
from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TFSharedEmbeddings,
     )

     if _tf_gpu_memory_limit is not None:
          __magic_name__  :  Dict     			=   tf.config.list_physical_devices('GPU')
          for gpu in gpus:
               # Restrict TensorFlow to only allocate x GB of memory on the GPUs
               try:
                    tf.config.set_logical_device_configuration(
                        gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                    )
                    __magic_name__  :  int     			=   tf.config.list_logical_devices('GPU')
                    print('Logical GPUs', logical_gpus)
               except RuntimeError as e:
                    # Virtual devices must be set before GPUs have been initialized
                    print(e)


@require_tf
class 							lowerCamelCase       :



  """simple docstring"""





  lowerCAmelCase_								=		None
  lowerCAmelCase_								=		()
  lowerCAmelCase_								=		()
  lowerCAmelCase_								=		True
  lowerCAmelCase_								=		True
  lowerCAmelCase_								=		True
  lowerCAmelCase_								=		False

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase=False					):
      A_								=					copy.deepcopy(__UpperCamelCase					)

      if model_class in get_values(__UpperCamelCase					):
          A_								=					{
              k: tf.tile(tf.expand_dims(__UpperCamelCase						,    1					)						,    (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)					)
              if isinstance(__UpperCamelCase						,    tf.Tensor					) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }

      if return_labels:
          if model_class in get_values(__UpperCamelCase					):
              A_								=					tf.ones(self.model_tester.batch_size						,    dtype=tf.intaa					)
          elif model_class in get_values(__UpperCamelCase					):
              A_								=					tf.zeros(self.model_tester.batch_size						,    dtype=tf.intaa					)
              A_								=					tf.zeros(self.model_tester.batch_size						,    dtype=tf.intaa					)
          elif model_class in [
              *get_values(__UpperCamelCase					),
              *get_values(__UpperCamelCase					),
          ]:
              A_								=					tf.zeros(self.model_tester.batch_size						,    dtype=tf.intaa					)
          elif model_class in get_values(__UpperCamelCase					):
              A_								=					tf.zeros(self.model_tester.batch_size						,    dtype=tf.intaa					)
          elif model_class in [
              *get_values(__UpperCamelCase					),
              *get_values(__UpperCamelCase					),
              *get_values(__UpperCamelCase					),
              *get_values(__UpperCamelCase					),
              *get_values(__UpperCamelCase					),
          ]:
              A_								=					tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.seq_length)						,    dtype=tf.intaa					)
      return inputs_dict

  @slow
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          A_								=					self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)
          A_								=					model_class(__UpperCamelCase					)

          @tf.function
          def run_in_graph_mode():
              return model(__UpperCamelCase					)

          A_								=					run_in_graph_mode()
          self.assertIsNotNone(__UpperCamelCase					)

  @slow
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          A_								=					self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)
          A_								=					model_class(__UpperCamelCase					)

          @tf.function(experimental_compile=__UpperCamelCase					)
          def run_in_graph_mode():
              return model(__UpperCamelCase					)

          A_								=					run_in_graph_mode()
          self.assertIsNotNone(__UpperCamelCase					)

  @slow
  def 					lowercase_						(					self					):
      # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
          A_								=					model_class(__UpperCamelCase					)
          if getattr(__UpperCamelCase						,    "hf_compute_loss"						,    __UpperCamelCase					):
              # Test that model correctly compute the loss with kwargs
              A_								=					self._prepare_for_class(inputs_dict.copy()						,    __UpperCamelCase						,    return_labels=__UpperCamelCase					)
              # Is there a better way to remove these decoder inputs?
              A_								=					{
                  key: val
                  for key, val in prepared_for_class.items()
                  if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
              }

              A_								=					{
                  "labels",
                  "label",
                  "label_ids",
                  "start_positions",
                  "start_position",
                  "end_positions",
                  "end_position",
                  "next_sentence_label",
              }
              A_								=					possible_label_cols.intersection(set(__UpperCamelCase					)					)
              self.assertGreater(len(__UpperCamelCase					)						,    0						,    msg="No matching label names found!"					)
              A_								=					{key: val for key, val in prepared_for_class.items() if key in label_names}
              A_								=					{key: val for key, val in prepared_for_class.items() if key not in label_names}
              self.assertGreater(len(__UpperCamelCase					)						,    0					)

              # Make sure it works with XLA!
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0					)						,    jit_compile=__UpperCamelCase					)
              # Make sure the model fits without crashing regardless of where we pass the labels
              A_								=					model.fit(
                  __UpperCamelCase						,    validation_data=__UpperCamelCase						,    steps_per_epoch=1						,    validation_steps=1						,    shuffle=__UpperCamelCase						,    verbose=0						,    )
              A_								=					history.history["loss"][0]
              self.assertTrue(not isnan(__UpperCamelCase					)					)
              A_								=					history.history["val_loss"][0]
              self.assertTrue(not isnan(__UpperCamelCase					)					)

              # Now test it with separate labels, to make sure that path works in XLA too.
              A_								=					model_class(__UpperCamelCase					)
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0					)						,    jit_compile=__UpperCamelCase					)
              A_								=					model.fit(
                  __UpperCamelCase						,    __UpperCamelCase						,    validation_data=(inputs_minus_labels, labels)						,    steps_per_epoch=1						,    validation_steps=1						,    shuffle=__UpperCamelCase						,    verbose=0						,    )

              A_								=					history.history["loss"][0]
              self.assertTrue(not isnan(__UpperCamelCase					)					)
              A_								=					history.history["val_loss"][0]
              self.assertTrue(not isnan(__UpperCamelCase					)					)

  @slow
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      A_								=					True
      A_								=					True

      if hasattr(__UpperCamelCase						,    "use_cache"					):
          A_								=					True

      A_								=					getattr(self.model_tester						,    "encoder_seq_length"						,    self.model_tester.seq_length					)
      A_								=					getattr(self.model_tester						,    "key_length"						,    __UpperCamelCase					)

      for model_class in self.all_model_classes[:2]:
          A_								=					self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)
          A_								=					model_class(__UpperCamelCase					)
          model.build()
          A_								=					len(model(__UpperCamelCase					)					)

          for key in list(class_inputs_dict.keys()					):
              # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
              if key not in model.input_signature:
                  del class_inputs_dict[key]
              # Check it's a tensor, in case the inputs dict has some bools in it too
              elif isinstance(class_inputs_dict[key]						,    tf.Tensor					) and class_inputs_dict[key].dtype.is_integer:
                  A_								=					tf.cast(class_inputs_dict[key]						,    tf.intaa					)

          if set(class_inputs_dict.keys()					) != set(model.input_signature.keys()					):
              continue  # Some models have inputs that the preparation functions don't create, we skip those

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(__UpperCamelCase						,    saved_model=__UpperCamelCase					)
              A_								=					os.path.join(__UpperCamelCase						,    "saved_model"						,    "1"					)
              A_								=					tf.keras.models.load_model(__UpperCamelCase					)
              A_								=					model(__UpperCamelCase					)

              if self.is_encoder_decoder:
                  A_								=					outputs["encoder_hidden_states"]
                  A_								=					outputs["encoder_attentions"]
              else:
                  A_								=					outputs["hidden_states"]
                  A_								=					outputs["attentions"]

              self.assertEqual(len(__UpperCamelCase					)						,    __UpperCamelCase					)

              A_								=					getattr(
                  self.model_tester						,    "expected_num_hidden_layers"						,    self.model_tester.num_hidden_layers + 1					)

              self.assertEqual(len(__UpperCamelCase					)						,    __UpperCamelCase					)
              self.assertListEqual(
                  list(output_hidden_states[0].shape[-2:]					)						,    [self.model_tester.seq_length, self.model_tester.hidden_size]						,    )

              self.assertEqual(len(__UpperCamelCase					)						,    self.model_tester.num_hidden_layers					)
              self.assertListEqual(
                  list(output_attentions[0].shape[-3:]					)						,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,    )

  @slow
  def 					lowercase_						(					self					):
      tf.keras.mixed_precision.set_global_policy("mixed_float16"					)

      # try/finally block to ensure subsequent tests run in float32
      try:
          A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
              A_								=					self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)
              A_								=					model_class(__UpperCamelCase					)
              A_								=					model(__UpperCamelCase					)

              self.assertIsNotNone(__UpperCamelCase					)
      finally:
          tf.keras.mixed_precision.set_global_policy("float32"					)

  @slow
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()
      # head_mask and decoder_head_mask has different shapes than other input args
      if "head_mask" in inputs_dict:
          del inputs_dict["head_mask"]
      if "decoder_head_mask" in inputs_dict:
          del inputs_dict["decoder_head_mask"]
      if "cross_attn_head_mask" in inputs_dict:
          del inputs_dict["cross_attn_head_mask"]
      A_								=					{
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__					),)
          for module_member_name in dir(__UpperCamelCase					)
          if module_member_name.endswith("MainLayer"					)
          for module_member in (getattr(__UpperCamelCase						,    __UpperCamelCase					),)
          if isinstance(__UpperCamelCase						,    __UpperCamelCase					)
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(__UpperCamelCase						,    "_keras_serializable"						,    __UpperCamelCase					)
      }

      for main_layer_class in tf_main_layer_classes:
          # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
          if "T5" in main_layer_class.__name__:
              # Take the same values than in TFT5ModelTester for this shared layer
              A_								=					TFSharedEmbeddings(self.model_tester.vocab_size						,    self.model_tester.hidden_size						,    name="shared"					)
              A_								=					False
              A_								=					main_layer_class(__UpperCamelCase						,    embed_tokens=__UpperCamelCase					)
          else:
              A_								=					main_layer_class(__UpperCamelCase					)

          A_								=					{
              name: tf.keras.Input(tensor.shape[1:]						,    dtype=tensor.dtype					) for name, tensor in inputs_dict.items()
          }

          if hasattr(self.model_tester						,    "num_labels"					):
              A_								=					self.model_tester.num_labels
          else:
              A_								=					2

          A_								=					tf.data.Dataset.from_tensor_slices(
              (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)					))					).batch(1					)

          A_								=					main_layer(__UpperCamelCase					)[0]
          A_								=					tf.keras.layers.Dense(__UpperCamelCase						,    activation="softmax"						,    name="outputs"					)(__UpperCamelCase					)
          A_								=					tf.keras.models.Model(inputs=__UpperCamelCase						,    outputs=[outputs]					)

          model.compile(loss="binary_crossentropy"						,    optimizer="adam"						,    metrics=["binary_accuracy"]					)
          model.fit(__UpperCamelCase						,    epochs=1					)

          with tempfile.TemporaryDirectory() as tmpdirname:
              A_								=					os.path.join(__UpperCamelCase						,    "keras_model.h5"					)
              model.save(__UpperCamelCase					)
              if "T5" in main_layer_class.__name__:
                  A_								=					tf.keras.models.load_model(
                      __UpperCamelCase						,    custom_objects={
                          main_layer_class.__name__: main_layer_class,
                          "TFSharedEmbeddings": TFSharedEmbeddings,
                      }						,    )
              else:
                  A_								=					tf.keras.models.load_model(
                      __UpperCamelCase						,    custom_objects={main_layer_class.__name__: main_layer_class}					)
              assert isinstance(__UpperCamelCase						,    tf.keras.Model					)
              model(__UpperCamelCase					)

  @slow
  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
          A_								=					model_class(__UpperCamelCase					)

          A_								=					copy.deepcopy(__UpperCamelCase					)

          if not self.is_encoder_decoder:
              A_								=					inputs["input_ids"]
              del inputs["input_ids"]
          else:
              A_								=					inputs["input_ids"]
              A_								=					inputs.get("decoder_input_ids"						,    __UpperCamelCase					)
              del inputs["input_ids"]
              inputs.pop("decoder_input_ids"						,    __UpperCamelCase					)

          if not self.is_encoder_decoder:
              A_								=					model.get_input_embeddings()(__UpperCamelCase					)
          else:
              A_								=					model.get_input_embeddings()(__UpperCamelCase					)
              A_								=					model.get_input_embeddings()(__UpperCamelCase					)

          A_								=					self._prepare_for_class(__UpperCamelCase						,    __UpperCamelCase					)

          @tf.function
          def run_in_graph_mode():
              return model(__UpperCamelCase					)

          A_								=					run_in_graph_mode()
          self.assertIsNotNone(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      # special tokens cannot be bad tokens
      A_								=					[]
      if model.config.bos_token_id is not None:
          special_tokens.append(model.config.bos_token_id					)
      if model.config.pad_token_id is not None:
          special_tokens.append(model.config.pad_token_id					)
      if model.config.eos_token_id is not None:
          special_tokens.append(model.config.eos_token_id					)

      # create random bad tokens that are not special tokens
      A_								=					[]
      while len(__UpperCamelCase					) < num_bad_tokens:
          A_								=					tf.squeeze(ids_tensor((1, 1)						,    self.model_tester.vocab_size					)						,    0					).numpy()[0]
          if token not in special_tokens:
              bad_tokens.append(__UpperCamelCase					)
      return bad_tokens

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      for token_id in output_ids[0].numpy().tolist():
          self.assertGreaterEqual(__UpperCamelCase						,    0					)
          self.assertLess(__UpperCamelCase						,    self.model_tester.vocab_size					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      # for all bad word tokens
      for bad_word_ids in bad_words_ids:
          # for all slices in batch
          for generated_ids_slice in generated_ids:
              # for all word idx
              for i in range(len(__UpperCamelCase					)						,    len(__UpperCamelCase					)					):
                  # if tokens match
                  if generated_ids_slice[i - len(__UpperCamelCase					) : i] == bad_word_ids:
                      return True
      return False








import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__magic_name__  :  Dict     			=   1_0_0_0


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=1.2						,    __UpperCamelCase="g"						,    __UpperCamelCase=0.5						,    __UpperCamelCase="-"						,    __UpperCamelCase="test_out.jpg"						,    __UpperCamelCase=True						,    __UpperCamelCase=False						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=0.7						,    ):
      if isinstance(__UpperCamelCase						,    torch.Tensor					):
          A_								=					img.numpy().astype("np.uint8"					)
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					img_tensorize(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    np.ndarray					)

      A_	,	A_								=					img.shape[1], img.shape[0]
      A_								=					mplfigure.Figure(frameon=__UpperCamelCase					)
      A_								=					fig.get_dpi()
      A_								=					(width * scale + 1E-2) / dpi
      A_								=					(height * scale + 1E-2) / dpi
      fig.set_size_inches(__UpperCamelCase						,    __UpperCamelCase					)
      A_								=					fig.add_axes([0.0, 0.0, 1.0, 1.0]					)
      ax.axis("off"					)
      ax.set_xlim(0.0						,    __UpperCamelCase					)
      ax.set_ylim(__UpperCamelCase					)

      A_								=					saveas
      A_								=					rgb
      A_								=					pynb
      A_								=					img
      A_								=					edgecolor
      A_								=					0.5
      A_								=					linestyle
      A_								=					int(np.sqrt(min(__UpperCamelCase						,    __UpperCamelCase					)					) * scale // 3					)
      A_								=					width
      A_								=					height
      A_								=					scale
      A_								=					fig
      A_								=					ax
      A_								=					pad
      A_								=					idaobj
      A_								=					idaattr
      A_								=					FigureCanvasAgg(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      if color is None:
          A_								=					self.edgecolor
      ((A_)	,	(A_)	,	(A_)	,	(A_))								=					box
      A_								=					xa - xa
      A_								=					ya - ya
      self.ax.add_patch(
          mpl.patches.Rectangle(
              (xa, ya)						,    __UpperCamelCase						,    __UpperCamelCase						,    fill=__UpperCamelCase						,    edgecolor=__UpperCamelCase						,    linewidth=self.font_size // 3						,    alpha=self.alpha						,    linestyle=self.linestyle						,    )					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None					):
      if len(boxes.shape					) > 2:
          A_								=					boxes[0]
      if len(obj_ids.shape					) > 1:
          A_								=					obj_ids[0]
      if len(obj_scores.shape					) > 1:
          A_								=					obj_scores[0]
      if len(attr_ids.shape					) > 1:
          A_								=					attr_ids[0]
      if len(attr_scores.shape					) > 1:
          A_								=					attr_scores[0]
      if isinstance(__UpperCamelCase						,    torch.Tensor					):
          A_								=					boxes.numpy()
      if isinstance(__UpperCamelCase						,    __UpperCamelCase					):
          A_								=					np.array(__UpperCamelCase					)
      assert isinstance(__UpperCamelCase						,    np.ndarray					)
      A_								=					np.prod(boxes[:, 2:] - boxes[:, :2]						,    axis=1					)
      A_								=					np.argsort(-areas					).tolist()
      A_								=					boxes[sorted_idxs] if boxes is not None else None
      A_								=					obj_ids[sorted_idxs] if obj_ids is not None else None
      A_								=					obj_scores[sorted_idxs] if obj_scores is not None else None
      A_								=					attr_ids[sorted_idxs] if attr_ids is not None else None
      A_								=					attr_scores[sorted_idxs] if attr_scores is not None else None

      A_								=					[self._random_color(maximum=1					) for _ in range(len(__UpperCamelCase					)					)]
      A_								=					[assigned_colors[idx] for idx in sorted_idxs]
      if obj_ids is not None:
          A_								=					self._create_text_labels_attr(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
          for i in range(len(__UpperCamelCase					)					):
              A_								=					assigned_colors[i]
              self.add_box(boxes[i]						,    __UpperCamelCase					)
              self.draw_labels(labels[i]						,    boxes[i]						,    __UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_	,	A_	,	A_	,	A_								=					box
      A_								=					(xa, ya)
      A_								=					(ya - ya) * (xa - xa)
      A_								=					_SMALL_OBJ * self.scale
      if instance_area < small or ya - ya < 40 * self.scale:
          if ya >= self.height - 5:
              A_								=					(xa, ya)
          else:
              A_								=					(xa, ya)

      A_								=					(ya - ya) / np.sqrt(self.height * self.width					)
      A_								=					self._change_color_brightness(__UpperCamelCase						,    brightness_factor=0.7					)
      A_								=					np.clip((height_ratio - 0.02) / 0.08 + 1						,    1.2						,    2					)
      font_size *= 0.75 * self.font_size

      self.draw_text(
          text=__UpperCamelCase						,    position=__UpperCamelCase						,    color=__UpperCamelCase						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase="g"						,    __UpperCamelCase="left"						,    ):
      A_								=					0
      A_								=					self.font_size
      A_								=					np.maximum(list(mplc.to_rgb(__UpperCamelCase					)					)						,    0.2					)
      A_								=					max(0.8						,    np.max(__UpperCamelCase					)					)
      A_								=					{
          "facecolor": "black",
          "alpha": self.alpha,
          "pad": self.pad,
          "edgecolor": "none",
      }
      A_	,	A_								=					position
      self.ax.text(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    size=font_size * self.scale						,    family="sans-serif"						,    bbox=__UpperCamelCase						,    verticalalignment="top"						,    horizontalalignment=__UpperCamelCase						,    color=__UpperCamelCase						,    zorder=10						,    rotation=__UpperCamelCase						,    )

  def 					lowercase_						(					self						,    __UpperCamelCase=None					):
      if saveas is None:
          A_								=					self.saveas
      if saveas.lower().endswith(".jpg"					) or saveas.lower().endswith(".png"					):
          cva.imwrite(
              __UpperCamelCase						,    self._get_buffer()[:, :, ::-1]						,    )
      else:
          self.fig.savefig(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					[self.idaobj[i] for i in classes]
      A_								=					[self.idaattr[i] for i in attr_classes]
      A_								=					[
          f'{label} {score:.2f} {attr} {attr_score:.2f}'
          for label, score, attr, attr_score in zip(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      ]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					[self.idaobj[i] for i in classes]
      if scores is not None:
          if labels is None:
              A_								=					["{:.0f}%".format(s * 100					) for s in scores]
          else:
              A_								=					["{} {:.0f}%".format(__UpperCamelCase						,    s * 100					) for li, s in zip(__UpperCamelCase						,    __UpperCamelCase					)]
      return labels

  def 					lowercase_						(					self						,    __UpperCamelCase=255					):
      A_								=					np.random.randint(0						,    len(_COLORS					)					)
      A_								=					_COLORS[idx] * maximum
      if not self.rgb:
          A_								=					ret[::-1]
      return ret

  def 					lowercase_						(					self					):
      if not self.pynb:
          A_	,	A_								=					self.canvas.print_to_buffer()
          if (width, height) != (self.width, self.height):
              A_								=					cva.resize(self.img						,    (width, height)					)
          else:
              A_								=					self.img
      else:
          A_								=					io.BytesIO()  # works for cairo backend
          self.canvas.print_rgba(__UpperCamelCase					)
          A_	,	A_								=					self.width, self.height
          A_								=					buf.getvalue()
          A_								=					self.img

      A_								=					np.frombuffer(__UpperCamelCase						,    dtype="uint8"					)
      A_								=					buffer.reshape(__UpperCamelCase						,    __UpperCamelCase						,    4					)
      A_	,	A_								=					np.split(__UpperCamelCase						,    [3]						,    axis=2					)

      try:
          import numexpr as ne  # fuse them with numexpr

          A_								=					ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"					)
      except ImportError:
          A_								=					alpha.astype("float32"					) / 255.0
          A_								=					img * (1 - alpha) + rgb * alpha

      return visualized_image.astype("uint8"					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      assert brightness_factor >= -1.0 and brightness_factor <= 1.0
      A_								=					mplc.to_rgb(__UpperCamelCase					)
      A_								=					colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCamelCase					)					)
      A_								=					polygon_color[1] + (brightness_factor * polygon_color[1])
      A_								=					0.0 if modified_lightness < 0.0 else modified_lightness
      A_								=					1.0 if modified_lightness > 1.0 else modified_lightness
      A_								=					colorsys.hls_to_rgb(polygon_color[0]						,    __UpperCamelCase						,    polygon_color[2]					)
      return modified_color



__magic_name__  :  List[str]     			=   (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






