
"""simple docstring"""





import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_a		       =							datasets.logging.get_logger(__name__)


_a		       =							'\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

_a		       =							'\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

_a		       =							'\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=False,		__lowerCamelCase=False,		__lowerCamelCase=True,		__lowerCamelCase=False,		__lowerCamelCase="dummy_doc"  ):
   UpperCAmelCase_					:					Tuple			     =    {doc: key_lines}
   UpperCAmelCase_					:					Any			     =    {doc: sys_lines}

   UpperCAmelCase_					:					Optional[Any]			     =    {}

   UpperCAmelCase_					:					str			     =    0
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					List[Any]			     =    0
   UpperCAmelCase_					:					Optional[Any]			     =    0
   UpperCAmelCase_					:					Optional[Any]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    reader.get_doc_mentions(__lowerCamelCase,		key_doc_lines[doc],		__lowerCamelCase  )
   key_singletons_num += singletons_num

   if NP_only or min_span:
      UpperCAmelCase_					:					int			     =    reader.set_annotated_parse_trees(__lowerCamelCase,		key_doc_lines[doc],		__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    reader.get_doc_mentions(__lowerCamelCase,		sys_doc_lines[doc],		__lowerCamelCase  )
   sys_singletons_num += singletons_num

   if NP_only or min_span:
      UpperCAmelCase_					:					int			     =    reader.set_annotated_parse_trees(__lowerCamelCase,		key_doc_lines[doc],		__lowerCamelCase,		__lowerCamelCase  )

   if remove_nested:
      UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    reader.remove_nested_coref_mentions(__lowerCamelCase,		__lowerCamelCase  )
      key_nested_coref_num += nested_mentions
      key_removed_nested_clusters += removed_clusters

      UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    reader.remove_nested_coref_mentions(__lowerCamelCase,		__lowerCamelCase  )
      sys_nested_coref_num += nested_mentions
      sys_removed_nested_clusters += removed_clusters

   UpperCAmelCase_					:					List[Any]			     =    reader.get_mention_assignments(__lowerCamelCase,		__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    reader.get_mention_assignments(__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					Union[str, Any]			     =    (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

   if remove_nested:
      logger.info(
          "Number of removed nested coreferring mentions in the key "
          f"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""  )
      logger.info(
          "Number of resulting singleton clusters in the key "
          f"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""  )

   if not keep_singletons:
      logger.info(
          f"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
          "files, respectively"  )

   return doc_coref_infos







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    get_coref_infos(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    {}
   UpperCAmelCase_					:					List[Any]			     =    0
   UpperCAmelCase_					:					int			     =    0

   for name, metric in metrics:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    evaluator.evaluate_documents(__lowerCamelCase,		__lowerCamelCase,		beta=1  )
      if name in ["muc", "bcub", "ceafe"]:
         conll += fa
         conll_subparts_num += 1
      output_scores.update({f"""{name}/recall""": recall, f"""{name}/precision""": precision, f"""{name}/f1""": fa}  )

      logger.info(
          name.ljust(10  ),		f"""Recall: {recall * 100:.2f}""",		f""" Precision: {precision * 100:.2f}""",		f""" F1: {fa * 100:.2f}""",		)

   if conll_subparts_num == 3:
      UpperCAmelCase_					:					Optional[int]			     =    (conll / 3) * 100
      logger.info(f"""CoNLL score: {conll:.2f}"""  )
      output_scores.update({"conll_score": conll}  )

   return output_scores







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    False
   for line in key_lines:
      if not line.startswith("#"  ):
         if len(line.split()  ) > 6:
            UpperCAmelCase_					:					List[Any]			     =    line.split()[5]
            if not parse_col == "-":
               UpperCAmelCase_					:					Any			     =    True
               break
            else:
               break
   return has_gold_parse


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




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return datasets.MetricInfo(
         description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
             {
                 "predictions": datasets.Sequence(datasets.Value("string"  )  ),
                 "references": datasets.Sequence(datasets.Value("string"  )  ),
             }  )				,   codebase_urls=["https://github.com/ns-moosavi/coval"]				,   reference_urls=[
             "https://github.com/ns-moosavi/coval",
             "https://www.aclweb.org/anthology/P16-1060",
             "http://www.conll.cemantix.org/2012/data.html",
         ]				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_=True				,   lowercase_=False				,   lowercase_=False				,   lowercase_=False  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    [
         ("mentions", evaluator.mentions),
         ("muc", evaluator.muc),
         ("bcub", evaluator.b_cubed),
         ("ceafe", evaluator.ceafe),
         ("lea", evaluator.lea),
     ]

     if min_span:
        UpperCAmelCase_					:					int			     =    util.check_gold_parse_annotation(lowercase_  )
        if not has_gold_parse:
           raise NotImplementedError("References should have gold parse annotation to use 'min_span'."  )
           # util.parse_key_file(key_file)
           # key_file = key_file + ".parsed"

     UpperCAmelCase_					:					str			     =    evaluate(
         key_lines=lowercase_				,   sys_lines=lowercase_				,   metrics=lowercase_				,   NP_only=lowercase_				,   remove_nested=lowercase_				,   keep_singletons=lowercase_				,   min_span=lowercase_				,   )

     return score




"""simple docstring"""





import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        DeiTForImageClassification,
        DeiTForImageClassificationWithTeacher,
        DeiTForMaskedImageModeling,
        DeiTModel,
    )
    from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor


class A_	:




  '''simple docstring'''







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

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    parent
     UpperCAmelCase_					:					int			     =    batch_size
     UpperCAmelCase_					:					int			     =    image_size
     UpperCAmelCase_					:					List[Any]			     =    patch_size
     UpperCAmelCase_					:					Any			     =    num_channels
     UpperCAmelCase_					:					Optional[int]			     =    is_training
     UpperCAmelCase_					:					Union[str, Any]			     =    use_labels
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_size
     UpperCAmelCase_					:					str			     =    num_hidden_layers
     UpperCAmelCase_					:					List[str]			     =    num_attention_heads
     UpperCAmelCase_					:					str			     =    intermediate_size
     UpperCAmelCase_					:					str			     =    hidden_act
     UpperCAmelCase_					:					List[Any]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					str			     =    type_sequence_label_size
     UpperCAmelCase_					:					str			     =    initializer_range
     UpperCAmelCase_					:					Union[str, Any]			     =    scope
     UpperCAmelCase_					:					str			     =    encoder_stride

     # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
     UpperCAmelCase_					:					int			     =    (image_size // patch_size) ** 2
     UpperCAmelCase_					:					Optional[Any]			     =    num_patches + 2







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					Tuple			     =    None
     if self.use_labels:
        UpperCAmelCase_					:					Any			     =    ids_tensor([self.batch_size]				,   self.type_sequence_label_size  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_config()

     return config, pixel_values, labels







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return DeiTConfig(
         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=lowercase_				,   initializer_range=self.initializer_range				,   encoder_stride=self.encoder_stride				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    DeiTModel(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    DeiTForMaskedImageModeling(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )
     self.parent.assertEqual(
         result.reconstruction.shape				,   (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

     # test greyscale images
     UpperCAmelCase_					:					List[str]			     =    1
     UpperCAmelCase_					:					Optional[Any]			     =    DeiTForMaskedImageModeling(lowercase_  )
     model.to(lowercase_  )
     model.eval()

     UpperCAmelCase_					:					Dict			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_  )
     self.parent.assertEqual(result.reconstruction.shape				,   (self.batch_size, 1, self.image_size, self.image_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.type_sequence_label_size
     UpperCAmelCase_					:					Union[str, Any]			     =    DeiTForImageClassification(lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[str]			     =    model(lowercase_				,   labels=lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)  )

     # test greyscale images
     UpperCAmelCase_					:					Union[str, Any]			     =    1
     UpperCAmelCase_					:					Optional[int]			     =    DeiTForImageClassification(lowercase_  )
     model.to(lowercase_  )
     model.eval()

     UpperCAmelCase_					:					Dict			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_				,   labels=lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Dict			     =    config_and_inputs
     UpperCAmelCase_					:					Optional[int]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(
      {
          """feature-extraction""": DeiTModel,
          """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__			:						List[Any]       =		False
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		False
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    DeiTModelTester(self  )
     UpperCAmelCase_					:					Optional[int]			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

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







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

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					List[Any]			     =    model_class(lowercase_  )
        self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)  )
        UpperCAmelCase_					:					Any			     =    model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowercase_				,   nn.Linear  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_					:					Optional[int]			     =    ["pixel_values"]
        self.assertListEqual(arg_names[:1]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_=False  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    super()._prepare_for_class(lowercase_				,   lowercase_				,   return_labels=lowercase_  )

     if return_labels:
        if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
           del inputs_dict["labels"]

     return inputs_dict







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not self.model_tester.is_training:
        return

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_					:					Union[str, Any]			     =    True

     for model_class in self.all_model_classes:
        # DeiTForImageClassificationWithTeacher supports inference-only
        if (
            model_class in get_values(lowercase_  )
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
           continue
        UpperCAmelCase_					:					Optional[int]			     =    model_class(lowercase_  )
        model.to(lowercase_  )
        model.train()
        UpperCAmelCase_					:					List[Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_				,   return_labels=lowercase_  )
        UpperCAmelCase_					:					Dict			     =    model(**lowercase_  ).loss
        loss.backward()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
        return

     UpperCAmelCase_					:					Dict			     =    False
     UpperCAmelCase_					:					Optional[int]			     =    True

     for model_class in self.all_model_classes:
        if model_class in get_values(lowercase_  ) or not model_class.supports_gradient_checkpointing:
           continue
        # DeiTForImageClassificationWithTeacher supports inference-only
        if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
           continue
        UpperCAmelCase_					:					List[str]			     =    model_class(lowercase_  )
        model.gradient_checkpointing_enable()
        model.to(lowercase_  )
        model.train()
        UpperCAmelCase_					:					Optional[int]			     =    self._prepare_for_class(lowercase_				,   lowercase_				,   return_labels=lowercase_  )
        UpperCAmelCase_					:					Any			     =    model(**lowercase_  ).loss
        loss.backward()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase_					:					Dict			     =    [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]

     for model_class in self.all_model_classes:
        if (
            model_class
            not in [
                *get_values(lowercase_  ),
                *get_values(lowercase_  ),
            ]
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
           continue

        for problem_type in problem_types:
           with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""  ):
              UpperCAmelCase_					:					str			     =    problem_type["title"]
              UpperCAmelCase_					:					List[Any]			     =    problem_type["num_labels"]

              UpperCAmelCase_					:					Union[str, Any]			     =    model_class(lowercase_  )
              model.to(lowercase_  )
              model.train()

              UpperCAmelCase_					:					int			     =    self._prepare_for_class(lowercase_				,   lowercase_				,   return_labels=lowercase_  )

              if problem_type["num_labels"] > 1:
                 UpperCAmelCase_					:					List[Any]			     =    inputs["labels"].unsqueeze(1  ).repeat(1				,   problem_type["num_labels"]  )

              UpperCAmelCase_					:					Tuple			     =    inputs["labels"].to(problem_type["dtype"]  )

              # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
              # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
              # they have the same size." which is a symptom something in wrong for the regression problem.
              # See https://github.com/huggingface/transformers/issues/11780
              with warnings.catch_warnings(record=lowercase_  ) as warning_list:
                 UpperCAmelCase_					:					List[str]			     =    model(**lowercase_  ).loss
              for w in warning_list:
                 if "Using a target size that is different to the input size" in str(w.message  ):
                    raise ValueError(
                        F"""Something is going wrong in the regression problem: intercepted {w.message}"""  )

              loss.backward()







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_					:					Union[str, Any]			     =    DeiTModel.from_pretrained(lowercase_  )
        self.assertIsNotNone(lowercase_  )







def 	__a     (		):
   UpperCAmelCase_					:					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  ):

     """simple docstring"""
     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"  )
         if is_vision_available()
         else None
     )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"  ).to(
         lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    self.default_image_processor
     UpperCAmelCase_					:					List[str]			     =    prepare_img()
     UpperCAmelCase_					:					int			     =    image_processor(images=lowercase_				,   return_tensors="pt"  ).to(lowercase_  )

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_					:					Dict			     =    model(**lowercase_  )

     # verify the logits
     UpperCAmelCase_					:					List[str]			     =    torch.Size((1, 1000)  )
     self.assertEqual(outputs.logits.shape				,   lowercase_  )

     UpperCAmelCase_					:					str			     =    torch.tensor([-1.02_66, 0.19_12, -1.28_61]  ).to(lowercase_  )

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







  @slow
  @require_accelerate
  @require_torch_gpu
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    DeiTModel.from_pretrained(
         "facebook/deit-base-distilled-patch16-224"				,   torch_dtype=torch.floataa				,   device_map="auto"  )
     UpperCAmelCase_					:					str			     =    self.default_image_processor

     UpperCAmelCase_					:					Union[str, Any]			     =    prepare_img()
     UpperCAmelCase_					:					List[Any]			     =    image_processor(images=lowercase_				,   return_tensors="pt"  )
     UpperCAmelCase_					:					List[str]			     =    inputs.pixel_values.to(lowercase_  )

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
        UpperCAmelCase_					:					int			     =    model(lowercase_  )


