'''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 lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			str=2, UpperCamelCase__      :			Tuple=3, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			Optional[Any]=2, UpperCamelCase__      :			Optional[Any]=7, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Dict=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Dict=True, UpperCamelCase__      :			str=99, UpperCamelCase__      :			Tuple=36, UpperCamelCase__      :			Any=3, UpperCamelCase__      :			str=4, UpperCamelCase__      :			List[str]=37, UpperCamelCase__      :			Optional[Any]="gelu", UpperCamelCase__      :			List[str]=0.1, UpperCamelCase__      :			Tuple=0.1, UpperCamelCase__      :			Optional[Any]=5_12, UpperCamelCase__      :			int=16, UpperCamelCase__      :			Tuple=2, UpperCamelCase__      :			int=0.02, UpperCamelCase__      :			Dict=6, UpperCamelCase__      :			Optional[int]=6, UpperCamelCase__      :			Tuple=3, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			Dict=None, UpperCamelCase__      :			int=10_00, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  num_channels
        _A    		=  image_size
        _A    		=  patch_size
        _A    		=  text_seq_length
        _A    		=  is_training
        _A    		=  use_input_mask
        _A    		=  use_token_type_ids
        _A    		=  use_labels
        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  type_sequence_label_size
        _A    		=  initializer_range
        _A    		=  coordinate_size
        _A    		=  shape_size
        _A    		=  num_labels
        _A    		=  num_choices
        _A    		=  scope
        _A    		=  range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        _A    		=  text_seq_length
        _A    		=  (image_size // patch_size) ** 2 + 1
        _A    		=  self.text_seq_length + self.image_seq_length
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			str:
        _A    		=  ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size     )

        _A    		=  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    		=  bbox[i, j, 3]
                       _A    		=  bbox[i, j, 1]
                       _A    		=  t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                       _A    		=  bbox[i, j, 2]
                       _A    		=  bbox[i, j, 0]
                       _A    		=  t

        _A    		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        _A    		=  None
        if self.use_input_mask:
             _A    		=  random_attention_mask([self.batch_size, self.text_seq_length]     )

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

        _A    		=  None
        _A    		=  None
        if self.use_labels:
             _A    		=  ids_tensor([self.batch_size], self.type_sequence_label_size     )
             _A    		=  ids_tensor([self.batch_size, self.text_seq_length], self.num_labels     )

        _A    		=  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      :			Any, UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Tuple     )  ->			Optional[int]:
        _A    		=  LayoutLMvaModel(config=lowercase_     )
        model.to(lowercase_     )
        model.eval()

        # text + image
        _A    		=  model(lowercase_, pixel_values=lowercase_     )
        _A    		=  model(
            lowercase_, bbox=lowercase_, pixel_values=lowercase_, attention_mask=lowercase_, token_type_ids=lowercase_     )
        _A    		=  model(lowercase_, bbox=lowercase_, pixel_values=lowercase_, token_type_ids=lowercase_     )
        _A    		=  model(lowercase_, bbox=lowercase_, pixel_values=lowercase_     )

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

        # text only
        _A    		=  model(lowercase_     )
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)     )

        # image only
        _A    		=  model(pixel_values=lowercase_     )
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size)     )
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			List[str], UpperCamelCase__      :			str, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			str, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[Any]     )  ->			Any:
        _A    		=  self.num_labels
        _A    		=  LayoutLMvaForSequenceClassification(lowercase_     )
        model.to(lowercase_     )
        model.eval()
        _A    		=  model(
            lowercase_, bbox=lowercase_, pixel_values=lowercase_, attention_mask=lowercase_, token_type_ids=lowercase_, labels=lowercase_, )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Any, UpperCamelCase__      :			int, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			str, UpperCamelCase__      :			Any     )  ->			List[Any]:
        _A    		=  self.num_labels
        _A    		=  LayoutLMvaForTokenClassification(config=lowercase_     )
        model.to(lowercase_     )
        model.eval()
        _A    		=  model(
            lowercase_, bbox=lowercase_, pixel_values=lowercase_, attention_mask=lowercase_, token_type_ids=lowercase_, labels=lowercase_, )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels)     )
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			int, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Tuple     )  ->			int:
        _A    		=  LayoutLMvaForQuestionAnswering(config=lowercase_     )
        model.to(lowercase_     )
        model.eval()
        _A    		=  model(
            lowercase_, bbox=lowercase_, pixel_values=lowercase_, attention_mask=lowercase_, token_type_ids=lowercase_, start_positions=lowercase_, end_positions=lowercase_, )
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)     )
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  self.prepare_config_and_inputs()
        (
            _A
        )    		=  config_and_inputs
        _A    		=  {
            '''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 lowercase_       (		a_    ,    a_    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False

   __lowerCAmelCase         =   (
       (
           LayoutLMvaModel,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase         =   (
       {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
       if is_torch_available()
       else {}
   )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Dict, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			List[str], UpperCamelCase__      :			int     )  ->			str:
        # `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 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        _A    		=  LayoutLMvaModelTester(self     )
        _A    		=  ConfigTester(self, config_class=lowercase_, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			str, UpperCamelCase__      :			str, UpperCamelCase__      :			Dict=False     )  ->			str:
        _A    		=  copy.deepcopy(lowercase_     )
        if model_class in get_values(lowercase_     ):
             _A    		=  {
                 k: v.unsqueeze(1     ).expand(-1, self.model_tester.num_choices, -1     ).contiguous()
                 if isinstance(lowercase_, torch.Tensor     ) and v.ndim > 1
                 else v
                 for k, v in inputs_dict.items()
             }
        if return_labels:
             if model_class in get_values(lowercase_     ):
                  _A    		=  torch.ones(self.model_tester.batch_size, dtype=torch.long, device=lowercase_     )
             elif model_class in get_values(lowercase_     ):
                  _A    		=  torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=lowercase_     )
                  _A    		=  torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=lowercase_     )
             elif model_class in [
                 *get_values(lowercase_     ),
             ]:
                  _A    		=  torch.zeros(
                      self.model_tester.batch_size, dtype=torch.long, device=lowercase_     )
             elif model_class in [
                 *get_values(lowercase_     ),
             ]:
                  _A    		=  torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=torch.long, device=lowercase_, )

        return inputs_dict
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Tuple:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             _A    		=  type
             self.model_tester.create_and_check_model(*lowercase_     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Optional[int]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase_     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase_     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase_     )

   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Tuple:
        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    		=  LayoutLMvaModel.from_pretrained(lowercase_     )
             self.assertIsNotNone(lowercase_     )






def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image







@require_torch
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @cached_property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        return LayoutLMvaImageProcessor(apply_ocr=lowercase_     ) if is_vision_available() else None

   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        _A    		=  LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'     ).to(lowercase_     )

        _A    		=  self.default_image_processor
        _A    		=  prepare_img()
        _A    		=  image_processor(images=lowercase_, return_tensors='pt'     ).pixel_values.to(lowercase_     )

        _A    		=  torch.tensor([[1, 2]]     )
        _A    		=  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

        # forward pass
        _A    		=  model(
            input_ids=input_ids.to(lowercase_     ), bbox=bbox.to(lowercase_     ), pixel_values=pixel_values.to(lowercase_     ), )

        # verify the logits
        _A    		=  torch.Size((1, 1_99, 7_68)     )
        self.assertEqual(outputs.last_hidden_state.shape, lowercase_     )

        _A    		=  torch.tensor(
            [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]     ).to(lowercase_     )

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






"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




