





"""simple docstring"""





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      snake_case						:

      def __init__(     self  :Tuple		,				_lowerCamelCase  :List[str]		,				_lowerCamelCase  :Any=1_3		,				_lowerCamelCase  :Any=7		,				_lowerCamelCase  :Optional[Any]=True		,				_lowerCamelCase  :Tuple=True		,				_lowerCamelCase  :Optional[int]=True		,				_lowerCamelCase  :List[str]=True		,				_lowerCamelCase  :Optional[int]=9_9		,				_lowerCamelCase  :int=3_2		,				_lowerCamelCase  :List[Any]=2		,				_lowerCamelCase  :Optional[int]=4		,				_lowerCamelCase  :Union[str, Any]=3_7		,				_lowerCamelCase  :List[str]="gelu"		,				_lowerCamelCase  :Optional[int]=0.1		,				_lowerCamelCase  :Any=0.1		,				_lowerCamelCase  :List[Any]=5_1_2		,				_lowerCamelCase  :List[str]=1_6		,				_lowerCamelCase  :Optional[Any]=2		,				_lowerCamelCase  :int=0.0_2		,				_lowerCamelCase  :str=3		,				_lowerCamelCase  :Union[str, Any]=4		,				_lowerCamelCase  :str=None		,				_lowerCamelCase  :Dict=1_0_0_0		,				):
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			parent
       __SCREAMING_SNAKE_CASE					:     Any   				=			batch_size
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			seq_length
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			is_training
       __SCREAMING_SNAKE_CASE					:     Dict   				=			use_input_mask
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			use_token_type_ids
       __SCREAMING_SNAKE_CASE					:     str   				=			use_labels
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			vocab_size
       __SCREAMING_SNAKE_CASE					:     Dict   				=			hidden_size
       __SCREAMING_SNAKE_CASE					:     Any   				=			num_hidden_layers
       __SCREAMING_SNAKE_CASE					:     Any   				=			num_attention_heads
       __SCREAMING_SNAKE_CASE					:     int   				=			intermediate_size
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			hidden_act
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			hidden_dropout_prob
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			max_position_embeddings
       __SCREAMING_SNAKE_CASE					:     int   				=			type_vocab_size
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			type_sequence_label_size
       __SCREAMING_SNAKE_CASE					:     Dict   				=			initializer_range
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			num_labels
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			num_choices
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			scope
       __SCREAMING_SNAKE_CASE					:     int   				=			range_bbox

      def 		SCREAMING_SNAKE_CASE_     (     self  :List[str]		):
       __SCREAMING_SNAKE_CASE					:     str   				=			ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size		)

       # convert bbox to numpy since TF does not support item assignment
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			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]:
          __SCREAMING_SNAKE_CASE					:     Tuple   				=			bbox[i, j, 3]
          __SCREAMING_SNAKE_CASE					:     Any   				=			bbox[i, j, 1]
          __SCREAMING_SNAKE_CASE					:     List[str]   				=			t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          __SCREAMING_SNAKE_CASE					:     int   				=			bbox[i, j, 2]
          __SCREAMING_SNAKE_CASE					:     List[str]   				=			bbox[i, j, 0]
          __SCREAMING_SNAKE_CASE					:     List[str]   				=			t
       __SCREAMING_SNAKE_CASE					:     Any   				=			tf.convert_to_tensor(_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     str   				=			None
       if self.use_input_mask:
        __SCREAMING_SNAKE_CASE					:     Any   				=			random_attention_mask([self.batch_size, self.seq_length]		)

       __SCREAMING_SNAKE_CASE					:     int   				=			None
       if self.use_token_type_ids:
        __SCREAMING_SNAKE_CASE					:     Dict   				=			ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size		)

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

       __SCREAMING_SNAKE_CASE					:     Dict   				=			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 		SCREAMING_SNAKE_CASE_     (     self  :Tuple		,				_lowerCamelCase  :Tuple		,				_lowerCamelCase  :Optional[Any]		,				_lowerCamelCase  :Any		,				_lowerCamelCase  :Union[str, Any]		,				_lowerCamelCase  :str		,				_lowerCamelCase  :List[Any]		,				_lowerCamelCase  :Any		,				_lowerCamelCase  :str		):
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			TFLayoutLMModel(config=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		)
       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 		SCREAMING_SNAKE_CASE_     (     self  :Union[str, Any]		,				_lowerCamelCase  :List[str]		,				_lowerCamelCase  :int		,				_lowerCamelCase  :Optional[Any]		,				_lowerCamelCase  :int		,				_lowerCamelCase  :Tuple		,				_lowerCamelCase  :Dict		,				_lowerCamelCase  :Tuple		,				_lowerCamelCase  :Dict		):
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			TFLayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE		)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Union[str, Any]		,				_lowerCamelCase  :List[Any]		,				_lowerCamelCase  :Optional[Any]		,				_lowerCamelCase  :str		,				_lowerCamelCase  :List[str]		,				_lowerCamelCase  :Optional[int]		,				_lowerCamelCase  :Tuple		,				_lowerCamelCase  :Union[str, Any]		,				_lowerCamelCase  :List[Any]		):
       __SCREAMING_SNAKE_CASE					:     Any   				=			self.num_labels
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			TFLayoutLMForSequenceClassification(config=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :str		,				_lowerCamelCase  :List[Any]		,				_lowerCamelCase  :str		,				_lowerCamelCase  :List[str]		,				_lowerCamelCase  :Optional[Any]		,				_lowerCamelCase  :List[Any]		,				_lowerCamelCase  :Any		,				_lowerCamelCase  :Union[str, Any]		,				_lowerCamelCase  :List[Any]		):
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			self.num_labels
       __SCREAMING_SNAKE_CASE					:     int   				=			TFLayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE		)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[int]		,				_lowerCamelCase  :Any		,				_lowerCamelCase  :str		,				_lowerCamelCase  :List[str]		,				_lowerCamelCase  :Dict		,				_lowerCamelCase  :int		,				_lowerCamelCase  :Dict		,				_lowerCamelCase  :int		,				_lowerCamelCase  :Union[str, Any]		):
       __SCREAMING_SNAKE_CASE					:     Dict   				=			TFLayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			model(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)
       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 		SCREAMING_SNAKE_CASE_     (     self  :Optional[Any]		):
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			self.prepare_config_and_inputs()
       (
           (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				, (
           __SCREAMING_SNAKE_CASE   
       )				,    
       )					:     List[str]   				=			config_and_inputs
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			{
           '''input_ids''': input_ids,
           '''bbox''': bbox,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict







@require_tf
class      snake_case						(  a_					,	a_					,	unittest.TestCase  ):
      lowerCamelCase__         =					(
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      lowerCamelCase__         =					(
          {
              '''feature-extraction''': TFLayoutLMModel,
              '''fill-mask''': TFLayoutLMForMaskedLM,
              '''text-classification''': TFLayoutLMForSequenceClassification,
              '''token-classification''': TFLayoutLMForTokenClassification,
              '''zero-shot''': TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase__         =					False
      lowerCamelCase__         =					True
      lowerCamelCase__         =					10

      def 		SCREAMING_SNAKE_CASE_     (     self  :Union[str, Any]		):
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			TFLayoutLMModelTester(self		)
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				hidden_size=3_7		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :str		):
       self.config_tester.run_common_tests()

      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[Any]		):
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Any		):
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[Any]		):
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Any		):
       __SCREAMING_SNAKE_CASE					:     Dict   				=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :int		):
       __SCREAMING_SNAKE_CASE					:     Any   				=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE		)

      @slow
      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[Any]		):

       for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __SCREAMING_SNAKE_CASE					:     str   				=			TFLayoutLMModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
        self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



      @unittest.skip('''Onnx compliancy broke with TF 2.10'''		)
      def 		SCREAMING_SNAKE_CASE_     (     self  :str		):
       pass







def 						lowerCAmelCase_      (    ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE					:     Optional[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
 __SCREAMING_SNAKE_CASE					:     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
 __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			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
 __SCREAMING_SNAKE_CASE					:     List[str]   				=			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)
 __SCREAMING_SNAKE_CASE					:     Any   				=			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      snake_case						(  unittest.TestCase  ):

      @slow
      def 		SCREAMING_SNAKE_CASE_     (     self  :Dict		):
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''		)

       __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE					:     str   				=			prepare_layoutlm_batch_inputs()

       # forward pass
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			model(input_ids=_SCREAMING_SNAKE_CASE		,				bbox=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)

       # test the sequence output on [0, :3, :3]
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			tf.convert_to_tensor(
           [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]		,				)

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

       # test the pooled output on [1, :3]
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]		)

       self.assertTrue(np.allclose(outputs.pooler_output[1, :3]		,				_SCREAMING_SNAKE_CASE		,				atol=1e-3		)		)

      @slow
      def 		SCREAMING_SNAKE_CASE_     (     self  :Union[str, Any]		):
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,				num_labels=2		)

       __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE				, __SCREAMING_SNAKE_CASE					:     str   				=			prepare_layoutlm_batch_inputs()

       # forward pass
       __SCREAMING_SNAKE_CASE					:     str   				=			model(
           input_ids=_SCREAMING_SNAKE_CASE		,				bbox=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		,				labels=tf.convert_to_tensor([1, 1]		)		,				)

       # test whether we get a loss as a scalar
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			outputs.loss
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			(2,)
       self.assertEqual(loss.shape		,				_SCREAMING_SNAKE_CASE		)

       # test the shape of the logits
       __SCREAMING_SNAKE_CASE					:     int   				=			outputs.logits
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			(2, 2)
       self.assertEqual(logits.shape		,				_SCREAMING_SNAKE_CASE		)

      @slow
      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[int]		):
       __SCREAMING_SNAKE_CASE					:     Union[str, Any]   				=			TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,				num_labels=1_3		)

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

       # forward pass
       __SCREAMING_SNAKE_CASE					:     Dict   				=			model(
           input_ids=_SCREAMING_SNAKE_CASE		,				bbox=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE		)

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



      @slow
      def 		SCREAMING_SNAKE_CASE_     (     self  :Dict		):
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''		)

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

       # forward pass
       __SCREAMING_SNAKE_CASE					:     Dict   				=			model(input_ids=_SCREAMING_SNAKE_CASE		,				bbox=_SCREAMING_SNAKE_CASE		,				attention_mask=_SCREAMING_SNAKE_CASE		,				token_type_ids=_SCREAMING_SNAKE_CASE		)

       # test the shape of the logits
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			tf.convert_to_tensor((2, 2_5)		)
       self.assertEqual(outputs.start_logits.shape		,				_SCREAMING_SNAKE_CASE		)
       self.assertEqual(outputs.end_logits.shape		,				_SCREAMING_SNAKE_CASE		)




'''simple docstring'''



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



@slow
@require_flax
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([canny_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'Chef in the kitchen'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([pose_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





