






"""simple docstring"""





import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("""ignore""")

UpperCamelCase			=      """K-MEANS-CLUST/ """






def     A(  snake_case_						,       snake_case_						,       snake_case_=None		):
							"""simple docstring"""





							if seed is not None:  # useful for obtaining consistent results
														np.random.seed(snake_case_		)
							lowercase__:    int        =      data.shape[0]  # number of data points

							# Pick K indices from range [0, N).
							lowercase__:    str        =      np.random.randint(0						,       snake_case_						,       snake_case_		)

							# Keep centroids as dense format, as many entries will be nonzero due to averaging.
							# As long as at least one document in a cluster contains a word,
							# it will carry a nonzero weight in the TF-IDF vector of the centroid.
							lowercase__:    Optional[Any]        =      data[rand_indices, :]

							return centroids






def     A(  snake_case_						,       snake_case_		):
							"""simple docstring"""





							return pairwise_distances(snake_case_						,       snake_case_						,       metric="euclidean"		)






def     A(  snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    Optional[Any]        =      centroid_pairwise_dist(snake_case_						,       snake_case_		)

							# Compute cluster assignments for each data point:
							# Fill in the blank (RHS only)
							lowercase__:    Any        =      np.argmin(snake_case_						,       axis=1		)

							return cluster_assignment






def     A(  snake_case_						,       snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    List[Any]        =      []
							for i in range(snake_case_		):
														# Select all data points that belong to cluster i. Fill in the blank (RHS only)
														lowercase__:    List[Any]        =      data[cluster_assignment == i]
														# Compute the mean of the data points. Fill in the blank (RHS only)
														lowercase__:    List[str]        =      member_data_points.mean(axis=0		)
														new_centroids.append(snake_case_		)
							lowercase__:    List[str]        =      np.array(snake_case_		)

							return new_centroids






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		):
							"""simple docstring"""





							lowercase__:    Optional[Any]        =      0.0
							for i in range(snake_case_		):
														# Select all data points that belong to cluster i. Fill in the blank (RHS only)
														lowercase__:    Tuple        =      data[cluster_assignment == i, :]

														if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
																					# Compute distances from centroid to data points (RHS only)
																					lowercase__:    Optional[Any]        =      pairwise_distances(
																					    snake_case_						,       [centroids[i]]						,       metric="euclidean"		)
																					lowercase__:    List[str]        =      distances**2
																					heterogeneity += np.sum(snake_case_		)

							return heterogeneity






def     A(  snake_case_						,       snake_case_		):
							"""simple docstring"""





							plt.figure(figsize=(7, 4)		)
							plt.plot(snake_case_						,       linewidth=4		)
							plt.xlabel("# Iterations"		)
							plt.ylabel("Heterogeneity"		)
							plt.title(F"""Heterogeneity of clustering over time, K={k:d}"""		)
							plt.rcParams.update({"font.size": 16}		)
							plt.show()






def     A(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_=500						,       snake_case_=None						,       snake_case_=False		):
							"""simple docstring"""





							lowercase__:    Tuple        =      initial_centroids[:]
							lowercase__:    Dict        =      None

							for itr in range(snake_case_		):
														if verbose:
																					print(snake_case_						,       end=""		)

														# 1. Make cluster assignments using nearest centroids
														lowercase__:    Any        =      assign_clusters(snake_case_						,       snake_case_		)

														# 2. Compute a new centroid for each of the k clusters, averaging all data
														#    points assigned to that cluster.
														lowercase__:    Any        =      revise_centroids(snake_case_						,       snake_case_						,       snake_case_		)

														# Check for convergence: if none of the assignments changed, stop
														if (
														    prev_cluster_assignment is not None
														    and (prev_cluster_assignment == cluster_assignment).all()
														):
																					break

														# Print number of new assignments
														if prev_cluster_assignment is not None:
																					lowercase__:    Tuple        =      np.sum(prev_cluster_assignment != cluster_assignment		)
																					if verbose:
																												print(
																												    F"""    {num_changed:5d} elements changed their cluster assignment."""		)

        # Record heterogeneity convergence metric
														if record_heterogeneity is not None:
																					# YOUR CODE HERE
																					lowercase__:    Dict        =      compute_heterogeneity(snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		)
																					record_heterogeneity.append(snake_case_		)

														lowercase__:    List[str]        =      cluster_assignment[:]

							return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
		from sklearn import datasets as ds

		UpperCamelCase			=      ds.load_iris()
		UpperCamelCase			=      3
		UpperCamelCase			=      []
		UpperCamelCase			=      get_initial_centroids(dataset["""data"""], k, seed=0)
		UpperCamelCase,      UpperCamelCase			=      kmeans(
		    dataset["""data"""],
		    k,
		    initial_centroids,
		    maxiter=4_00,
		    record_heterogeneity=heterogeneity,
		    verbose=True,
		)
		plot_heterogeneity(heterogeneity, k)






def     A(  snake_case_						,       snake_case_						,       snake_case_=None		):
							"""simple docstring"""





							if fill_missing_report:
														lowercase__:    Optional[Any]        =      df.fillna(value=snake_case_		)
							lowercase__:    Optional[int]        =      1
							lowercase__:    Dict        =      df.select_dtypes(np.number		).columns
							lowercase__:    Optional[Any]        =      (
							    df.groupby(["Cluster"]		)[  # construct report dataframe
							        numeric_cols
							    ]  # group by cluster number
							    .agg(
							        [
							            ("sum", np.sum),
							            ("mean_with_zeros", lambda snake_case_		: np.mean(np.nan_to_num(snake_case_		)		)),
							            ("mean_without_zeros", lambda snake_case_		: x.replace(0						,       np.NaN		).mean()),
							            (
							                "mean_25-75",
							                lambda snake_case_		: np.mean(
							                    np.nan_to_num(
							                        sorted(snake_case_		)[
							                            round(len(snake_case_		) * 25 / 100		) : round(len(snake_case_		) * 75 / 100		)
							                        ]		)		),
							            ),
							            ("mean_with_na", np.mean),
							            ("min", lambda snake_case_		: x.min()),
							            ("5%", lambda snake_case_		: x.quantile(0.05		)),
							            ("25%", lambda snake_case_		: x.quantile(0.25		)),
							            ("50%", lambda snake_case_		: x.quantile(0.50		)),
							            ("75%", lambda snake_case_		: x.quantile(0.75		)),
							            ("95%", lambda snake_case_		: x.quantile(0.95		)),
							            ("max", lambda snake_case_		: x.max()),
							            ("count", lambda snake_case_		: x.count()),
							            ("stdev", lambda snake_case_		: x.std()),
							            ("mode", lambda snake_case_		: x.mode()[0]),
							            ("median", lambda snake_case_		: x.median()),
							            ("# > 0", lambda snake_case_		: (x > 0).sum()),
							        ]		)
							    .T.reset_index()
							    .rename(index=snake_case_						,       columns={"level_0": "Features", "level_1": "Type"}		)
							)  # rename columns
							# calculate the size of cluster(count of clientID's)
							lowercase__:    Optional[Any]        =      report[
							    (report["Features"] == "dummy") & (report["Type"] == "count")
							].copy()  # avoid SettingWithCopyWarning
							lowercase__:    Optional[Any]        =      (
							    "ClusterSize"  # rename created cluster df to match report column names
							)
							lowercase__:    Tuple        =      "# of Customers"
							lowercase__:    Tuple        =      pd.DataFrame(
							    clustersize.iloc[:, 2:].values
							    / clustersize.iloc[:, 2:].values.sum()		)
							lowercase__:    Any        =      "% of Customers"  # rename created cluster df to match report column names
							lowercase__:    List[str]        =      "ClusterProportion"
							lowercase__:    Optional[int]        =      clusterproportion.columns.tolist()
							lowercase__:    Dict        =      cols[-2:] + cols[:-2]
							lowercase__:    Optional[Any]        =      clusterproportion[cols]  # rearrange columns to match report
							lowercase__:    str        =      report.columns
							lowercase__:    Tuple        =      pd.DataFrame(
							    abs(
							        report[report["Type"] == "count"].iloc[:, 2:].values
							        - clustersize.iloc[:, 2:].values		)		)  # generating df with count of nan values
							lowercase__:    str        =      0
							lowercase__:    Union[str, Any]        =      "# of nan"
							lowercase__:    Optional[int]        =      report[
							    report["Type"] == "count"
							].Features.tolist()  # filling values in order to match report
							lowercase__:    List[str]        =      a.columns.tolist()
							lowercase__:    List[Any]        =      cols[-2:] + cols[:-2]
							lowercase__:    Dict        =      a[cols]  # rearrange columns to match report
							lowercase__:    Tuple        =      report.columns  # rename columns to match report
							lowercase__:    Optional[Any]        =      report.drop(
							    report[report.Type == "count"].index		)  # drop count values except cluster size
							lowercase__:    Optional[int]        =      pd.concat(
							    [report, a, clustersize, clusterproportion]						,       axis=0		)  # concat report with clustert size and nan values
							lowercase__:    Tuple        =      report["Features"].isin(snake_case_		)
							lowercase__:    Any        =      report.columns.tolist()
							lowercase__:    List[str]        =      cols[0:2] + cols[-1:] + cols[2:-1]
							lowercase__:    Any        =      report[cols]
							lowercase__:    Any        =      {
							    "ClusterSize": 9,
							    "ClusterProportion": 8,
							    "mean_with_zeros": 7,
							    "mean_with_na": 6,
							    "max": 5,
							    "50%": 4,
							    "min": 3,
							    "25%": 2,
							    "75%": 1,
							    "# of nan": 0,
							    "# > 0": -1,
							    "sum_with_na": -2,
							}
							lowercase__:    str        =      (
							    report.assign(
							        Sortera=lambda snake_case_		: x.Type.map(snake_case_		)						,       Sortera=lambda snake_case_		: list(reversed(range(len(snake_case_		)		)		)		)						,       )
							    .sort_values(["Sorter1", "Mark", "Sorter2"]						,       ascending=snake_case_		)
							    .drop(["Sorter1", "Sorter2"]						,       axis=1		)
							)
							lowercase__:    Dict        =      ""
							lowercase__:    int        =      report.reset_index()
							lowercase__:    List[Any]        =      report.drop(columns=["index"]		)
							return report


if __name__ == "__main__":
		import doctest

		doctest.testmod()














"""simple docstring"""





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )







class 		_a       :





   '''simple docstring'''






   def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=7			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=99			,				UpperCAmelCase_=32			,				UpperCAmelCase_=5			,				UpperCAmelCase_=4			,				UpperCAmelCase_=37			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=512			,				UpperCAmelCase_=16			,				UpperCAmelCase_=2			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=3			,				UpperCAmelCase_=4			,				UpperCAmelCase_=None			,				UpperCAmelCase_=1_000			,				)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    int        =      parent
          lowercase__:    str        =      batch_size
          lowercase__:    Tuple        =      seq_length
          lowercase__:    Any        =      is_training
          lowercase__:    Union[str, Any]        =      use_input_mask
          lowercase__:    str        =      use_token_type_ids
          lowercase__:    Optional[int]        =      use_labels
          lowercase__:    List[str]        =      vocab_size
          lowercase__:    Optional[Any]        =      hidden_size
          lowercase__:    Optional[Any]        =      num_hidden_layers
          lowercase__:    Optional[int]        =      num_attention_heads
          lowercase__:    List[str]        =      intermediate_size
          lowercase__:    Any        =      hidden_act
          lowercase__:    Any        =      hidden_dropout_prob
          lowercase__:    List[str]        =      attention_probs_dropout_prob
          lowercase__:    Tuple        =      max_position_embeddings
          lowercase__:    Any        =      type_vocab_size
          lowercase__:    List[str]        =      type_sequence_label_size
          lowercase__:    Dict        =      initializer_range
          lowercase__:    Dict        =      num_labels
          lowercase__:    List[str]        =      num_choices
          lowercase__:    int        =      scope
          lowercase__:    List[Any]        =      range_bbox





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    int        =      ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

          lowercase__:    Tuple        =      ids_tensor([self.batch_size, self.seq_length, 4]			,				self.range_bbox)
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]):
                 for j in range(bbox.shape[1]):
                        if bbox[i, j, 3] < bbox[i, j, 1]:
                               lowercase__:    List[str]        =      bbox[i, j, 3]
                               lowercase__:    List[str]        =      bbox[i, j, 1]
                               lowercase__:    Union[str, Any]        =      t
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                               lowercase__:    Optional[int]        =      bbox[i, j, 2]
                               lowercase__:    Tuple        =      bbox[i, j, 0]
                               lowercase__:    Optional[Any]        =      t

          lowercase__:    Dict        =      None
          if self.use_input_mask:
                 lowercase__:    str        =      ids_tensor([self.batch_size, self.seq_length]			,				vocab_size=2)

          lowercase__:    str        =      None
          if self.use_token_type_ids:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

          lowercase__:    Tuple        =      None
          lowercase__:    Dict        =      None
          lowercase__:    Any        =      None
          if self.use_labels:
                 lowercase__:    List[Any]        =      ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
                 lowercase__:    Any        =      ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
                 lowercase__:    List[str]        =      ids_tensor([self.batch_size]			,				self.num_choices)

          lowercase__:    int        =      self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          return LayoutLMConfig(
              vocab_size=self.vocab_size			,				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			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				initializer_range=self.initializer_range			,				)





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    Any        =      LayoutLMModel(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Tuple        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Dict        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
          lowercase__:    Any        =      model(UpperCAmelCase_			,				UpperCAmelCase_)
          self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))
          self.parent.assertEqual(result.pooler_output.shape			,				(self.batch_size, self.hidden_size))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForMaskedLM(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    str        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Union[str, Any]        =      self.num_labels
          lowercase__:    Optional[Any]        =      LayoutLMForSequenceClassification(UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    Optional[Any]        =      model(
              UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[str]        =      self.num_labels
          lowercase__:    List[str]        =      LayoutLMForTokenClassification(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
          self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels))





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




          '''simple docstring'''




          lowercase__:    Dict        =      LayoutLMForQuestionAnswering(config=UpperCAmelCase_)
          model.to(UpperCAmelCase_)
          model.eval()
          lowercase__:    List[Any]        =      model(
              UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				start_positions=UpperCAmelCase_			,				end_positions=UpperCAmelCase_			,				)
          self.parent.assertEqual(result.start_logits.shape			,				(self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape			,				(self.batch_size, self.seq_length))





   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    int        =      self.prepare_config_and_inputs()
          (
              (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     (
              lowercase__
          )  ,     
          ):    int        =      config_and_inputs
          lowercase__:    Optional[int]        =      {
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_torch
class 		_a       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





   '''simple docstring'''






   UpperCamelCase__      					=    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   UpperCamelCase__      					=    (
       {
           """document-question-answering""": LayoutLMForQuestionAnswering,
           """feature-extraction""": LayoutLMModel,
           """fill-mask""": LayoutLMForMaskedLM,
           """text-classification""": LayoutLMForSequenceClassification,
           """token-classification""": LayoutLMForTokenClassification,
           """zero-shot""": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCamelCase__      					=    True





   def __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    List[str]        =      LayoutLMModelTester(self)
          lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				hidden_size=37)





   def __lowercase    (   self)	-> Optional[Any]:




          '''simple docstring'''




          self.config_tester.run_common_tests()





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    int        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase_)





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowercase__:    Union[str, Any]        =      type
                 self.model_tester.create_and_check_model(*UpperCAmelCase_)





   def __lowercase    (   self)	-> List[str]:




          '''simple docstring'''




          lowercase__:    Any        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_)





   def __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          lowercase__:    Dict        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)





   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          lowercase__:    Optional[Any]        =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)





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




          '''simple docstring'''




          pass







def     A(  ):
       """simple docstring"""





       lowercase__:    Any        =      torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Optional[Any]        =      torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						,       device=snake_case_		)  # noqa: E231
       lowercase__:    Dict        =      torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						,       device=snake_case_		)  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       lowercase__:    int        =      torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						,       device=snake_case_		)  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		_a       (						unittest.TestCase   ):





   '''simple docstring'''






   @slow
   def __lowercase    (   self)	-> Any:




          '''simple docstring'''




          lowercase__:    str        =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Dict        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the sequence output on [0, :3, :3]
          lowercase__:    Optional[int]        =      torch.tensor(
              [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,				device=UpperCAmelCase_			,				)

          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]			,				UpperCAmelCase_			,				atol=1E-3))

          # test the pooled output on [1, :3]
          lowercase__:    List[Any]        =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]			,				device=UpperCAmelCase_)

          self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]			,				UpperCAmelCase_			,				atol=1E-3))





   @slow
   def __lowercase    (   self)	-> Optional[Any]:




          '''simple docstring'''




          lowercase__:    int        =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=2).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(
              input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=torch.tensor([1, 1]			,				device=UpperCAmelCase_)			,				)

          # test whether we get a loss as a scalar
          lowercase__:    Dict        =      outputs.loss
          lowercase__:    int        =      torch.Size([])
          self.assertEqual(loss.shape			,				UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      outputs.logits
          lowercase__:    Any        =      torch.Size((2, 2))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Union[str, Any]:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=13).to(
              UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    Any        =      model(
              input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)

          # test the loss calculation to be around 2.65
          # expected_loss = torch.tensor(2.65, device=torch_device)

          # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
          # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

          # test the shape of the logits
          lowercase__:    int        =      outputs.logits
          lowercase__:    int        =      torch.Size((2, 25, 13))
          self.assertEqual(logits.shape			,				UpperCAmelCase_)





   @slow
   def __lowercase    (   self)	-> Tuple:




          '''simple docstring'''




          lowercase__:    Tuple        =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

          lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__  ,     lowercase__:    Tuple        =      prepare_layoutlm_batch_inputs()

          # forward pass
          lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

          # test the shape of the logits
          lowercase__:    str        =      torch.Size((2, 25))
          self.assertEqual(outputs.start_logits.shape			,				UpperCAmelCase_)
          self.assertEqual(outputs.end_logits.shape			,				UpperCAmelCase_)






