

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
  )
  from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor





class A__    :



 """simple docstring"""







 def __init__(		self :	str     ,						A_ :	List[str]     ,						A_ :	str=2     ,						A_ :	Tuple=3     ,						A_ :	Optional[Any]=4     ,						A_ :	List[str]=2     ,						A_ :	List[str]=7     ,						A_ :	Optional[Any]=True     ,						A_ :	Any=True     ,						A_ :	Dict=True     ,						A_ :	Tuple=True     ,						A_ :	List[str]=9_9     ,						A_ :	Any=3_6     ,						A_ :	Any=3     ,						A_ :	str=4     ,						A_ :	List[str]=3_7     ,						A_ :	Tuple="gelu"     ,						A_ :	Union[str, Any]=0.1     ,						A_ :	Union[str, Any]=0.1     ,						A_ :	int=5_1_2     ,						A_ :	List[str]=1_6     ,						A_ :	Tuple=2     ,						A_ :	str=0.02     ,						A_ :	Any=6     ,						A_ :	str=6     ,						A_ :	str=3     ,						A_ :	Optional[int]=4     ,						A_ :	Tuple=None     ,						A_ :	Optional[Any]=1_0_0_0     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					Tuple        =    batch_size
      _lowerCAmelCase   :					Any        =    num_channels
      _lowerCAmelCase   :					Optional[int]        =    image_size
      _lowerCAmelCase   :					Optional[int]        =    patch_size
      _lowerCAmelCase   :					Optional[Any]        =    text_seq_length
      _lowerCAmelCase   :					Optional[Any]        =    is_training
      _lowerCAmelCase   :					List[str]        =    use_input_mask
      _lowerCAmelCase   :					Any        =    use_token_type_ids
      _lowerCAmelCase   :					Union[str, Any]        =    use_labels
      _lowerCAmelCase   :					Optional[Any]        =    vocab_size
      _lowerCAmelCase   :					Optional[int]        =    hidden_size
      _lowerCAmelCase   :					Union[str, Any]        =    num_hidden_layers
      _lowerCAmelCase   :					str        =    num_attention_heads
      _lowerCAmelCase   :					Optional[int]        =    intermediate_size
      _lowerCAmelCase   :					Any        =    hidden_act
      _lowerCAmelCase   :					Any        =    hidden_dropout_prob
      _lowerCAmelCase   :					List[Any]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					Any        =    max_position_embeddings
      _lowerCAmelCase   :					List[str]        =    type_vocab_size
      _lowerCAmelCase   :					List[Any]        =    type_sequence_label_size
      _lowerCAmelCase   :					List[Any]        =    initializer_range
      _lowerCAmelCase   :					Union[str, Any]        =    coordinate_size
      _lowerCAmelCase   :					str        =    shape_size
      _lowerCAmelCase   :					Any        =    num_labels
      _lowerCAmelCase   :					Optional[Any]        =    num_choices
      _lowerCAmelCase   :					Union[str, Any]        =    scope
      _lowerCAmelCase   :					int        =    range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      _lowerCAmelCase   :					Any        =    text_seq_length
      _lowerCAmelCase   :					Optional[Any]        =    (image_size // patch_size) ** 2 + 1
      _lowerCAmelCase   :					List[Any]        =    self.text_seq_length + self.image_seq_length

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    ids_tensor([self.batch_size, self.text_seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					Optional[Any]        =    ids_tensor([self.batch_size, self.text_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]:
                     _lowerCAmelCase   :					str        =    bbox[i, j, 3]
                     _lowerCAmelCase   :					Dict        =    bbox[i, j, 1]
                     _lowerCAmelCase   :					List[Any]        =    t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                     _lowerCAmelCase   :					Tuple        =    bbox[i, j, 2]
                     _lowerCAmelCase   :					Optional[int]        =    bbox[i, j, 0]
                     _lowerCAmelCase   :					Union[str, Any]        =    t

      _lowerCAmelCase   :					int        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _lowerCAmelCase   :					Optional[Any]        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					List[Any]        =    random_attention_mask([self.batch_size, self.text_seq_length]       )

      _lowerCAmelCase   :					Any        =    None
      if self.use_token_type_ids:
           _lowerCAmelCase   :					str        =    ids_tensor([self.batch_size, self.text_seq_length]     ,						self.type_vocab_size       )

      _lowerCAmelCase   :					List[Any]        =    None
      _lowerCAmelCase   :					List[str]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					Optional[int]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size, self.text_seq_length]     ,						self.num_labels       )

      _lowerCAmelCase   :					Tuple        =    LayoutLMvaConfig(
          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     ,						coordinate_size=self.coordinate_size     ,						shape_size=self.shape_size     ,						input_size=self.image_size     ,						patch_size=self.patch_size     ,						)

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

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	str     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	str     ,						A_ :	Optional[int]     ,						A_ :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    LayoutLMvaModel(config=A_       )
      model.to(A_       )
      model.eval()

      # text + image
      _lowerCAmelCase   :					Optional[int]        =    model(A_     ,						pixel_values=A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    model(
          A_     ,						bbox=A_     ,						pixel_values=A_     ,						attention_mask=A_     ,						token_type_ids=A_       )
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						bbox=A_     ,						pixel_values=A_     ,						token_type_ids=A_       )
      _lowerCAmelCase   :					List[str]        =    model(A_     ,						bbox=A_     ,						pixel_values=A_       )

      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

      # text only
      _lowerCAmelCase   :					int        =    model(A_       )
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,						(self.batch_size, self.text_seq_length, self.hidden_size)       )

      # image only
      _lowerCAmelCase   :					Optional[Any]        =    model(pixel_values=A_       )
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,						(self.batch_size, self.image_seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Dict     ,						A_ :	List[str]     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	Any     ,						A_ :	Dict     ,						A_ :	int     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.num_labels
      _lowerCAmelCase   :					Optional[int]        =    LayoutLMvaForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					str        =    model(
          A_     ,						bbox=A_     ,						pixel_values=A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	int     ,						A_ :	Optional[Any]     ,						A_ :	Any     ,						A_ :	Union[str, Any]     ,						A_ :	str     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	Optional[int]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_labels
      _lowerCAmelCase   :					Optional[int]        =    LayoutLMvaForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(
          A_     ,						bbox=A_     ,						pixel_values=A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.text_seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	int     ,						A_ :	Any     ,						A_ :	Any     ,						A_ :	int     ,						A_ :	str     ,						A_ :	int     ,						A_ :	Any     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    LayoutLMvaForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					str        =    model(
          A_     ,						bbox=A_     ,						pixel_values=A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						start_positions=A_     ,						end_positions=A_     ,						)
      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    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.prepare_config_and_inputs()
      (
          (
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			
      )   :					List[Any]        =    config_and_inputs
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "input_ids": input_ids,
          "bbox": bbox,
          "pixel_values": pixel_values,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						False
 _lowercase     :					int      =						False
 _lowercase     :					int      =						False

 _lowercase     :					Dict      =						(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowercase     :					Tuple      =						(
     {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
     if is_torch_available()
     else {}
 )

 def    __magic_name__       (		self :	int     ,						A_ :	List[str]     ,						A_ :	Dict     ,						A_ :	Optional[Any]     ,						A_ :	Optional[Any]     ,						A_ :	str       ):






      '''simple docstring'''




      return True

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    LayoutLMvaModelTester(self       )
      _lowerCAmelCase   :					List[Any]        =    ConfigTester(self     ,						config_class=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Any     ,						A_ :	Any     ,						A_ :	Any=False       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    copy.deepcopy(A_       )
      if model_class in get_values(A_       ):
           _lowerCAmelCase   :					int        =    {
               k: v.unsqueeze(1       ).expand(-1     ,						self.model_tester.num_choices     ,						-1       ).contiguous()
               if isinstance(A_     ,						torch.Tensor       ) and v.ndim > 1
               else v
               for k, v in inputs_dict.items()
           }
      if return_labels:
           if model_class in get_values(A_       ):
                _lowerCAmelCase   :					List[str]        =    torch.ones(self.model_tester.batch_size     ,						dtype=torch.long     ,						device=A_       )
           elif model_class in get_values(A_       ):
                _lowerCAmelCase   :					Dict        =    torch.zeros(
                    self.model_tester.batch_size     ,						dtype=torch.long     ,						device=A_       )
                _lowerCAmelCase   :					int        =    torch.zeros(
                    self.model_tester.batch_size     ,						dtype=torch.long     ,						device=A_       )
           elif model_class in [
               *get_values(A_       ),
           ]:
                _lowerCAmelCase   :					Any        =    torch.zeros(
                    self.model_tester.batch_size     ,						dtype=torch.long     ,						device=A_       )
           elif model_class in [
               *get_values(A_       ),
           ]:
                _lowerCAmelCase   :					Optional[Any]        =    torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,						dtype=torch.long     ,						device=A_     ,						)

      return inputs_dict

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _lowerCAmelCase   :					Dict        =    type
           self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A_       )

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






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A_       )

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A_       )


 @slow
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					List[str]        =    LayoutLMvaModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )







def 	_snake_case		(    )       ->      Union[str, Any]:

     """simple docstring"""







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





@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @cached_property
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      return LayoutLMvaImageProcessor(apply_ocr=A_       ) if is_vision_available() else None


 @slow
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"       ).to(A_       )

      _lowerCAmelCase   :					str        =    self.default_image_processor
      _lowerCAmelCase   :					Any        =    prepare_img()
      _lowerCAmelCase   :					Dict        =    image_processor(images=A_     ,						return_tensors="pt"       ).pixel_values.to(A_       )

      _lowerCAmelCase   :					str        =    torch.tensor([[1, 2]]       )
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

      # forward pass
      _lowerCAmelCase   :					Union[str, Any]        =    model(
          input_ids=input_ids.to(A_       )     ,						bbox=bbox.to(A_       )     ,						pixel_values=pixel_values.to(A_       )     ,						)

      # verify the logits
      _lowerCAmelCase   :					Tuple        =    torch.Size((1, 1_9_9, 7_6_8)       )
      self.assertEqual(outputs.last_hidden_state.shape     ,						A_       )

      _lowerCAmelCase   :					Any        =    torch.tensor(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       ).to(A_       )

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









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     # Display within reasonable bounds
     _lowerCAmelCase   :					Tuple        =    get_bounds(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     plt.ylim(max([-80, bounds[0]]				)		,							min([80, bounds[1]]				)				)
     plt.ylabel("Gain (dB)"				)

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     plt.ylim(-2 * pi		,							2 * pi				)
     plt.ylabel("Phase shift (Radians)"				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE		,							-2 * pi				)				)
     plt.show()






