
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     42
				lowerCamelCase_       =     42





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  int=0.9_9_9				,	lowerCamelCase_:  Optional[Any]="cosine"				,	):



				'''simple docstring'''







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_:  Optional[Any]		):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_:  Optional[Any]		):
												return math.exp(t * -1_2.0		)

				else:
								raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

				A     :  Dict   =				[]
				for i in range(lowerCamelCase_		):
								A     :  Dict   =				i / num_diffusion_timesteps
								A     :  List[str]   =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_		) / alpha_bar_fn(lowerCamelCase_		)				,	lowerCamelCase_		)		)
				return torch.tensor(lowerCamelCase_				,	dtype=torch.floataa		)






class     _SCREAMING_SNAKE_CASE							(		snake_case,    snake_case							):
				lowerCamelCase_       =     1





				@register_to_config
				def __init__(		self :		Optional[Any]					,      snake_case_ :		int = 1000					,      snake_case_ :		float = 0.00_01					,      snake_case_ :		float = 0.02					,      snake_case_ :		str = "linear"					,      snake_case_ :		float = 0.0					,      snake_case_ :		Optional[np.ndarray] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								if trained_betas is not None:
												A     :  Any   =				torch.from_numpy(snake_case_							)
								elif beta_schedule == "linear":
												A     :  Tuple   =				torch.linspace(snake_case_					,      snake_case_					,      snake_case_					,      dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												A     :  str   =				(
												    torch.linspace(beta_start**0.5					,      beta_end**0.5					,      snake_case_					,      dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												A     :  int   =				betas_for_alpha_bar(snake_case_							)
								elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												A     :  Optional[Any]   =				torch.linspace(-6					,      6					,      snake_case_							)
												A     :  str   =				torch.sigmoid(snake_case_							) * (beta_end - beta_start) + beta_start
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

								A     :  Any   =				1.0 - self.betas
								A     :  int   =				torch.cumprod(self.alphas					,      dim=0							)
								A     :  Any   =				torch.tensor(1.0							)

								A     :  Dict   =				torch.tensor(1.0							)

								# standard deviation of the initial noise distribution
								A     :  Optional[Any]   =				1.0

								# setable values
								A     :  Dict   =				None
								A     :  Dict   =				torch.from_numpy(np.arange(0					,      snake_case_							)[::-1].copy()							)

								A     :  Any   =				eta





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[int] = None							):


								"""simple docstring"""

								return sample





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		int					,      snake_case_ :		int = 10					,      snake_case_ :		int = 10					,      snake_case_ :		Union[str, torch.device] = None					,      ):


								"""simple docstring"""

								A     :  List[Any]   =				min(self.config.num_train_timesteps					,      snake_case_							)
								A     :  int   =				num_inference_steps

								A     :  List[Any]   =				[]

								A     :  Optional[Any]   =				{}
								for j in range(0					,      num_inference_steps - jump_length					,      snake_case_							):
												A     :  List[Any]   =				jump_n_sample - 1

								A     :  Optional[int]   =				num_inference_steps
								while t >= 1:
												A     :  Optional[Any]   =				t - 1
												timesteps.append(snake_case_							)

												if jumps.get(snake_case_					,      0							) > 0:
																A     :  Optional[int]   =				jumps[t] - 1
																for _ in range(snake_case_							):
																				A     :  Dict   =				t + 1
																				timesteps.append(snake_case_							)

								A     :  Optional[Any]   =				np.array(snake_case_							) * (self.config.num_train_timesteps // self.num_inference_steps)
								A     :  Optional[Any]   =				torch.from_numpy(snake_case_							).to(snake_case_							)





				def        _UpperCAmelCase						(		self :		Optional[int]					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								A     :  str   =				t - self.config.num_train_timesteps // self.num_inference_steps

								A     :  str   =				self.alphas_cumprod[t]
								A     :  List[Any]   =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								A     :  Dict   =				1 - alpha_prod_t
								A     :  Any   =				1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								A     :  int   =				(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		int					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		Optional[torch.Generator] = None					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								A     :  Any   =				timestep
								A     :  List[str]   =				timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								A     :  List[Any]   =				self.alphas_cumprod[t]
								A     :  Optional[int]   =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								A     :  Optional[Any]   =				1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								A     :  Optional[Any]   =				(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												A     :  Optional[Any]   =				torch.clamp(snake_case_					,      -1					,      1							)

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								A     :  Dict   =				model_output.device
								A     :  str   =				randn_tensor(model_output.shape					,      generator=snake_case_					,      device=snake_case_					,      dtype=model_output.dtype							)
								A     :  Tuple   =				self.eta * self._get_variance(snake_case_							) ** 0.5

								A     :  List[str]   =				0
								if t > 0 and self.eta > 0:
												A     :  Optional[int]   =				std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								A     :  List[Any]   =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								A     :  Dict   =				alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								A     :  Any   =				(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								A     :  Optional[int]   =				mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

								return RePaintSchedulerOutput(prev_sample=snake_case_					,      pred_original_sample=snake_case_							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Any					,      snake_case_ :		Tuple					,      snake_case_ :		List[str]=None							):


								"""simple docstring"""

								A     :  List[str]   =				self.config.num_train_timesteps // self.num_inference_steps

								for i in range(snake_case_							):
												A     :  Dict   =				self.betas[timestep + i]
												if sample.device.type == "mps":
																# randn does not work reproducibly on mps
																A     :  Any   =				randn_tensor(sample.shape					,      dtype=sample.dtype					,      generator=snake_case_							)
																A     :  Any   =				noise.to(sample.device							)
												else:
																A     :  Union[str, Any]   =				randn_tensor(sample.shape					,      generator=snake_case_					,      device=sample.device					,      dtype=sample.dtype							)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												A     :  str   =				(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		torch.IntTensor					,      ):


								"""simple docstring"""

								raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''							)






				def __len__(		self :		Optional[Any]							):


								"""simple docstring"""

								return self.config.num_train_timesteps

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_							)