"""simple docstring"""
import fcntl
import os
import socket

import torch
import torch.distributed as dist





def       lowerCAmelCase__			(	*_UpperCamelCase				: int    )    -> Optional[int]:





						"""simple docstring"""






						with open(_SCREAMING_SNAKE_CASE       ,	'r'    ) as fh:
												fcntl.flock(_SCREAMING_SNAKE_CASE       ,	fcntl.LOCK_EX    )
												try:
																		print(*_SCREAMING_SNAKE_CASE    )
												finally:
																		fcntl.flock(_SCREAMING_SNAKE_CASE       ,	fcntl.LOCK_UN    )


SCREAMING_SNAKE_CASE__					  =     int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
SCREAMING_SNAKE_CASE__					  =     torch.device("cuda", local_rank)
SCREAMING_SNAKE_CASE__					  =     socket.gethostname()

SCREAMING_SNAKE_CASE__					  =     f"""[{hostname}-{local_rank}]"""

try:
					# test distributed
					dist.init_process_group("nccl")
					dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
					dist.barrier()

					# test cuda is available and can allocate memory
					torch.cuda.is_available()
					torch.ones(1).cuda(local_rank)

					# global rank
					SCREAMING_SNAKE_CASE__					  =     dist.get_rank()
					SCREAMING_SNAKE_CASE__					  =     dist.get_world_size()

					printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")

					dist.barrier()
					if rank == 0:
										printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
					printflock(f"""{gpu} is broken""")
					raise






'''simple docstring'''



from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    LayoutLMvaConfig,
	    TFLayoutLMvaForQuestionAnswering,
	    TFLayoutLMvaForSequenceClassification,
	    TFLayoutLMvaForTokenClassification,
	    TFLayoutLMvaModel,
	)

if is_vision_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=2						,				__a=3						,				__a=4						,				__a=2						,				__a=7						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=99						,				__a=36						,				__a=2						,				__a=4						,				__a=37						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=16						,				__a=2						,				__a=0.02						,				__a=6						,				__a=6						,				__a=3						,				__a=4						,				__a=None						,				__a=1000						,				):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  parent
						__a			:       int					    =  batch_size
						__a			:       Any					    =  num_channels
						__a			:       Optional[int]					    =  image_size
						__a			:       Dict					    =  patch_size
						__a			:       int					    =  is_training
						__a			:       Union[str, Any]					    =  use_input_mask
						__a			:       Optional[int]					    =  use_token_type_ids
						__a			:       Dict					    =  use_labels
						__a			:       str					    =  vocab_size
						__a			:       List[Any]					    =  hidden_size
						__a			:       Union[str, Any]					    =  num_hidden_layers
						__a			:       str					    =  num_attention_heads
						__a			:       Union[str, Any]					    =  intermediate_size
						__a			:       Any					    =  hidden_act
						__a			:       List[str]					    =  hidden_dropout_prob
						__a			:       List[str]					    =  attention_probs_dropout_prob
						__a			:       List[Any]					    =  max_position_embeddings
						__a			:       Tuple					    =  type_vocab_size
						__a			:       Any					    =  type_sequence_label_size
						__a			:       Optional[int]					    =  initializer_range
						__a			:       Any					    =  coordinate_size
						__a			:       List[Any]					    =  shape_size
						__a			:       Optional[int]					    =  num_labels
						__a			:       Dict					    =  num_choices
						__a			:       Union[str, Any]					    =  scope
						__a			:       Union[str, 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			:       Dict					    =  self.text_seq_length + self.image_seq_length




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  ids_tensor([self.batch_size, self.text_seq_length]						,				self.vocab_size							)

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

						__a			:       Dict					    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						__a			:       str					    =  None
						if self.use_input_mask:
											__a			:       Optional[Any]					    =  random_attention_mask([self.batch_size, self.text_seq_length]							)

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

						__a			:       Optional[Any]					    =  None
						__a			:       Optional[int]					    =  None
						if self.use_labels:
											__a			:       List[Any]					    =  ids_tensor([self.batch_size]						,				self.type_sequence_label_size							)
											__a			:       Optional[int]					    =  ids_tensor([self.batch_size, self.text_seq_length]						,				self.num_labels							)

						__a			:       int					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Dict					    =  TFLayoutLMvaModel(config=__a							)

						# text + image
						__a			:       List[Any]					    =  model(__a						,				pixel_values=__a						,				training=__a							)
						__a			:       Any					    =  model(
						    __a						,				bbox=__a						,				pixel_values=__a						,				attention_mask=__a						,				token_type_ids=__a						,				training=__a						,				)
						__a			:       Optional[int]					    =  model(__a						,				bbox=__a						,				pixel_values=__a						,				training=__a							)

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

						# text only
						__a			:       Any					    =  model(__a						,				training=__a							)
						self.parent.assertEqual(
						    result.last_hidden_state.shape						,				(self.batch_size, self.text_seq_length, self.hidden_size)							)

						# image only
						__a			:       str					    =  model({'pixel_values': pixel_values}						,				training=__a							)
						self.parent.assertEqual(
						    result.last_hidden_state.shape						,				(self.batch_size, self.image_seq_length, self.hidden_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Any					    =  self.num_labels
						__a			:       Dict					    =  TFLayoutLMvaForSequenceClassification(config=__a							)
						__a			:       List[str]					    =  model(
						    __a						,				bbox=__a						,				pixel_values=__a						,				attention_mask=__a						,				token_type_ids=__a						,				labels=__a						,				training=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  self.num_labels
						__a			:       Optional[Any]					    =  TFLayoutLMvaForTokenClassification(config=__a							)
						__a			:       List[str]					    =  model(
						    __a						,				bbox=__a						,				pixel_values=__a						,				attention_mask=__a						,				token_type_ids=__a						,				labels=__a						,				training=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.text_seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[Any]					    =  2
						__a			:       Any					    =  TFLayoutLMvaForQuestionAnswering(config=__a							)
						__a			:       Any					    =  model(
						    __a						,				bbox=__a						,				pixel_values=__a						,				attention_mask=__a						,				token_type_ids=__a						,				start_positions=__a						,				end_positions=__a						,				training=__a						,				)
						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							):

						'''simple docstring'''





						__a			:       Dict					    =  self.prepare_config_and_inputs()
						((__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a))			:       Dict					    =  config_and_inputs
						__a			:       Any					    =  {
						    '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_tf
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (
	    (
	        TFLayoutLMvaModel,
	        TFLayoutLMvaForQuestionAnswering,
	        TFLayoutLMvaForSequenceClassification,
	        TFLayoutLMvaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	A_				=    (
	    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
	    if is_tf_available()
	    else {}
	)

	A_				=    False
	A_				=    False
	A_				=    False




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						return True




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       str					    =  copy.deepcopy(__a							)

						if model_class in get_values(__a							):
											__a			:       str					    =  {
											    k: tf.tile(tf.expand_dims(__a						,				1							)						,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
											    if isinstance(__a						,				tf.Tensor							) and v.ndim > 0
											    else v
											    for k, v in inputs_dict.items()
											}

						if return_labels:
											if model_class in get_values(__a							):
																__a			:       Optional[int]					    =  tf.ones(self.model_tester.batch_size						,				dtype=tf.intaa							)
											elif model_class in get_values(__a							):
																__a			:       int					    =  tf.zeros(self.model_tester.batch_size						,				dtype=tf.intaa							)
																__a			:       Optional[Any]					    =  tf.zeros(self.model_tester.batch_size						,				dtype=tf.intaa							)
											elif model_class in get_values(__a							):
																__a			:       Any					    =  tf.zeros(self.model_tester.batch_size						,				dtype=tf.intaa							)
											elif model_class in get_values(__a							):
																__a			:       Union[str, Any]					    =  tf.zeros(
																    (self.model_tester.batch_size, self.model_tester.text_seq_length)						,				dtype=tf.intaa							)

						return inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  TFLayoutLMvaModelTester(self							)
						__a			:       Optional[int]					    =  ConfigTester(self						,				config_class=__a						,				hidden_size=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
											__a			:       Dict					    =  model_class(__a							)
											if getattr(__a						,				'hf_compute_loss'						,				__a							):
																# The number of elements in the loss should be the same as the number of elements in the label
																__a			:       Union[str, Any]					    =  self._prepare_for_class(inputs_dict.copy()						,				__a						,				return_labels=__a							)
																__a			:       str					    =  prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()						,				reverse=__a							)[0]
																]
																__a			:       Dict					    =  added_label.shape.as_list()[:1]

																# Test that model correctly compute the loss with kwargs
																__a			:       int					    =  self._prepare_for_class(inputs_dict.copy()						,				__a						,				return_labels=__a							)
																__a			:       Dict					    =  prepared_for_class.pop('input_ids'							)

																__a			:       Tuple					    =  model(__a						,				**__a							)[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

																# Test that model correctly compute the loss when we mask some positions
																__a			:       int					    =  self._prepare_for_class(inputs_dict.copy()						,				__a						,				return_labels=__a							)
																__a			:       str					    =  prepared_for_class.pop('input_ids'							)
																if "labels" in prepared_for_class:
																					__a			:       Union[str, Any]					    =  prepared_for_class['labels'].numpy()
																					if len(labels.shape							) > 1 and labels.shape[1] != 1:
																										__a			:       List[Any]					    =  -100
																										__a			:       List[str]					    =  tf.convert_to_tensor(__a							)
																										__a			:       Any					    =  model(__a						,				**__a							)[0]
																										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)
																										self.assertTrue(not np.any(np.isnan(loss.numpy()							)							)							)

                # Test that model correctly compute the loss with a dict
																__a			:       Union[str, Any]					    =  self._prepare_for_class(inputs_dict.copy()						,				__a						,				return_labels=__a							)
																__a			:       str					    =  model(__a							)[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

																# Test that model correctly compute the loss with a tuple
																__a			:       Tuple					    =  self._prepare_for_class(inputs_dict.copy()						,				__a						,				return_labels=__a							)

																# Get keys that were added with the _prepare_for_class function
																__a			:       Dict					    =  prepared_for_class.keys() - inputs_dict.keys()
																__a			:       Any					    =  inspect.signature(model.call							).parameters
																__a			:       str					    =  list(signature.keys()							)

																# Create a dictionary holding the location of the tensors in the tuple
																__a			:       List[Any]					    =  {0: 'input_ids'}
																for label_key in label_keys:
																					__a			:       List[Any]					    =  signature_names.index(__a							)
																					__a			:       Union[str, Any]					    =  label_key
																__a			:       List[str]					    =  sorted(tuple_index_mapping.items()							)
																# Initialize a list with their default values, update the values and convert to a tuple
																__a			:       Union[str, Any]					    =  []

																for name in signature_names:
																					if name != "kwargs":
																										list_input.append(signature[name].default							)

																for index, value in sorted_tuple_index_mapping:
																					__a			:       Optional[Any]					    =  prepared_for_class[value]

																__a			:       str					    =  tuple(__a							)

																# Send to model
																__a			:       Tuple					    =  model(tuple_input[:-1]							)[0]

																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__a			:       Any					    =  type
											self.model_tester.create_and_check_model(__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(
						    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(
						    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       int					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(
						    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       List[Any]					    =  TFLayoutLMvaModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					return image





@require_tf
class 							__UpperCamelCase				(		unittest.TestCase					):




	@cached_property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return LayoutLMvaImageProcessor(apply_ocr=__a							) if is_vision_available() else None





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'							)

						__a			:       Tuple					    =  self.default_image_processor
						__a			:       List[Any]					    =  prepare_img()
						__a			:       int					    =  image_processor(images=__a						,				return_tensors='tf'							).pixel_values

						__a			:       Union[str, Any]					    =  tf.constant([[1, 2]]							)
						__a			:       Optional[Any]					    =  tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]							)						,				axis=0							)

						# forward pass
						__a			:       Tuple					    =  model(input_ids=__a						,				bbox=__a						,				pixel_values=__a						,				training=__a							)

						# verify the logits
						__a			:       List[Any]					    =  (1, 199, 768)
						self.assertEqual(outputs.last_hidden_state.shape						,				__a							)

						__a			:       Optional[Any]					    =  tf.constant(
						    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							)

						self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]						,				__a						,				atol=1E-4							)							)



