




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

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


_lowerCamelCase	:		Any							       =   logging.getLogger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	int					, UpperCAmelCase__      :	Optional[Any]=-1)						->Tuple:





           '''simple docstring'''

           A__       =    label_idx


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Union[Split, str])						->List[InputExample]:





           '''simple docstring'''

           if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                  A__       =    mode.value
           A__       =    os.path.join(UpperCAmelCase__					, f"""{mode}.txt""")
           A__       =    1
           A__       =    []
           with open(UpperCAmelCase__					, encoding='''utf-8''') as f:
                  A__       =    []
                  A__       =    []
                  for line in f:
                         if line.startswith('''-DOCSTART-''') or line == "" or line == "\n":
                                if words:
                                       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""					, words=UpperCAmelCase__					, labels=UpperCAmelCase__))
                                       guid_index += 1
                                       A__       =    []
                                       A__       =    []
                         else:
                                A__       =    line.split(''' ''')
                                words.append(splits[0])
                                if len(UpperCAmelCase__) > 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=UpperCAmelCase__					, labels=UpperCAmelCase__))
           return examples


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	TextIO					, UpperCAmelCase__      :	TextIO					, UpperCAmelCase__      :	List)						->Optional[Any]:





           '''simple docstring'''

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	str)						->List[str]:





           '''simple docstring'''

           if path:
                  with open(UpperCAmelCase__					, '''r''') as f:
                         A__       =    f.read().splitlines()
                  if "O" not in labels:
                         A__       =    ['''O'''] + labels
                  return labels
           else:
                  return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           super().__init__(label_idx=-2)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, UpperCAmelCase__      :	str)						->List[str]:





           '''simple docstring'''

           if path:
                  with open(UpperCAmelCase__					, '''r''') as f:
                         A__       =    f.read().splitlines()
                  if "O" not in labels:
                         A__       =    ['''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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Union[Split, str])						->List[InputExample]:





           '''simple docstring'''

           if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                  A__       =    mode.value
           A__       =    os.path.join(UpperCAmelCase__					, f"""{mode}.txt""")
           A__       =    1
           A__       =    []

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	TextIO					, UpperCAmelCase__      :	TextIO					, UpperCAmelCase__      :	List)						->Dict:





           '''simple docstring'''

           A__       =    0
           for sentence in parse_incr(UpperCAmelCase__):
                  A__       =    preds_list[example_id]
                  A__       =    ''''''
                  for token in sentence:
                         out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0)}) """
                  out += "\n"
                  writer.write(UpperCAmelCase__)
                  example_id += 1





    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	str)						->List[str]:





           '''simple docstring'''

           if path:
                  with open(UpperCAmelCase__					, '''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",
                  ]








"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ViTMSNForImageClassification, ViTMSNModel
					from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=30		, _a=2		, _a=3		, _a=True		, _a=True		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=10		, _a=0.02		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					image_size
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					scope

							# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCamelCase			      =					(image_size // patch_size) ** 2
							lowerCamelCase			      =					num_patches + 1





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							lowerCamelCase			      =					None
							if self.use_labels:
										lowerCamelCase			      =					ids_tensor([self.batch_size]		, self.type_sequence_label_size )

							lowerCamelCase			      =					self.get_config()

							return config, pixel_values, labels





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTMSNConfig(
							    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		, initializer_range=self.initializer_range		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.type_sequence_label_size
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, labels=_a )
							print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}""" )
							print("""Labels: {labels}""" )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							lowerCamelCase			      =					1
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

							lowerCamelCase			      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size) )





				def    _lowerCAmelCase				(       self ):





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






@require_torch
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__UpperCamelCase			  =						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, has_text_modality=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				@unittest.skip(reason="""ViTMSN does not use inputs_embeds""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCamelCase			      =					model_class(_a )
										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]		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					ViTMSNModel.from_pretrained(_a )
										self.assertIsNotNone(_a )




def     a__ (				)						->					Any:
			lowerCamelCase			      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
			return image






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






				'''simple docstring'''





				@cached_property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small""" ) if is_vision_available() else None





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							torch.manual_seed(2 )
							lowerCamelCase			      =					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )

							lowerCamelCase			      =					self.default_image_processor
							lowerCamelCase			      =					prepare_img()
							lowerCamelCase			      =					image_processor(images=_a		, return_tensors="""pt""" ).to(_a )

							# forward pass
							with torch.no_grad():
										lowerCamelCase			      =					model(**_a )

							# verify the logits
							lowerCamelCase			      =					torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape		, _a )

							lowerCamelCase			      =					torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(_a )

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



