



"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     List[Any]				,		__A:     List[str]				,		__A:     List[Any]=13				,		__A:     Dict=7				,		__A:     Any=True				,		__A:     Tuple=True				,		__A:     str=True				,		__A:     Union[str, Any]=True				,		__A:     str=99				,		__A:     Optional[Any]=32				,		__A:     Optional[Any]=2				,		__A:     Tuple=4				,		__A:     Optional[Any]=37				,		__A:     Tuple="gelu"				,		__A:     Any=0.1				,		__A:     str=0.1				,		__A:     Dict=512				,		__A:     int=16				,		__A:     Tuple=2				,		__A:     int=0.0_2				,		__A:     Union[str, Any]=3				,		__A:     str=4				,		__A:     Dict=None				,		__A:     Dict=1000				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_input_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_labels
												a__          =  num_choices
												a__          =  scope
												a__          =  range_bbox




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												# convert bbox to numpy since TF does not support item assignment
												a__          =  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]:
																											a__          =  bbox[i, j, 3]
																											a__          =  bbox[i, j, 1]
																											a__          =  t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											a__          =  bbox[i, j, 2]
																											a__          =  bbox[i, j, 0]
																											a__          =  t
												a__          =  tf.convert_to_tensor(UpperCAmelCase__    )

												a__          =  None
												if self.use_input_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

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

												a__          =  None
												a__          =  None
												a__          =  None
												if self.use_labels:
																	a__          =  ids_tensor([self.batch_size]				,		self.type_sequence_label_size    )
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels    )
																	a__          =  ids_tensor([self.batch_size]				,		self.num_choices    )

												a__          =  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  lowercase		(   self:     int				,		__A:     Tuple				,		__A:     Optional[Any]				,		__A:     Dict				,		__A:     Tuple				,		__A:     List[Any]				,		__A:     Tuple				,		__A:     List[Any]				,		__A:     int    ):

												'''simple docstring'''

												a__          =  TFLayoutLMModel(config=UpperCAmelCase__    )

												a__          =  model(UpperCAmelCase__				,		UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__    )
												a__          =  model(UpperCAmelCase__				,		UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__    )
												a__          =  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:     List[str]				,		__A:     str				,		__A:     Union[str, Any]				,		__A:     Any				,		__A:     int				,		__A:     Optional[int]				,		__A:     List[str]				,		__A:     Optional[int]				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TFLayoutLMForMaskedLM(config=UpperCAmelCase__    )

												a__          =  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:     Dict				,		__A:     int				,		__A:     Tuple				,		__A:     Optional[int]				,		__A:     str				,		__A:     int				,		__A:     Any				,		__A:     Any				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  TFLayoutLMForSequenceClassification(config=UpperCAmelCase__    )

												a__          =  model(UpperCAmelCase__				,		UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )




							def  lowercase		(   self:     Tuple				,		__A:     List[Any]				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     Dict				,		__A:     str				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  TFLayoutLMForTokenClassification(config=UpperCAmelCase__    )

												a__          =  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:     Optional[Any]				,		__A:     Union[str, Any]				,		__A:     int				,		__A:     Any				,		__A:     Dict				,		__A:     List[Any]				,		__A:     Optional[int]				,		__A:     int				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  TFLayoutLMForQuestionAnswering(config=UpperCAmelCase__    )

												a__          =  model(UpperCAmelCase__				,		UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=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:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												(
												    a__	
												)          =  config_and_inputs
												a__          =  {
												    '''input_ids''': input_ids,
												    '''bbox''': bbox,
												    '''token_type_ids''': token_type_ids,
												    '''attention_mask''': input_mask,
												}
												return config, inputs_dict







@require_tf
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(
							    (
							        TFLayoutLMModel,
							        TFLayoutLMForMaskedLM,
							        TFLayoutLMForTokenClassification,
							        TFLayoutLMForSequenceClassification,
							        TFLayoutLMForQuestionAnswering,
							    )
							    if is_tf_available()
							    else ()
							)
							_SCREAMING_SNAKE_CASE    =(
							    {
							        'feature-extraction': TFLayoutLMModel,
							        'fill-mask': TFLayoutLMForMaskedLM,
							        'text-classification': TFLayoutLMForSequenceClassification,
							        'token-classification': TFLayoutLMForTokenClassification,
							        'zero-shot': TFLayoutLMForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =10




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  TFLayoutLMModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=UpperCAmelCase__				,		hidden_size=37    )




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCAmelCase__    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__    )




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__    )




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

												'''simple docstring'''

												for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  TFLayoutLMModel.from_pretrained(UpperCAmelCase__    )
																	self.assertIsNotNone(UpperCAmelCase__    )





							@unittest.skip('''Onnx compliancy broke with TF 2.10'''    )
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  tf.convert_to_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]])  # noqa: E231
					a__          =  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
					a__          =  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
					a__          =  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)
					a__          =  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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

												a__          =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__          =  model(input_ids=UpperCAmelCase__				,		bbox=UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__    )

												# test the sequence output on [0, :3, :3]
												a__          =  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]				,		UpperCAmelCase__				,		atol=1e-3    )    )

												# test the pooled output on [1, :3]
												a__          =  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]				,		UpperCAmelCase__				,		atol=1e-3    )    )




							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				,		num_labels=2    )

												a__          =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__          =  model(
												    input_ids=UpperCAmelCase__				,		bbox=UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__				,		labels=tf.convert_to_tensor([1, 1]    )				,		)

												# test whether we get a loss as a scalar
												a__          =  outputs.loss
												a__          =  (2,)
												self.assertEqual(loss.shape				,		UpperCAmelCase__    )

												# test the shape of the logits
												a__          =  outputs.logits
												a__          =  (2, 2)
												self.assertEqual(logits.shape				,		UpperCAmelCase__    )




							@slow
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''				,		num_labels=13    )

												a__          =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__          =  model(
												    input_ids=UpperCAmelCase__				,		bbox=UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__				,		labels=UpperCAmelCase__    )

												# test the shape of the logits
												a__          =  outputs.logits
												a__          =  tf.convert_to_tensor((2, 25, 13)    )
												self.assertEqual(logits.shape				,		UpperCAmelCase__    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

												a__          =  prepare_layoutlm_batch_inputs()

												# forward pass
												a__          =  model(input_ids=UpperCAmelCase__				,		bbox=UpperCAmelCase__				,		attention_mask=UpperCAmelCase__				,		token_type_ids=UpperCAmelCase__    )

												# test the shape of the logits
												a__          =  tf.convert_to_tensor((2, 25)    )
												self.assertEqual(outputs.start_logits.shape				,		UpperCAmelCase__    )
												self.assertEqual(outputs.end_logits.shape				,		UpperCAmelCase__    )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


