






"""simple docstring"""
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=99     ,     lowerCAmelCase_=36     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=37     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=512     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=3     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=1000     ,     ):
		A_		:						Optional[int]	    =   parent
		A_		:						Tuple	    =   batch_size
		A_		:						Optional[Any]	    =   num_channels
		A_		:						Any	    =   image_size
		A_		:						Any	    =   patch_size
		A_		:						int	    =   text_seq_length
		A_		:						int	    =   is_training
		A_		:						Any	    =   use_input_mask
		A_		:						Tuple	    =   use_token_type_ids
		A_		:						List[str]	    =   use_labels
		A_		:						Tuple	    =   vocab_size
		A_		:						Optional[Any]	    =   hidden_size
		A_		:						List[Any]	    =   num_hidden_layers
		A_		:						Any	    =   num_attention_heads
		A_		:						Optional[int]	    =   intermediate_size
		A_		:						List[Any]	    =   hidden_act
		A_		:						Optional[int]	    =   hidden_dropout_prob
		A_		:						str	    =   attention_probs_dropout_prob
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						List[str]	    =   type_vocab_size
		A_		:						List[Any]	    =   type_sequence_label_size
		A_		:						Optional[Any]	    =   initializer_range
		A_		:						Tuple	    =   coordinate_size
		A_		:						Tuple	    =   shape_size
		A_		:						Tuple	    =   num_labels
		A_		:						Any	    =   num_choices
		A_		:						Tuple	    =   scope
		A_		:						Any	    =   range_bbox

		# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
		A_		:						Optional[int]	    =   text_seq_length
		A_		:						Any	    =   (image_size // patch_size) ** 2 + 1
		A_		:						Optional[Any]	    =   self.text_seq_length + self.image_seq_length





	def    lowerCamelCase(self							):
		A_		:						int	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.vocab_size							)

		A_		:						List[str]	    =   ids_tensor([self.batch_size, self.text_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_		:						Union[str, Any]	    =   bbox[i, j, 3]
					A_		:						Optional[int]	    =   bbox[i, j, 1]
					A_		:						Any	    =   t
				if bbox[i, j, 2] < bbox[i, j, 0]:
					A_		:						str	    =   bbox[i, j, 2]
					A_		:						List[Any]	    =   bbox[i, j, 0]
					A_		:						Union[str, Any]	    =   t

		A_		:						Any	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		A_		:						Dict	    =   None
		if self.use_input_mask:
			A_		:						Optional[int]	    =   random_attention_mask([self.batch_size, self.text_seq_length]							)

		A_		:						Dict	    =   None
		if self.use_token_type_ids:
			A_		:						str	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.type_vocab_size							)

		A_		:						Dict	    =   None
		A_		:						Tuple	    =   None
		if self.use_labels:
			A_		:						Any	    =   ids_tensor([self.batch_size]     ,     self.type_sequence_label_size							)
			A_		:						str	    =   ids_tensor([self.batch_size, self.text_seq_length]     ,     self.num_labels							)

		A_		:						List[str]	    =   LayoutLMvaConfig(
		    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     ,     coordinate_size=self.coordinate_size     ,     shape_size=self.shape_size     ,     input_size=self.image_size     ,     patch_size=self.patch_size     ,     )

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   LayoutLMvaModel(config=lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()

		# text + image
		A_		:						Optional[int]	    =   model(lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   model(
		    lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     token_type_ids=lowerCAmelCase_							)
		A_		:						List[str]	    =   model(lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_     ,     token_type_ids=lowerCAmelCase_							)
		A_		:						Tuple	    =   model(lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_							)

		self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)							)

		# text only
		A_		:						str	    =   model(lowerCAmelCase_							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape     ,     (self.batch_size, self.text_seq_length, self.hidden_size)							)

		# image only
		A_		:						List[Any]	    =   model(pixel_values=lowerCAmelCase_							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape     ,     (self.batch_size, self.image_seq_length, self.hidden_size)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   self.num_labels
		A_		:						int	    =   LayoutLMvaForSequenceClassification(lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()
		A_		:						Any	    =   model(
		    lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     token_type_ids=lowerCAmelCase_     ,     labels=lowerCAmelCase_     ,     )
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   self.num_labels
		A_		:						Union[str, Any]	    =   LayoutLMvaForTokenClassification(config=lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()
		A_		:						List[Any]	    =   model(
		    lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     token_type_ids=lowerCAmelCase_     ,     labels=lowerCAmelCase_     ,     )
		self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.text_seq_length, self.num_labels)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   LayoutLMvaForQuestionAnswering(config=lowerCAmelCase_							)
		model.to(lowerCAmelCase_							)
		model.eval()
		A_		:						Optional[Any]	    =   model(
		    lowerCAmelCase_     ,     bbox=lowerCAmelCase_     ,     pixel_values=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     token_type_ids=lowerCAmelCase_     ,     start_positions=lowerCAmelCase_     ,     end_positions=lowerCAmelCase_     ,     )
		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    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   self.prepare_config_and_inputs()
		(
		    (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     (
		    A_
		)    ,     
		)		:						Optional[Any]	    =   config_and_inputs
		A_		:						str	    =   {
		    """input_ids""": input_ids,
		    """bbox""": bbox,
		    """pixel_values""": pixel_values,
		    """token_type_ids""": token_type_ids,
		    """attention_mask""": input_mask,
		}
		return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				False
	_A       :				Union[str, Any]    =				False
	_A       :				Any    =				False

	_A       :				Optional[Any]    =				(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_A       :				Optional[int]    =				(
	    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
		# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
		# the sequence dimension of the text embedding only.
		# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
		return True





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   LayoutLMvaModelTester(self							)
		A_		:						Optional[int]	    =   ConfigTester(self     ,     config_class=lowerCAmelCase_     ,     hidden_size=37							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		A_		:						Any	    =   copy.deepcopy(lowerCAmelCase_							)
		if model_class in get_values(lowerCAmelCase_							):
			A_		:						List[str]	    =   {
			    k: v.unsqueeze(1							).expand(-1     ,     self.model_tester.num_choices     ,     -1							).contiguous()
			    if isinstance(lowerCAmelCase_     ,     torch.Tensor							) and v.ndim > 1
			    else v
			    for k, v in inputs_dict.items()
			}
		if return_labels:
			if model_class in get_values(lowerCAmelCase_							):
				A_		:						int	    =   torch.ones(self.model_tester.batch_size     ,     dtype=torch.long     ,     device=lowerCAmelCase_							)
			elif model_class in get_values(lowerCAmelCase_							):
				A_		:						Any	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=lowerCAmelCase_							)
				A_		:						Optional[Any]	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=lowerCAmelCase_							)
			elif model_class in [
			    *get_values(lowerCAmelCase_							),
			]:
				A_		:						Any	    =   torch.zeros(
				    self.model_tester.batch_size     ,     dtype=torch.long     ,     device=lowerCAmelCase_							)
			elif model_class in [
			    *get_values(lowerCAmelCase_							),
			]:
				A_		:						Optional[int]	    =   torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,     dtype=torch.long     ,     device=lowerCAmelCase_     ,     )

		return inputs_dict





	def    lowerCamelCase(self							):
		self.config_tester.run_common_tests()





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.model_tester.prepare_config_and_inputs()
		for type in ["absolute", "relative_key", "relative_key_query"]:
			A_		:						Tuple	    =   type
			self.model_tester.create_and_check_model(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_							)





	@slow
	def    lowerCamelCase(self							):
		for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:						str	    =   LayoutLMvaModel.from_pretrained(lowerCAmelCase_							)
			self.assertIsNotNone(lowerCAmelCase_							)






def 			__UpperCamelCase	(		):
	A_		:						List[str]	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
	return image




@require_torch
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	@cached_property
	def    lowerCamelCase(self							):
		return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase_							) if is_vision_available() else None





	@slow
	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""							).to(lowerCAmelCase_							)

		A_		:						List[Any]	    =   self.default_image_processor
		A_		:						str	    =   prepare_img()
		A_		:						Union[str, Any]	    =   image_processor(images=lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values.to(lowerCAmelCase_							)

		A_		:						Optional[int]	    =   torch.tensor([[1, 2]]							)
		A_		:						Dict	    =   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

		# forward pass
		A_		:						Optional[Any]	    =   model(
		    input_ids=input_ids.to(lowerCAmelCase_							)     ,     bbox=bbox.to(lowerCAmelCase_							)     ,     pixel_values=pixel_values.to(lowerCAmelCase_							)     ,     )

		# verify the logits
		A_		:						Optional[int]	    =   torch.Size((1, 199, 768)							)
		self.assertEqual(outputs.last_hidden_state.shape     ,     lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   torch.tensor(
		    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(lowerCAmelCase_							)

		self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,     lowerCAmelCase_     ,     atol=1e-4							)							)









"""simple docstring"""
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	}

	with open(os.path.join(snake_case__   ,   """git_log.json"""					)   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=4					)






def 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   False
		return

	assert torch.cuda.is_available()

	logger.info("""Initializing GPUs"""					)
	if params.n_gpu > 1:
		assert params.local_rank != -1

		A_		:						str	    =   int(os.environ["""WORLD_SIZE"""]					)
		A_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   params.global_rank // params.n_gpu_per_node
		A_		:						Tuple	    =   True

		assert params.n_nodes == int(os.environ["""N_NODES"""]					)
		assert params.node_id == int(os.environ["""NODE_RANK"""]					)

	# local job (single GPU)
	else:
		assert params.local_rank == -1

		A_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						List[Any]	    =   False

	# sanity checks
	assert params.n_nodes >= 1
	assert 0 <= params.node_id < params.n_nodes
	assert 0 <= params.local_rank <= params.global_rank < params.world_size
	assert params.world_size == params.n_nodes * params.n_gpu_per_node

	# define whether this is the master process / if we are in multi-node distributed mode
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   F"""--- Global rank: {params.global_rank} - """
	logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes					)
	logger.info(PREFIX + """Node ID        : %i""" % params.node_id					)
	logger.info(PREFIX + """Local rank     : %i""" % params.local_rank					)
	logger.info(PREFIX + """World size     : %i""" % params.world_size					)
	logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node					)
	logger.info(PREFIX + """Master         : %s""" % str(params.is_master					)					)
	logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node					)					)
	logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu					)					)
	logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()					)

	# set GPU device
	torch.cuda.set_device(params.local_rank					)

	# initialize multi-GPU
	if params.multi_gpu:
		logger.info("""Initializing PyTorch distributed"""					)
		torch.distributed.init_process_group(
		    init_method="""env://"""   ,   backend="""nccl"""   ,   )






def 			__UpperCamelCase	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

