





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Dict      =							"""layoutlmv3"""






							def __init__(    self	,			__lowerCAmelCase=50265	,			__lowerCAmelCase=768	,			__lowerCAmelCase=12	,			__lowerCAmelCase=12	,			__lowerCAmelCase=3072	,			__lowerCAmelCase="gelu"	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=512	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=1E-5	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=1024	,			__lowerCAmelCase=128	,			__lowerCAmelCase=128	,			__lowerCAmelCase=True	,			__lowerCAmelCase=32	,			__lowerCAmelCase=128	,			__lowerCAmelCase=64	,			__lowerCAmelCase=256	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=224	,			__lowerCAmelCase=3	,			__lowerCAmelCase=16	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	,			):
														super().__init__(
														    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	,			initializer_range=__lowerCAmelCase	,			layer_norm_eps=__lowerCAmelCase	,			pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														UpperCamelCase__					  =      max_ad_position_embeddings
														UpperCamelCase__					  =      coordinate_size
														UpperCamelCase__					  =      shape_size
														UpperCamelCase__					  =      has_relative_attention_bias
														UpperCamelCase__					  =      rel_pos_bins
														UpperCamelCase__					  =      max_rel_pos
														UpperCamelCase__					  =      has_spatial_attention_bias
														UpperCamelCase__					  =      rel_ad_pos_bins
														UpperCamelCase__					  =      max_rel_ad_pos
														UpperCamelCase__					  =      text_embed
														UpperCamelCase__					  =      visual_embed
														UpperCamelCase__					  =      input_size
														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      patch_size
														UpperCamelCase__					  =      classifier_dropout






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   List[str]      =							version.parse("""1.12"""							)






							@property
							def 					_lowerCamelCase		(    self	):
														# The order of inputs is different for question answering and sequence classification
														if self.task in ["question-answering", "sequence-classification"]:
																					return OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																					        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																					        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
																					    ]	)
														else:
																					return OrderedDict(
																					    [
																					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
																					        ("""bbox""", {0: """batch""", 1: """sequence"""}),
																					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
																					        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
																					    ]	)






							@property
							def 					_lowerCamelCase		(    self	):
														return 1E-5






							@property
							def 					_lowerCamelCase		(    self	):
														return 12







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = -1	,			__lowerCAmelCase = -1	,			__lowerCAmelCase = False	,			__lowerCAmelCase = None	,			__lowerCAmelCase = 3	,			__lowerCAmelCase = 40	,			__lowerCAmelCase = 40	,			):
														setattr(processor.image_processor	,			"""apply_ocr"""	,			__lowerCAmelCase	)

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														UpperCamelCase__					  =      compute_effective_axis_dimension(
														    __lowerCAmelCase	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0	)
														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														UpperCamelCase__					  =      processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase	)
														UpperCamelCase__					  =      compute_effective_axis_dimension(
														    __lowerCAmelCase	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=__lowerCAmelCase	)
														# Generate dummy inputs according to compute batch and sequence
														UpperCamelCase__					  =      [[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

														# Generate dummy bounding boxes
														UpperCamelCase__					  =      [[[48, 84, 73, 128]]] * batch_size

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
														UpperCamelCase__					  =      self._generate_dummy_images(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)

														UpperCamelCase__					  =      dict(
														    processor(
														        __lowerCAmelCase	,			text=__lowerCAmelCase	,			boxes=__lowerCAmelCase	,			return_tensors=__lowerCAmelCase	,			)	)

														return inputs











import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
					from transformers.models.rag.configuration_rag import RagConfig
					from transformers.models.rag.tokenization_rag import RagTokenizer

@require_faiss
@require_torch
class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      tempfile.mkdtemp()
														UpperCamelCase__					  =      8

														# DPR tok
														UpperCamelCase__					  =      [
														    """[UNK]""",
														    """[CLS]""",
														    """[SEP]""",
														    """[PAD]""",
														    """[MASK]""",
														    """want""",
														    """##want""",
														    """##ed""",
														    """wa""",
														    """un""",
														    """runn""",
														    """##ing""",
														    """,""",
														    """low""",
														    """lowest""",
														]
														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""dpr_tokenizer"""	)
														os.makedirs(__lowerCAmelCase	,			exist_ok=__lowerCAmelCase	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			DPR_VOCAB_FILES_NAMES["""vocab_file"""]	)
														with open(self.vocab_file	,			"""w"""	,			encoding="""utf-8"""	) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)

														# BART tok
														UpperCamelCase__					  =      [
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														UpperCamelCase__					  =      dict(zip(__lowerCAmelCase	,			range(len(__lowerCAmelCase	)	)	)	)
														UpperCamelCase__					  =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														UpperCamelCase__					  =      {"""unk_token""": """<unk>"""}

														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""bart_tokenizer"""	)
														os.makedirs(__lowerCAmelCase	,			exist_ok=__lowerCAmelCase	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			BART_VOCAB_FILES_NAMES["""vocab_file"""]	)
														UpperCamelCase__					  =      os.path.join(__lowerCAmelCase	,			BART_VOCAB_FILES_NAMES["""merges_file"""]	)
														with open(self.vocab_file	,			"""w"""	,			encoding="""utf-8"""	) as fp:
																					fp.write(json.dumps(__lowerCAmelCase	) + """\n"""	)
														with open(self.merges_file	,			"""w"""	,			encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(__lowerCAmelCase	)	)






							def 					_lowerCamelCase		(    self	):
														return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,			"""dpr_tokenizer"""	)	)






							def 					_lowerCamelCase		(    self	):
														return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,			"""bart_tokenizer"""	)	)






							def 					_lowerCamelCase		(    self	):
														shutil.rmtree(self.tmpdirname	)






							@require_tokenizers
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      os.path.join(self.tmpdirname	,			"""rag_tokenizer"""	)
														UpperCamelCase__					  =      RagConfig(question_encoder=DPRConfig().to_dict()	,			generator=BartConfig().to_dict()	)
														UpperCamelCase__					  =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,			generator=self.get_bart_tokenizer()	)
														rag_config.save_pretrained(__lowerCAmelCase	)
														rag_tokenizer.save_pretrained(__lowerCAmelCase	)
														UpperCamelCase__					  =      RagTokenizer.from_pretrained(__lowerCAmelCase	,			config=__lowerCAmelCase	)
														self.assertIsInstance(new_rag_tokenizer.question_encoder	,			__lowerCAmelCase	)
														self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,			rag_tokenizer.question_encoder.get_vocab()	)
														self.assertIsInstance(new_rag_tokenizer.generator	,			__lowerCAmelCase	)
														self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,			rag_tokenizer.generator.get_vocab()	)






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-token-nq"""	)
														UpperCamelCase__					  =      [
														    """who got the first nobel prize in physics""",
														    """when is the next deadpool movie being released""",
														    """which mode is used for short wave broadcast service""",
														    """who is the owner of reading football club""",
														    """when is the next scandal episode coming out""",
														    """when is the last time the philadelphia won the superbowl""",
														    """what is the most current adobe flash player version""",
														    """how many episodes are there in dragon ball z""",
														    """what is the first step in the evolution of the eye""",
														    """where is gall bladder situated in human body""",
														    """what is the main mineral in lithium batteries""",
														    """who is the president of usa right now""",
														    """where do the greasers live in the outsiders""",
														    """panda is a national animal of which country""",
														    """what is the name of manchester united stadium""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""	)
														UpperCamelCase__					  =      [
														    """who got the first nobel prize in physics""",
														    """when is the next deadpool movie being released""",
														    """which mode is used for short wave broadcast service""",
														    """who is the owner of reading football club""",
														    """when is the next scandal episode coming out""",
														    """when is the last time the philadelphia won the superbowl""",
														    """what is the most current adobe flash player version""",
														    """how many episodes are there in dragon ball z""",
														    """what is the first step in the evolution of the eye""",
														    """where is gall bladder situated in human body""",
														    """what is the main mineral in lithium batteries""",
														    """who is the president of usa right now""",
														    """where do the greasers live in the outsiders""",
														    """panda is a national animal of which country""",
														    """what is the name of manchester united stadium""",
														]
														UpperCamelCase__					  =      tokenizer(__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)




