




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  A		:



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







								"""simple docstring"""



								lowerCAmelCase_      			=  parent
								lowerCAmelCase_      			=  batch_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  patch_size
								lowerCAmelCase_      			=  text_seq_length
								lowerCAmelCase_      			=  is_training
								lowerCAmelCase_      			=  use_input_mask
								lowerCAmelCase_      			=  use_token_type_ids
								lowerCAmelCase_      			=  use_labels
								lowerCAmelCase_      			=  vocab_size
								lowerCAmelCase_      			=  hidden_size
								lowerCAmelCase_      			=  num_hidden_layers
								lowerCAmelCase_      			=  num_attention_heads
								lowerCAmelCase_      			=  intermediate_size
								lowerCAmelCase_      			=  hidden_act
								lowerCAmelCase_      			=  hidden_dropout_prob
								lowerCAmelCase_      			=  attention_probs_dropout_prob
								lowerCAmelCase_      			=  max_position_embeddings
								lowerCAmelCase_      			=  type_vocab_size
								lowerCAmelCase_      			=  type_sequence_label_size
								lowerCAmelCase_      			=  initializer_range
								lowerCAmelCase_      			=  coordinate_size
								lowerCAmelCase_      			=  shape_size
								lowerCAmelCase_      			=  num_labels
								lowerCAmelCase_      			=  num_choices
								lowerCAmelCase_      			=  scope
								lowerCAmelCase_      			=  range_bbox

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  ids_tensor([self.batch_size, self.text_seq_length],	self.vocab_size   )

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

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

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

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

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

								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  LayoutLMvaModel(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()

								# text + image
								lowerCAmelCase_      			=  model(UpperCamelCase__,	pixel_values=UpperCamelCase__   )
								lowerCAmelCase_      			=  model(
								    UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__,	attention_mask=UpperCamelCase__,	token_type_ids=UpperCamelCase__   )
								lowerCAmelCase_      			=  model(UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__,	token_type_ids=UpperCamelCase__   )
								lowerCAmelCase_      			=  model(UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__   )

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

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

								# image only
								lowerCAmelCase_      			=  model(pixel_values=UpperCamelCase__   )
								self.parent.assertEqual(
								    result.last_hidden_state.shape,	(self.batch_size, self.image_seq_length, self.hidden_size)   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.num_labels
								lowerCAmelCase_      			=  LayoutLMvaForSequenceClassification(UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								lowerCAmelCase_      			=  model(
								    UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__,	attention_mask=UpperCamelCase__,	token_type_ids=UpperCamelCase__,	labels=UpperCamelCase__,	)
								self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.num_labels
								lowerCAmelCase_      			=  LayoutLMvaForTokenClassification(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								lowerCAmelCase_      			=  model(
								    UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__,	attention_mask=UpperCamelCase__,	token_type_ids=UpperCamelCase__,	labels=UpperCamelCase__,	)
								self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.text_seq_length, self.num_labels)   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  LayoutLMvaForQuestionAnswering(config=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								model.eval()
								lowerCAmelCase_      			=  model(
								    UpperCamelCase__,	bbox=UpperCamelCase__,	pixel_values=UpperCamelCase__,	attention_mask=UpperCamelCase__,	token_type_ids=UpperCamelCase__,	start_positions=UpperCamelCase__,	end_positions=UpperCamelCase__,	)
								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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					(
								    lowerCAmelCase_
								)			,					
								)      			=  config_and_inputs
								lowerCAmelCase_      			=  {
								    '''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  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False

		__snake_case									=   (
		    (
		        LayoutLMvaModel,
		        LayoutLMvaForSequenceClassification,
		        LayoutLMvaForTokenClassification,
		        LayoutLMvaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		__snake_case									=   (
		    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
		    if is_torch_available()
		    else {}
		)



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								return True



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  LayoutLMvaModelTester(self   )
								lowerCAmelCase_      			=  ConfigTester(self,	config_class=UpperCamelCase__,	hidden_size=37   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=False   ):







								"""simple docstring"""



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

								return inputs_dict



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.config_tester.run_common_tests()



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
														lowerCAmelCase_      			=  type
														self.model_tester.create_and_check_model(*UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__   )







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_      			=  LayoutLMvaModel.from_pretrained(UpperCamelCase__   )
														self.assertIsNotNone(UpperCamelCase__   )







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






@require_torch
class  A		(  unittest.TestCase     ):



		@cached_property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''   ).to(UpperCamelCase__   )

								lowerCAmelCase_      			=  self.default_image_processor
								lowerCAmelCase_      			=  prepare_img()
								lowerCAmelCase_      			=  image_processor(images=UpperCamelCase__,	return_tensors='''pt'''   ).pixel_values.to(UpperCamelCase__   )

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

								# forward pass
								lowerCAmelCase_      			=  model(
								    input_ids=input_ids.to(UpperCamelCase__   ),	bbox=bbox.to(UpperCamelCase__   ),	pixel_values=pixel_values.to(UpperCamelCase__   ),	)

								# verify the logits
								lowerCAmelCase_      			=  torch.Size((1, 199, 768)   )
								self.assertEqual(outputs.last_hidden_state.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  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(UpperCamelCase__   )

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










# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




