import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

_SCREAMING_SNAKE_CASE     =     """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

_SCREAMING_SNAKE_CASE     =     """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""







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




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

      a__  		=  {
          type_name: {
              "precision": score["precision"],
              "recall": score["recall"],
              "f1": score["f1-score"],
              "number": score["support"],
          }
          for type_name, score in report.items()
      }
      a__  		=  overall_score["precision"]
      a__  		=  overall_score["recall"]
      a__  		=  overall_score["f1-score"]
      a__  		=  accuracy_score(y_true=lowerCAmelCase   ,   y_pred=lowerCAmelCase )

      return scores


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
     from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DPTImageProcessor







class        UpperCAmelCase_					:




    def __init__(       self	: int   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Dict=2   ,   lowerCAmelCase	: Dict=32   ,   lowerCAmelCase	: Optional[int]=16   ,   lowerCAmelCase	: Tuple=3   ,   lowerCAmelCase	: Tuple=True   ,   lowerCAmelCase	: str=True   ,   lowerCAmelCase	: int=32   ,   lowerCAmelCase	: int=4   ,   lowerCAmelCase	: Tuple=[0, 1, 2, 3]   ,   lowerCAmelCase	: Dict=4   ,   lowerCAmelCase	: Optional[int]=37   ,   lowerCAmelCase	: Tuple="gelu"   ,   lowerCAmelCase	: Optional[int]=0.1   ,   lowerCAmelCase	: Dict=0.1   ,   lowerCAmelCase	: Tuple=0.02   ,   lowerCAmelCase	: List[str]=3   ,   lowerCAmelCase	: Dict=False   ,   lowerCAmelCase	: str=None   ,   )						->			Tuple:
      a__  		=  parent
      a__  		=  batch_size
      a__  		=  image_size
      a__  		=  patch_size
      a__  		=  num_channels
      a__  		=  is_training
      a__  		=  use_labels
      a__  		=  hidden_size
      a__  		=  num_hidden_layers
      a__  		=  backbone_out_indices
      a__  		=  num_attention_heads
      a__  		=  intermediate_size
      a__  		=  hidden_act
      a__  		=  hidden_dropout_prob
      a__  		=  attention_probs_dropout_prob
      a__  		=  initializer_range
      a__  		=  num_labels
      a__  		=  scope
      a__  		=  is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      a__  		=  (image_size // patch_size) ** 2
      a__  		=  num_patches + 1




    def     __lowerCamelCase			(       self	: List[Any] )						->			str:
      a__  		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      a__  		=  None
      if self.use_labels:
        a__  		=  ids_tensor([self.batch_size, self.image_size, self.image_size]   ,   self.num_labels )

      a__  		=  self.get_config()

      return config, pixel_values, labels




    def     __lowerCamelCase			(       self	: List[Any] )						->			Union[str, Any]:
      return DPTConfig(
          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   ,   backbone_out_indices=self.backbone_out_indices   ,   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=lowerCAmelCase   ,   initializer_range=self.initializer_range   ,   is_hybrid=self.is_hybrid   ,   )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Optional[Any]:
      a__  		=  DPTModel(config=lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size) )




    def     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: str )						->			Optional[Any]:
      a__  		=  self.num_labels
      a__  		=  DPTForDepthEstimation(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.predicted_depth.shape   ,   (self.batch_size, self.image_size, self.image_size) )




    def     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[int] )						->			List[str]:
      a__  		=  self.num_labels
      a__  		=  DPTForSemanticSegmentation(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase   ,   labels=lowerCAmelCase )
      self.parent.assertEqual(
          result.logits.shape   ,   (self.batch_size, self.num_labels, self.image_size, self.image_size) )




    def     __lowerCamelCase			(       self	: int )						->			Any:
      a__  		=  self.prepare_config_and_inputs()
      a__		,a__		,a__  		=  config_and_inputs
      a__  		=  {"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class        UpperCAmelCase_					(				_a  ,  _a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							int    			=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    UpperCAmelCase_      :							str    			=				(
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_      :							List[str]    			=				False
    UpperCAmelCase_      :							int    			=				False
    UpperCAmelCase_      :							Optional[Any]    			=				False




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  DPTModelTester(self )
      a__  		=  ConfigTester(self   ,   config_class=lowerCAmelCase   ,   has_text_modality=lowerCAmelCase   ,   hidden_size=37 )




    def     __lowerCamelCase			(       self	: int )						->			List[Any]:
      self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds" )
    def     __lowerCamelCase			(       self	: str )						->			Dict:
      pass




    def     __lowerCamelCase			(       self	: List[str] )						->			int:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()

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




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

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

        a__  		=  ["pixel_values"]
        self.assertListEqual(arg_names[:1]   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Optional[int] )						->			Optional[Any]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			List[str]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			Tuple:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()
        a__  		=  True

        if model_class in get_values(lowerCAmelCase ):
          continue

        a__  		=  model_class(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.train()
        a__  		=  self._prepare_for_class(lowerCAmelCase   ,   lowerCAmelCase   ,   return_labels=lowerCAmelCase )
        a__  		=  model(**lowerCAmelCase ).loss
        loss.backward()




    def     __lowerCamelCase			(       self	: Any )						->			Dict:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()
        a__  		=  False
        a__  		=  True

        if model_class in get_values(lowerCAmelCase ) or not model_class.supports_gradient_checkpointing:
          continue
        a__  		=  model_class(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.gradient_checkpointing_enable()
        model.train()
        a__  		=  self._prepare_for_class(lowerCAmelCase   ,   lowerCAmelCase   ,   return_labels=lowerCAmelCase )
        a__  		=  model(**lowerCAmelCase ).loss
        loss.backward()




    def     __lowerCamelCase			(       self	: List[str] )						->			int:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__  		=  _config_zero_init(lowerCAmelCase )
      for model_class in self.all_model_classes:
        a__  		=  model_class(config=lowerCAmelCase )
        # Skip the check for the backbone
        a__  		=  []
        for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
            a__  		=  [f"{name}.{key}" for key in module.state_dict().keys()]
            break

        for name, param in model.named_parameters():
          if param.requires_grad:
            if name in backbone_params:
              continue
            self.assertIn(
                ((param.data.mean() * 1e9).round() / 1e9).item()   ,   [0.0, 1.0]   ,   msg=f"Parameter {name} of model {model_class} seems not properly initialized"   ,   )




    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
    def     __lowerCamelCase			(       self	: List[str] )						->			Union[str, Any]:
      pass




    @slow
    def     __lowerCamelCase			(       self	: List[str] )						->			Dict:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a__  		=  DPTModel.from_pretrained(lowerCAmelCase )
        self.assertIsNotNone(lowerCAmelCase )

def 					__lowercase						(    )					->int:

  '''simple docstring'''




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







@require_torch
@require_vision
@slow
class        UpperCAmelCase_					(				unittest.TestCase     ):




    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large" )
      a__  		=  DPTForDepthEstimation.from_pretrained("Intel/dpt-large" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )
        a__  		=  outputs.predicted_depth

      # verify the predicted depth
      a__  		=  torch.Size((1, 384, 384) )
      self.assertEqual(predicted_depth.shape   ,   lowerCAmelCase )

      a__  		=  torch.tensor(
          [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] ).to(lowerCAmelCase )

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




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			str:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )

      # verify the logits
      a__  		=  torch.Size((1, 150, 480, 480) )
      self.assertEqual(outputs.logits.shape   ,   lowerCAmelCase )

      a__  		=  torch.tensor(
          [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] ).to(lowerCAmelCase )

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




    def     __lowerCamelCase			(       self	: str )						->			Optional[int]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )

      a__  		=  outputs.logits.detach().cpu()

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase   ,   target_sizes=[(500, 300)] )
      a__  		=  torch.Size((500, 300) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase )
      a__  		=  torch.Size((480, 480) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

