
from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     42



class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case							):





				@register_to_config
				def __init__(		self :		Dict					,      snake_case_ :		int = 16					,      snake_case_ :		int = 88					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		int = 1					,      snake_case_ :		float = 0.0					,      snake_case_ :		int = 32					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		bool = False					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		str = "geglu"					,      snake_case_ :		Optional[int] = None					,      snake_case_ :		bool = False					,      snake_case_ :		bool = False					,      snake_case_ :		bool = False					,      snake_case_ :		str = "layer_norm"					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								super().__init__()
								A     :  List[Any]   =				use_linear_projection
								A     :  Union[str, Any]   =				num_attention_heads
								A     :  Optional[Any]   =				attention_head_dim
								A     :  Union[str, Any]   =				num_attention_heads * attention_head_dim

								# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
								# Define whether input is continuous or discrete depending on configuration
								A     :  int   =				(in_channels is not None) and (patch_size is None)
								A     :  Any   =				num_vector_embeds is not None
								A     :  Optional[Any]   =				in_channels is not None and patch_size is not None

								if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
												A     :  Tuple   =				(
												    f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
												    ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
												    ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
												    ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
												    ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
												)
												deprecate('''norm_type!=num_embeds_ada_norm'''					,      '''1.0.0'''					,      snake_case_					,      standard_warn=snake_case_							)
												A     :  Dict   =				'''ada_norm'''

								if self.is_input_continuous and self.is_input_vectorized:
												raise ValueError(
												    f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
												    ''' sure that either `in_channels` or `num_vector_embeds` is None.'''							)
								elif self.is_input_vectorized and self.is_input_patches:
												raise ValueError(
												    f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
												    ''' sure that either `num_vector_embeds` or `num_patches` is None.'''							)
								elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
												raise ValueError(
												    f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
												    f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""							)

								# 2. Define input layers
								if self.is_input_continuous:
												A     :  Optional[int]   =				in_channels

												A     :  Dict   =				torch.nn.GroupNorm(num_groups=snake_case_					,      num_channels=snake_case_					,      eps=1E-6					,      affine=snake_case_							)
												if use_linear_projection:
																A     :  Optional[int]   =				nn.Linear(snake_case_					,      snake_case_							)
												else:
																A     :  Dict   =				nn.Convad(snake_case_					,      snake_case_					,      kernel_size=1					,      stride=1					,      padding=0							)
								elif self.is_input_vectorized:
												assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
												assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

												A     :  Tuple   =				sample_size
												A     :  Union[str, Any]   =				sample_size
												A     :  List[Any]   =				num_vector_embeds
												A     :  str   =				self.height * self.width

												A     :  int   =				ImagePositionalEmbeddings(
												    num_embed=snake_case_					,      embed_dim=snake_case_					,      height=self.height					,      width=self.width							)
								elif self.is_input_patches:
												assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

												A     :  Optional[Any]   =				sample_size
												A     :  Optional[Any]   =				sample_size

												A     :  Optional[int]   =				patch_size
												A     :  str   =				PatchEmbed(
												    height=snake_case_					,      width=snake_case_					,      patch_size=snake_case_					,      in_channels=snake_case_					,      embed_dim=snake_case_					,      )

								# 3. Define transformers blocks
								A     :  Any   =				nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            snake_case_					,      snake_case_					,      snake_case_					,      dropout=snake_case_					,      cross_attention_dim=snake_case_					,      activation_fn=snake_case_					,      num_embeds_ada_norm=snake_case_					,      attention_bias=snake_case_					,      only_cross_attention=snake_case_					,      upcast_attention=snake_case_					,      norm_type=snake_case_					,      norm_elementwise_affine=snake_case_					,      )
								        for d in range(snake_case_							)
								    ]							)

								# 4. Define output layers
								A     :  Dict   =				in_channels if out_channels is None else out_channels
								if self.is_input_continuous:
												# TODO: should use out_channels for continuous projections
												if use_linear_projection:
																A     :  Optional[Any]   =				nn.Linear(snake_case_					,      snake_case_							)
												else:
																A     :  str   =				nn.Convad(snake_case_					,      snake_case_					,      kernel_size=1					,      stride=1					,      padding=0							)
								elif self.is_input_vectorized:
												A     :  List[str]   =				nn.LayerNorm(snake_case_							)
												A     :  Tuple   =				nn.Linear(snake_case_					,      self.num_vector_embeds - 1							)
								elif self.is_input_patches:
												A     :  Tuple   =				nn.LayerNorm(snake_case_					,      elementwise_affine=snake_case_					,      eps=1E-6							)
												A     :  List[Any]   =				nn.Linear(snake_case_					,      2 * inner_dim							)
												A     :  str   =				nn.Linear(snake_case_					,      patch_size * patch_size * self.out_channels							)






				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		torch.Tensor					,      snake_case_ :		Optional[torch.Tensor] = None					,      snake_case_ :		Optional[torch.LongTensor] = None					,      snake_case_ :		Optional[torch.LongTensor] = None					,      snake_case_ :		Dict[str, Any] = None					,      snake_case_ :		Optional[torch.Tensor] = None					,      snake_case_ :		Optional[torch.Tensor] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								if attention_mask is not None and attention_mask.ndim == 2:
												# assume that mask is expressed as:
												#   (1 = keep,      0 = discard)
												# convert mask into a bias that can be added to attention scores:
												#       (keep = +0,     discard = -10000.0)
												A     :  Optional[Any]   =				(1 - attention_mask.to(hidden_states.dtype							)) * -1_00_00.0
												A     :  int   =				attention_mask.unsqueeze(1							)

								# convert encoder_attention_mask to a bias the same way we do for attention_mask
								if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
												A     :  Dict   =				(1 - encoder_attention_mask.to(hidden_states.dtype							)) * -1_00_00.0
												A     :  Optional[int]   =				encoder_attention_mask.unsqueeze(1							)

								# 1. Input
								if self.is_input_continuous:
												A ,       A ,       A ,       A     :  Tuple   =				hidden_states.shape
												A     :  Tuple   =				hidden_states

												A     :  Optional[Any]   =				self.norm(snake_case_							)
												if not self.use_linear_projection:
																A     :  Optional[int]   =				self.proj_in(snake_case_							)
																A     :  Dict   =				hidden_states.shape[1]
																A     :  Optional[Any]   =				hidden_states.permute(0					,      2					,      3					,      1							).reshape(snake_case_					,      height * width					,      snake_case_							)
												else:
																A     :  Any   =				hidden_states.shape[1]
																A     :  Any   =				hidden_states.permute(0					,      2					,      3					,      1							).reshape(snake_case_					,      height * width					,      snake_case_							)
																A     :  List[Any]   =				self.proj_in(snake_case_							)
								elif self.is_input_vectorized:
												A     :  List[str]   =				self.latent_image_embedding(snake_case_							)
								elif self.is_input_patches:
												A     :  Dict   =				self.pos_embed(snake_case_							)

								# 2. Blocks
								for block in self.transformer_blocks:
												A     :  Optional[int]   =				block(
												    snake_case_					,      attention_mask=snake_case_					,      encoder_hidden_states=snake_case_					,      encoder_attention_mask=snake_case_					,      timestep=snake_case_					,      cross_attention_kwargs=snake_case_					,      class_labels=snake_case_					,      )

								# 3. Output
								if self.is_input_continuous:
												if not self.use_linear_projection:
																A     :  Optional[int]   =				hidden_states.reshape(snake_case_					,      snake_case_					,      snake_case_					,      snake_case_							).permute(0					,      3					,      1					,      2							).contiguous()
																A     :  Optional[Any]   =				self.proj_out(snake_case_							)
												else:
																A     :  Any   =				self.proj_out(snake_case_							)
																A     :  int   =				hidden_states.reshape(snake_case_					,      snake_case_					,      snake_case_					,      snake_case_							).permute(0					,      3					,      1					,      2							).contiguous()

												A     :  List[str]   =				hidden_states + residual
								elif self.is_input_vectorized:
												A     :  List[Any]   =				self.norm_out(snake_case_							)
												A     :  List[Any]   =				self.out(snake_case_							)
												# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
												A     :  Optional[Any]   =				logits.permute(0					,      2					,      1							)

												# log(p(x_0))
												A     :  List[Any]   =				F.log_softmax(logits.double()					,      dim=1							).float()
								elif self.is_input_patches:
												# TODO: cleanup!
												A     :  Tuple   =				self.transformer_blocks[0].norma.emb(
												    snake_case_					,      snake_case_					,      hidden_dtype=hidden_states.dtype							)
												A ,       A     :  List[str]   =				self.proj_out_a(F.silu(snake_case_							)							).chunk(2					,      dim=1							)
												A     :  Tuple   =				self.norm_out(snake_case_							) * (1 + scale[:, None]) + shift[:, None]
												A     :  List[Any]   =				self.proj_out_a(snake_case_							)

												# unpatchify
												A     :  List[Any]   =				int(hidden_states.shape[1] ** 0.5							)
												A     :  List[Any]   =				hidden_states.reshape(
												    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)							)
												A     :  str   =				torch.einsum('''nhwpqc->nchpwq'''					,      snake_case_							)
												A     :  Tuple   =				hidden_states.reshape(
												    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)							)

								if not return_dict:
												return (output,)

								return TransformeraDModelOutput(sample=snake_case_							)

import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    LayoutLMForMaskedLM,
					    LayoutLMForQuestionAnswering,
					    LayoutLMForSequenceClassification,
					    LayoutLMForTokenClassification,
					    LayoutLMModel,
					)

class     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		Optional[Any]					,      snake_case_ :		int					,      snake_case_ :		Union[str, Any]=13					,      snake_case_ :		int=7					,      snake_case_ :		List[str]=True					,      snake_case_ :		List[str]=True					,      snake_case_ :		Optional[Any]=True					,      snake_case_ :		int=True					,      snake_case_ :		Optional[Any]=99					,      snake_case_ :		List[Any]=32					,      snake_case_ :		Dict=5					,      snake_case_ :		Tuple=4					,      snake_case_ :		Optional[int]=37					,      snake_case_ :		List[Any]="gelu"					,      snake_case_ :		int=0.1					,      snake_case_ :		int=0.1					,      snake_case_ :		Optional[int]=512					,      snake_case_ :		Optional[int]=16					,      snake_case_ :		List[str]=2					,      snake_case_ :		List[str]=0.02					,      snake_case_ :		str=3					,      snake_case_ :		Tuple=4					,      snake_case_ :		Optional[int]=None					,      snake_case_ :		List[str]=1000					,      ):


								"""simple docstring"""

								A     :  Optional[Any]   =				parent
								A     :  Dict   =				batch_size
								A     :  List[str]   =				seq_length
								A     :  Tuple   =				is_training
								A     :  str   =				use_input_mask
								A     :  Optional[int]   =				use_token_type_ids
								A     :  List[str]   =				use_labels
								A     :  Union[str, Any]   =				vocab_size
								A     :  Any   =				hidden_size
								A     :  Dict   =				num_hidden_layers
								A     :  int   =				num_attention_heads
								A     :  Tuple   =				intermediate_size
								A     :  Optional[int]   =				hidden_act
								A     :  List[str]   =				hidden_dropout_prob
								A     :  Optional[Any]   =				attention_probs_dropout_prob
								A     :  str   =				max_position_embeddings
								A     :  Any   =				type_vocab_size
								A     :  Dict   =				type_sequence_label_size
								A     :  List[Any]   =				initializer_range
								A     :  Optional[int]   =				num_labels
								A     :  Optional[Any]   =				num_choices
								A     :  Union[str, Any]   =				scope
								A     :  Optional[Any]   =				range_bbox





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

								A     :  Any   =				ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

								A     :  List[Any]   =				ids_tensor([self.batch_size, self.seq_length, 4]					,      self.range_bbox							)
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]							):
												for j in range(bbox.shape[1]							):
																if bbox[i, j, 3] < bbox[i, j, 1]:
																				A     :  Dict   =				bbox[i, j, 3]
																				A     :  Optional[int]   =				bbox[i, j, 1]
																				A     :  Any   =				t
																if bbox[i, j, 2] < bbox[i, j, 0]:
																				A     :  Tuple   =				bbox[i, j, 2]
																				A     :  str   =				bbox[i, j, 0]
																				A     :  Any   =				t

								A     :  Any   =				None
								if self.use_input_mask:
												A     :  List[str]   =				ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2							)

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

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

								A     :  List[Any]   =				self.get_config()

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





				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								return 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					,      )





				def        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		Any					,      snake_case_ :		List[Any]					,      snake_case_ :		List[str]					,      snake_case_ :		Any					,      snake_case_ :		Optional[Any]					,      snake_case_ :		str					,      snake_case_ :		Dict					,      snake_case_ :		int							):


								"""simple docstring"""

								A     :  int   =				LayoutLMModel(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  str   =				model(snake_case_					,      snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_							)
								A     :  Optional[Any]   =				model(snake_case_					,      snake_case_					,      token_type_ids=snake_case_							)
								A     :  int   =				model(snake_case_					,      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        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Optional[int]					,      snake_case_ :		Tuple					,      snake_case_ :		Optional[int]					,      snake_case_ :		Dict					,      snake_case_ :		Any					,      snake_case_ :		str					,      snake_case_ :		int							):


								"""simple docstring"""

								A     :  Dict   =				LayoutLMForMaskedLM(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  List[str]   =				model(snake_case_					,      snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		List[str]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		List[Any]					,      snake_case_ :		List[str]					,      snake_case_ :		List[Any]					,      snake_case_ :		List[str]					,      snake_case_ :		Any					,      snake_case_ :		List[str]							):


								"""simple docstring"""

								A     :  str   =				self.num_labels
								A     :  Any   =				LayoutLMForSequenceClassification(snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Optional[Any]   =				model(
								    snake_case_					,      snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def        _UpperCAmelCase						(		self :		str					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Any					,      snake_case_ :		Tuple					,      snake_case_ :		int					,      snake_case_ :		List[Any]					,      snake_case_ :		Tuple					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Optional[Any]							):


								"""simple docstring"""

								A     :  Any   =				self.num_labels
								A     :  List[str]   =				LayoutLMForTokenClassification(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  Tuple   =				model(snake_case_					,      snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





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


								"""simple docstring"""

								A     :  Any   =				LayoutLMForQuestionAnswering(config=snake_case_							)
								model.to(snake_case_							)
								model.eval()
								A     :  List[str]   =				model(
								    snake_case_					,      bbox=snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      start_positions=snake_case_					,      end_positions=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        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.prepare_config_and_inputs()
								(
								    (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       (
								    A
								) ,       
								)     :  Optional[int]   =				config_and_inputs
								A     :  Dict   =				{
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict



@require_torch
class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case,    unittest.TestCase							):
				lowerCamelCase_       =     (
				    (
				        LayoutLMModel,
				        LayoutLMForMaskedLM,
				        LayoutLMForSequenceClassification,
				        LayoutLMForTokenClassification,
				        LayoutLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				lowerCamelCase_       =     (
				    {
				        'document-question-answering': LayoutLMForQuestionAnswering,
				        'feature-extraction': LayoutLMModel,
				        'fill-mask': LayoutLMForMaskedLM,
				        'text-classification': LayoutLMForSequenceClassification,
				        'token-classification': LayoutLMForTokenClassification,
				        'zero-shot': LayoutLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCamelCase_       =     True





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				LayoutLMModelTester(self							)
								A     :  Any   =				ConfigTester(self					,      config_class=snake_case_					,      hidden_size=37							)





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								self.config_tester.run_common_tests()





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


								"""simple docstring"""

								A     :  Dict   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  int   =				self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												A     :  Optional[int]   =				type
												self.model_tester.create_and_check_model(*snake_case_							)





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  List[Any]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								A     :  str   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								A     :  Dict   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								A     :  Any   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_							)






				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								pass





def 							_lowerCamelCase			(					):



				'''simple docstring'''







				A     :  Optional[int]   =				torch.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]]				,	device=lowerCamelCase_		)  # noqa: E231
				A     :  str   =				torch.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],]				,	device=lowerCamelCase_		)  # noqa: E231
				A     :  Dict   =				torch.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]]]				,	device=lowerCamelCase_		)  # noqa: E231
				A     :  Dict   =				torch.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]]				,	device=lowerCamelCase_		)  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				A     :  List[Any]   =				torch.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]]				,	device=lowerCamelCase_		)  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class     _SCREAMING_SNAKE_CASE							(		unittest.TestCase							):





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


								"""simple docstring"""

								A     :  Any   =				LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''							).to(snake_case_							)

								A ,       A ,       A ,       A ,       A     :  Optional[Any]   =				prepare_layoutlm_batch_inputs()

								# forward pass
								A     :  Dict   =				model(input_ids=snake_case_					,      bbox=snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_							)

								# test the sequence output on [0, :3, :3]
								A     :  Optional[int]   =				torch.tensor(
								    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]					,      device=snake_case_					,      )

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

								# test the pooled output on [1, :3]
								A     :  Any   =				torch.tensor([-0.65_80, -0.02_14, 0.85_52]					,      device=snake_case_							)

								self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					,      snake_case_					,      atol=1E-3							)							)





				@slow
				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Any   =				LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''					,      num_labels=2							).to(
								    snake_case_							)

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

								# forward pass
								A     :  int   =				model(
								    input_ids=snake_case_					,      bbox=snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=torch.tensor([1, 1]					,      device=snake_case_							)					,      )

								# test whether we get a loss as a scalar
								A     :  int   =				outputs.loss
								A     :  Any   =				torch.Size([]							)
								self.assertEqual(loss.shape					,      snake_case_							)

								# test the shape of the logits
								A     :  List[Any]   =				outputs.logits
								A     :  Optional[Any]   =				torch.Size((2, 2)							)
								self.assertEqual(logits.shape					,      snake_case_							)





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


								"""simple docstring"""

								A     :  Any   =				LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''					,      num_labels=13							).to(
								    snake_case_							)

								A ,       A ,       A ,       A ,       A     :  Any   =				prepare_layoutlm_batch_inputs()

								# forward pass
								A     :  Union[str, Any]   =				model(
								    input_ids=snake_case_					,      bbox=snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_					,      labels=snake_case_							)

								# test the loss calculation to be around 2.65
								# expected_loss = torch.tensor(2.65, device=torch_device)

								# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
								# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

								# test the shape of the logits
								A     :  List[Any]   =				outputs.logits
								A     :  str   =				torch.Size((2, 25, 13)							)
								self.assertEqual(logits.shape					,      snake_case_							)






				@slow
				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								A     :  str   =				LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''							).to(snake_case_							)

								A ,       A ,       A ,       A ,       A     :  str   =				prepare_layoutlm_batch_inputs()

								# forward pass
								A     :  str   =				model(input_ids=snake_case_					,      bbox=snake_case_					,      attention_mask=snake_case_					,      token_type_ids=snake_case_							)

								# test the shape of the logits
								A     :  Optional[Any]   =				torch.Size((2, 25)							)
								self.assertEqual(outputs.start_logits.shape					,      snake_case_							)
								self.assertEqual(outputs.end_logits.shape					,      snake_case_							)