		List[str] 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		Any 					=					model(**lowercase__       )









"""simple docstring"""

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_			   =					logging.getLogger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=-1       ):
											# in NER datasets, the last column is usually reserved for NER label
											snake_case_					:		Union[str, Any] 					=					label_idx







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[str] 					=					mode.value
											snake_case_					:		List[Any] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		Any 					=					[]
											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																snake_case_					:		str 					=					[]
																snake_case_					:		List[Any] 					=					[]
																for line in f:
																					if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																										if words:
																															examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																															guid_index += 1
																															snake_case_					:		Optional[Any] 					=					[]
																															snake_case_					:		int 					=					[]
																					else:
																										snake_case_					:		Optional[Any] 					=					line.split(""" """       )
																										words.append(splits[0]       )
																										if len(lowercase__       ) > 1:
																															labels.append(splits[self.label_idx].replace("""\n"""   ,		""""""       )       )
																										else:
																															# Examples could have no label for mode = "test"
																															labels.append("""O"""       )
																if words:
																					examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					0
											for line in test_input_reader:
																if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
																					writer.write(lowercase__       )
																					if not preds_list[example_id]:
																										example_id += 1
																elif preds_list[example_id]:
																					snake_case_					:		Optional[int] 					=					line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
																					writer.write(lowercase__       )
																else:
																					logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""   ,		line.split()[0]       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Dict 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		List[Any] 					=					["""O"""] + labels
																return labels
											else:
																return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self       ):
											# in CONLL2003 dataset chunk column is second-to-last
											super().__init__(label_idx=-2       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					snake_case_					:		Any 					=					f.read().splitlines()
																if "O" not in labels:
																					snake_case_					:		Tuple 					=					["""O"""] + labels
																return labels
											else:
																return [
																    "O",
																    "B-ADVP",
																    "B-INTJ",
																    "B-LST",
																    "B-PRT",
																    "B-NP",
																    "B-SBAR",
																    "B-VP",
																    "B-ADJP",
																    "B-CONJP",
																    "B-PP",
																    "I-ADVP",
																    "I-INTJ",
																    "I-LST",
																    "I-PRT",
																    "I-NP",
																    "I-SBAR",
																    "I-VP",
																    "I-ADJP",
																    "I-CONJP",
																    "I-PP",
																]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		List[Any] 					=					mode.value
											snake_case_					:		Optional[int] 					=					os.path.join(lowercase__   ,		f'{mode}.txt'       )
											snake_case_					:		Tuple 					=					1
											snake_case_					:		str 					=					[]

											with open(lowercase__   ,		encoding="""utf-8"""       ) as f:
																for sentence in parse_incr(lowercase__       ):
																					snake_case_					:		Tuple 					=					[]
																					snake_case_					:		Any 					=					[]
																					for token in sentence:
																										words.append(token["""form"""]       )
																										labels.append(token["""upos"""]       )
																					assert len(lowercase__       ) == len(lowercase__       )
																					if words:
																										examples.append(InputExample(guid=f'{mode}-{guid_index}'   ,		words=lowercase__   ,		labels=lowercase__       )       )
																										guid_index += 1
											return examples







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Dict 					=					0
											for sentence in parse_incr(lowercase__       ):
																snake_case_					:		int 					=					preds_list[example_id]
																snake_case_					:		Dict 					=					""""""
																for token in sentence:
																					out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
																out += "\n"
																writer.write(lowercase__       )
																example_id += 1







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if path:
																with open(lowercase__   ,		"""r"""       ) as f:
																					return f.read().splitlines()
											else:
																return [
																    "ADJ",
																    "ADP",
																    "ADV",
																    "AUX",
																    "CCONJ",
																    "DET",
																    "INTJ",
																    "NOUN",
																    "NUM",
																    "PART",
																    "PRON",
																    "PROPN",
																    "PUNCT",
																    "SCONJ",
																    "SYM",
																    "VERB",
																    "X",
																]




