





'''simple docstring'''







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

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


_lowercase       						=			logging.getLogger(__name__)
class 			_lowercase						(       UpperCAmelCase_					):







					def __init__( self    ,   A__=-1    ) ->	Tuple:
						# in NER datasets, the last column is usually reserved for NER label
						snake_case				=			label_idx







					def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[InputExample]:
						if isinstance(_lowercase    ,   _lowercase    ):
							snake_case				=			mode.value
						snake_case				=			os.path.join(_lowercase    ,   F"""{mode}.txt"""    )
						snake_case				=			1
						snake_case				=			[]
						with open(_lowercase    ,   encoding='''utf-8'''    ) as f:
							snake_case				=			[]
							snake_case				=			[]
							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				=			[]
										snake_case				=			[]
								else:
									snake_case				=			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    ,   A__    ,   A__    ,   A__    ) ->	List[str]:
						snake_case				=			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				=			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    ,   A__    ) ->	List[str]:
						if path:
							with open(_lowercase    ,   '''r'''    ) as f:
								snake_case				=			f.read().splitlines()
							if "O" not in labels:
								snake_case				=			['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_					):







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







					def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
						if path:
							with open(_lowercase    ,   '''r'''    ) as f:
								snake_case				=			f.read().splitlines()
							if "O" not in labels:
								snake_case				=			['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_					):







					def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[InputExample]:
						if isinstance(_lowercase    ,   _lowercase    ):
							snake_case				=			mode.value
						snake_case				=			os.path.join(_lowercase    ,   F"""{mode}.txt"""    )
						snake_case				=			1
						snake_case				=			[]

						with open(_lowercase    ,   encoding='''utf-8'''    ) as f:
							for sentence in parse_incr(_lowercase    ):
								snake_case				=			[]
								snake_case				=			[]
								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    ,   A__    ,   A__    ,   A__    ) ->	Optional[int]:
						snake_case				=			0
						for sentence in parse_incr(_lowercase    ):
							snake_case				=			preds_list[example_id]
							snake_case				=			''
							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    ,   A__    ) ->	List[str]:
						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",
							]








'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=30    ,   A__=2    ,   A__=3    ,   A__=True    ,   A__=True    ,   A__=32    ,   A__=2    ,   A__=4    ,   A__=37    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=10    ,   A__=0.0_2    ,   A__=3    ,   A__=None    ,   ) ->	List[Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			image_size
      snake_case				=			patch_size
      snake_case				=			num_channels
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			scope

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







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

      snake_case				=			self.get_config()

      return config, pixel_values, labels







     def     UpperCamelCase			( self    ) ->	int:
      return ViTConfig(
          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    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			TFViTModel(config=A__    )
      snake_case				=			model(A__    ,   training=A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      snake_case				=			(image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, seq_length, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[int]:
      snake_case				=			self.type_sequence_label_size
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			model(A__    ,   labels=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.prepare_config_and_inputs()
      snake_case				,		snake_case				,		snake_case				=			config_and_inputs
      snake_case				=			{'''pixel_values''': pixel_values}
      return config, inputs_dict




@require_tf
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _UpperCAmelCase										=					(
         {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			TFViTModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   has_text_modality=A__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	int:
      self.config_tester.run_common_tests()







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	int:
      pass







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	str:
      pass







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

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)    )
       snake_case				=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(A__    ,   tf.keras.layers.Layer    )    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

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

       snake_case				=			['''pixel_values''']
       self.assertListEqual(arg_names[:1]    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			TFViTModel.from_pretrained('''google/vit-base-patch16-224'''    )
      self.assertIsNotNone(A__    )




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






@require_tf
@require_vision
class 			_lowercase						(       unittest.TestCase					):







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''    ) if is_vision_available() else None







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''    )

      snake_case				=			self.default_image_processor
      snake_case				=			prepare_img()
      snake_case				=			image_processor(images=A__    ,   return_tensors='''tf'''    )

      # forward pass
      snake_case				=			model(**A__    )

      # verify the logits
      snake_case				=			tf.TensorShape((1, 10_00)    )
      self.assertEqual(outputs.logits.shape    ,   A__    )

      snake_case				=			tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]    )

      tf.debugging.assert_near(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )

