




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
	from PIL import Image

	from transformers import ConvNextImageProcessor

class 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		Tuple,     _lowercase:		int=13,     _lowercase:		Dict=32,     _lowercase:		Any=3,     _lowercase:		int=4,     _lowercase:		Optional[Any]=[10, 20, 30, 40],     _lowercase:		Any=[2, 2, 3, 2],     _lowercase:		Optional[int]=True,     _lowercase:		List[Any]=True,     _lowercase:		List[Any]=37,     _lowercase:		List[str]="gelu",     _lowercase:		List[str]=10,     _lowercase:		Any=0.02,     _lowercase:		int=3,     _lowercase:		List[str]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  num_stages
									__lowerCAmelCase					=  hidden_sizes
									__lowerCAmelCase					=  depths
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  type_sequence_label_size
									__lowerCAmelCase					=  initializer_range
									__lowerCAmelCase					=  scope




					def     _lowercase						(   self:		Optional[Any]):


									'''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     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextConfig(
									    num_channels=self.num_channels,     hidden_sizes=self.hidden_sizes,     depths=self.depths,     num_stages=self.num_stages,     hidden_act=self.hidden_act,     is_decoder=_lowercase,     initializer_range=self.initializer_range,     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel(config=_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     training=_lowercase)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.type_sequence_label_size
									__lowerCAmelCase					=  TFConvNextForImageClassification(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     labels=_lowercase,     training=_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size))







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


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





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					__UpperCamelCase     	= (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

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




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37,     )




					@unittest.skip(reason="""ConvNext does not use inputs_embeds""")
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									pass




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					@slow
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									super().test_keras_fit()




					@unittest.skip(reason="""ConvNext does not support input and output embeddings""")
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_dataset_conversion()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Any):
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))
													__lowerCAmelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__lowerCAmelCase					=  self.model_tester.num_stages
													self.assertEqual(len(_lowercase),     expected_num_stages + 1)

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]),     [self.model_tester.image_size // 4, self.model_tester.image_size // 4],     )

									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__lowerCAmelCase					=  True

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(_lowercase:		Tuple,     _lowercase:		Tuple,     _lowercase:		str,     _lowercase:		Dict={}):
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase)
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase).to_tuple()

													def recursive_check(_lowercase:		Union[str, Any],     _lowercase:		List[str]):
																	if isinstance(_lowercase,     (List, Tuple)):
																					for tuple_iterable_value, dict_iterable_value in zip(_lowercase,     _lowercase):
																									recursive_check(_lowercase,     _lowercase)
																	elif tuple_object is None:
																					return
																	else:
																					self.assertTrue(
																					    all(tf.equal(_lowercase,     _lowercase)),     msg=(
																					        """Tuple and dict output are not equal. Difference:"""
																					        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}'''
																					    ),     )

																	recursive_check(_lowercase,     _lowercase)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224""")
									self.assertIsNotNone(_lowercase)





def      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


				__lowerCAmelCase					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image

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




					@cached_property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224""") if is_vision_available() else None







					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""")

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""tf""")

									# forward pass
									__lowerCAmelCase					=  model(**_lowercase)

									# verify the logits
									__lowerCAmelCase					=  tf.TensorShape((1, 1000))
									self.assertEqual(outputs.logits.shape,     _lowercase)

									__lowerCAmelCase					=  tf.constant([-0.0_260, -0.4_739, 0.1_911])

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









"""simple docstring"""






import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

SCREAMING_SNAKE_CASE__				:  str						= "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

SCREAMING_SNAKE_CASE__				:  Any						= "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION   )
class 	A_       (     datasets.Metric   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION  ,					citation=_CITATION  ,					homepage='https://github.com/chakki-works/seqeval'  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
             {
                 'predictions': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
                 'references': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
             }						)  ,					codebase_urls=['https://github.com/chakki-works/seqeval']  ,					reference_urls=['https://github.com/chakki-works/seqeval']  ,					)






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    classification_report(
         y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase  ,					suffix=__UpperCAmelCase  ,					output_dict=__UpperCAmelCase  ,					scheme=__UpperCAmelCase  ,					mode=__UpperCAmelCase  ,					sample_weight=__UpperCAmelCase  ,					zero_division=__UpperCAmelCase  ,					)
     report.pop('macro avg'						)
     report.pop('weighted avg'						)
     a   :	Optional[Any]						=    report.pop('micro avg'						)

     a   :	Optional[int]						=    {
         type_name: {
             'precision': score['precision'],
             'recall': score['recall'],
             'f1': score['f1-score'],
             'number': score['support'],
         }
         for type_name, score in report.items()
     }
     a   :	int						=    overall_score['precision']
     a   :	int						=    overall_score['recall']
     a   :	int						=    overall_score['f1-score']
     a   :	List[str]						=    accuracy_score(y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase						)

     return scores





