



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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Dict             =   """layoutlm"""


							def __init__(							self					,					a_=30_522					,					a_=768					,					a_=12					,					a_=12					,					a_=3_072					,					a_="gelu"					,					a_=0.1					,					a_=0.1					,					a_=512					,					a_=2					,					a_=0.02					,					a_=1E-1_2					,					a_=0					,					a_="absolute"					,					a_=True					,					a_=1_024					,					**a_					,					):
											super().__init__(pad_token_id=a_					,					**a_						)
											a__			 =						vocab_size
											a__			 =						hidden_size
											a__			 =						num_hidden_layers
											a__			 =						num_attention_heads
											a__			 =						hidden_act
											a__			 =						intermediate_size
											a__			 =						hidden_dropout_prob
											a__			 =						attention_probs_dropout_prob
											a__			 =						max_position_embeddings
											a__			 =						type_vocab_size
											a__			 =						initializer_range
											a__			 =						layer_norm_eps
											a__			 =						position_embedding_type
											a__			 =						use_cache
											a__			 =						max_ad_position_embeddings




class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_ = "default"					,					a_ = None					,					):
											super().__init__(a_					,					task=a_					,					patching_specs=a_						)
											a__			 =						config.max_ad_position_embeddings - 1


							@property
							def _a (							self						):
											return OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
											        ("""bbox""", {0: """batch""", 1: """sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
											        ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
											    ]						)







							def _a (							self					,					a_					,					a_ = -1					,					a_ = -1					,					a_ = False					,					a_ = None					,					):
											a__			 =						super().generate_dummy_inputs(
											    a_					,					batch_size=a_					,					seq_length=a_					,					is_pair=a_					,					framework=a_						)

											# Generate a dummy bbox
											a__			 =						[48, 84, 73, 128]

											if not framework == TensorType.PYTORCH:
															raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""						)

											if not is_torch_available():
															raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""						)
											import torch

											a__ ,     a__			 =						input_dict["""input_ids"""].shape
											a__			 =						torch.tensor([*[box] * seq_length]						).tile(a_					,					1					,					1						)
											return input_dict






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

