
from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.layoutlm.modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMModel,
      )





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=9_9    ,  __magic_name__=3_2    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=None    ,  __magic_name__=1_0_0_0    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      parent
       lowerCamelCase		:	List[Any]							=      batch_size
       lowerCamelCase		:	List[Any]							=      seq_length
       lowerCamelCase		:	Union[str, Any]							=      is_training
       lowerCamelCase		:	Union[str, Any]							=      use_input_mask
       lowerCamelCase		:	str							=      use_token_type_ids
       lowerCamelCase		:	Optional[int]							=      use_labels
       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	List[str]							=      hidden_size
       lowerCamelCase		:	Tuple							=      num_hidden_layers
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	str							=      intermediate_size
       lowerCamelCase		:	str							=      hidden_act
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	List[Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	List[Any]							=      max_position_embeddings
       lowerCamelCase		:	int							=      type_vocab_size
       lowerCamelCase		:	Dict							=      type_sequence_label_size
       lowerCamelCase		:	Optional[int]							=      initializer_range
       lowerCamelCase		:	str							=      num_labels
       lowerCamelCase		:	Dict							=      num_choices
       lowerCamelCase		:	List[str]							=      scope
       lowerCamelCase		:	Optional[int]							=      range_bbox
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

       # convert bbox to numpy since TF does not support item assignment
       lowerCamelCase		:	int							=      ids_tensor([self.batch_size, self.seq_length, 4]    ,  self.range_bbox      ).numpy()
       # 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		:	Dict							=      bbox[i, j, 3]
          lowerCamelCase		:	Any							=      bbox[i, j, 1]
          lowerCamelCase		:	str							=      t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          lowerCamelCase		:	Union[str, Any]							=      bbox[i, j, 2]
          lowerCamelCase		:	Tuple							=      bbox[i, j, 0]
          lowerCamelCase		:	Optional[Any]							=      t
       lowerCamelCase		:	str							=      tf.convert_to_tensor(__magic_name__      )

       lowerCamelCase		:	int							=      None
       if self.use_input_mask:
        lowerCamelCase		:	List[str]							=      random_attention_mask([self.batch_size, self.seq_length]      )

       lowerCamelCase		:	Optional[int]							=      None
       if self.use_token_type_ids:
        lowerCamelCase		:	List[str]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size      )

       lowerCamelCase		:	Optional[int]							=      None
       lowerCamelCase		:	Optional[int]							=      None
       lowerCamelCase		:	int							=      None
       if self.use_labels:
        lowerCamelCase		:	str							=      ids_tensor([self.batch_size]    ,  self.type_sequence_label_size      )
        lowerCamelCase		:	int							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels      )
        lowerCamelCase		:	Tuple							=      ids_tensor([self.batch_size]    ,  self.num_choices      )

       lowerCamelCase		:	str							=      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    ,  )

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      TFLayoutLMModel(config=__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      model(__magic_name__    ,  __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__      )
       lowerCamelCase		:	Tuple							=      model(__magic_name__    ,  __magic_name__    ,  token_type_ids=__magic_name__      )
       lowerCamelCase		:	Any							=      model(__magic_name__    ,  __magic_name__      )
       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 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Dict							=      TFLayoutLMForMaskedLM(config=__magic_name__      )

       lowerCamelCase		:	Tuple							=      model(__magic_name__    ,  __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Tuple							=      self.num_labels
       lowerCamelCase		:	List[str]							=      TFLayoutLMForSequenceClassification(config=__magic_name__      )

       lowerCamelCase		:	Dict							=      model(__magic_name__    ,  __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Any							=      self.num_labels
       lowerCamelCase		:	Dict							=      TFLayoutLMForTokenClassification(config=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      model(__magic_name__    ,  __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	List[str]							=      TFLayoutLMForQuestionAnswering(config=__magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      model(__magic_name__    ,  __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__      )
       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 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    (
           lowerCamelCase
       )			,    
       )		:	Any							=      config_and_inputs
       lowerCamelCase		:	Any							=      {
           """input_ids""": input_ids,
           """bbox""": bbox,
           """token_type_ids""": token_type_ids,
           """attention_mask""": input_mask,
       }
       return config, inputs_dict





@require_tf
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    List[str]           =			(
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      _UpperCAmelCase				:    str           =			(
          {
              """feature-extraction""": TFLayoutLMModel,
              """fill-mask""": TFLayoutLMForMaskedLM,
              """text-classification""": TFLayoutLMForSequenceClassification,
              """token-classification""": TFLayoutLMForTokenClassification,
              """zero-shot""": TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _UpperCAmelCase				:    Dict           =			False
      _UpperCAmelCase				:    Tuple           =			True
      _UpperCAmelCase				:    Union[str, Any]           =			10
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      TFLayoutLMModelTester(self      )
       lowerCamelCase		:	str							=      ConfigTester(self    ,  config_class=__magic_name__    ,  hidden_size=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__magic_name__      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:	str							=      TFLayoutLMModel.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )






      @unittest.skip("""Onnx compliancy broke with TF 2.10"""      )
      def 						UpperCamelCase__							(      self      ):
       pass





def     _a					(						):
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCamelCase		:	int							=      tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]							)  # noqa: E231
 lowerCamelCase		:	Union[str, Any]							=      tf.convert_to_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],]							)  # noqa: E231
 lowerCamelCase		:	Optional[int]							=      tf.convert_to_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]]]							)  # noqa: E231
 lowerCamelCase		:	Optional[int]							=      tf.convert_to_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]]							)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 lowerCamelCase		:	str							=      tf.convert_to_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]]							)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""      )

       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      prepare_layoutlm_batch_inputs()

       # forward pass
       lowerCamelCase		:	List[str]							=      model(input_ids=__magic_name__    ,  bbox=__magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__      )

       # test the sequence output on [0, :3, :3]
       lowerCamelCase		:	List[str]							=      tf.convert_to_tensor(
           [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]    ,  )

       self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,  __magic_name__    ,  atol=1e-3      )      )

       # test the pooled output on [1, :3]
       lowerCamelCase		:	List[Any]							=      tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]      )

       self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,  __magic_name__    ,  atol=1e-3      )      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       # initialize model with randomly initialized sequence classification head
       lowerCamelCase		:	Union[str, Any]							=      TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,  num_labels=2      )

       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[str]							=      prepare_layoutlm_batch_inputs()

       # forward pass
       lowerCamelCase		:	Union[str, Any]							=      model(
           input_ids=__magic_name__    ,  bbox=__magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=tf.convert_to_tensor([1, 1]      )    ,  )

       # test whether we get a loss as a scalar
       lowerCamelCase		:	List[str]							=      outputs.loss
       lowerCamelCase		:	Dict							=      (2,)
       self.assertEqual(loss.shape    ,  __magic_name__      )

       # test the shape of the logits
       lowerCamelCase		:	int							=      outputs.logits
       lowerCamelCase		:	str							=      (2, 2)
       self.assertEqual(logits.shape    ,  __magic_name__      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       # initialize model with randomly initialized token classification head
       lowerCamelCase		:	int							=      TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,  num_labels=1_3      )

       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Tuple							=      prepare_layoutlm_batch_inputs()

       # forward pass
       lowerCamelCase		:	int							=      model(
           input_ids=__magic_name__    ,  bbox=__magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__    ,  labels=__magic_name__      )

       # test the shape of the logits
       lowerCamelCase		:	Optional[int]							=      outputs.logits
       lowerCamelCase		:	Optional[Any]							=      tf.convert_to_tensor((2, 2_5, 1_3)      )
       self.assertEqual(logits.shape    ,  __magic_name__      )






      @slow
      def 						UpperCamelCase__							(      self      ):
       # initialize model with randomly initialized token classification head
       lowerCamelCase		:	Optional[Any]							=      TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""      )

       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      prepare_layoutlm_batch_inputs()

       # forward pass
       lowerCamelCase		:	Dict							=      model(input_ids=__magic_name__    ,  bbox=__magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__      )

       # test the shape of the logits
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor((2, 2_5)      )
       self.assertEqual(outputs.start_logits.shape    ,  __magic_name__      )
       self.assertEqual(outputs.end_logits.shape    ,  __magic_name__      )






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       warnings.warn(
           """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
           """ use OwlViTImageProcessor instead."""    ,  __magic_name__    ,  )
       super().__init__(*__magic_name__    ,  **__magic_name__      )




