'''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 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=2    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=None    ,							lowercase__=1_000    ,							)							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       parent
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			Dict    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			List[str]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			str    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			str    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			str    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			List[str]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			List[str]    	=       type_vocab_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       type_sequence_label_size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_labels
         SCREAMING_SNAKE_CASE					:			int    	=       num_choices
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scope
         SCREAMING_SNAKE_CASE					:			Any    	=       range_bbox






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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					:			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]:
                        SCREAMING_SNAKE_CASE					:			Dict    	=       bbox[i, j, 3]
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       bbox[i, j, 1]
                        SCREAMING_SNAKE_CASE					:			Dict    	=       t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bbox[i, j, 2]
                        SCREAMING_SNAKE_CASE					:			Any    	=       bbox[i, j, 0]
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       t
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.convert_to_tensor(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       random_attention_mask([self.batch_size, self.seq_length] )

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

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

         SCREAMING_SNAKE_CASE					:			int    	=       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    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLayoutLMModel(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__    ,							token_type_ids=lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(lowercase__    ,							lowercase__ )
         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    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       TFLayoutLMForMaskedLM(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.vocab_size) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			Tuple    	=       TFLayoutLMForSequenceClassification(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			int    	=       TFLayoutLMForTokenClassification(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			str    	=       TFLayoutLMForQuestionAnswering(config=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__    ,							lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )
         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 )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			int    	=       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					:			Optional[Any]    	=       {
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict






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




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						(
        (
            TFLayoutLMModel,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForTokenClassification,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForQuestionAnswering,
        )
        if is_tf_available()
        else ()
    )
    snake_case__							:   str							=						(
        {
            "feature-extraction": TFLayoutLMModel,
            "fill-mask": TFLayoutLMForMaskedLM,
            "text-classification": TFLayoutLMForSequenceClassification,
            "token-classification": TFLayoutLMForTokenClassification,
            "zero-shot": TFLayoutLMForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case__							:   Union[str, Any]							=						False
    snake_case__							:   List[Any]							=						True
    snake_case__							:   int							=						1_0






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       TFLayoutLMModelTester(self )
         SCREAMING_SNAKE_CASE					:			Any    	=       ConfigTester(self    ,							config_class=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> str:
         self.config_tester.run_common_tests()






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       TFLayoutLMModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )






    @unittest.skip('Onnx compliancy broke with TF 2.10' )
    def       _UpperCamelCase    (       self )							-> Tuple:
         pass






def     __lowerCAmelCase						(       )      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       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					:			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					:			str    	=       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					:			Any    	=       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					:			List[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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       TFLayoutLMModel.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					:			Optional[int]    	=       model(input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__ )

         # test the sequence output on [0, :3, :3]
         SCREAMING_SNAKE_CASE					:			int    	=       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]    ,							lowercase__    ,							atol=1E-3 ) )

         # test the pooled output on [1, :3]
         SCREAMING_SNAKE_CASE					:			int    	=       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]    ,							lowercase__    ,							atol=1E-3 ) )






    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         # initialize model with randomly initialized sequence classification head
         SCREAMING_SNAKE_CASE					:			int    	=       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					:			List[str]    	=       model(
             input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=tf.convert_to_tensor([1, 1] )    ,							)

         # test whether we get a loss as a scalar
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       outputs.loss
         SCREAMING_SNAKE_CASE					:			int    	=       (2,)
         self.assertEqual(loss.shape    ,							lowercase__ )

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			Dict    	=       outputs.logits
         SCREAMING_SNAKE_CASE					:			Any    	=       (2, 2)
         self.assertEqual(logits.shape    ,							lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> str:
         # initialize model with randomly initialized token classification head
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,							num_labels=13 )

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       prepare_layoutlm_batch_inputs()

         # forward pass
         SCREAMING_SNAKE_CASE					:			int    	=       model(
             input_ids=lowercase__    ,							bbox=lowercase__    ,							attention_mask=lowercase__    ,							token_type_ids=lowercase__    ,							labels=lowercase__ )

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tf.convert_to_tensor((2, 25, 13) )
         self.assertEqual(logits.shape    ,							lowercase__ )






    @slow
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         # initialize model with randomly initialized token classification head
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )

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

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

         # test the shape of the logits
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tf.convert_to_tensor((2, 25) )
         self.assertEqual(outputs.start_logits.shape    ,							lowercase__ )
         self.assertEqual(outputs.end_logits.shape    ,							lowercase__ )




'''simple docstring'''





import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



