



"""simple docstring"""
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						GPTaTokenizer
				_lowerCAmelCase     :    int			  =						GPTaTokenizerFast
				_lowerCAmelCase     :    Optional[Any]			  =						True
				_lowerCAmelCase     :    Optional[Any]			  =						{"""add_prefix_space""": True}
				_lowerCAmelCase     :    Union[str, Any]			  =						False

				def 		snake_case (				self    ):






										"""simple docstring"""

										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										snake_case								=			[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										    '<|endoftext|>',
										]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										snake_case								=			{'unk_token': '<unk>'}

										snake_case								=			os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']    )
										snake_case								=			os.path.join(self.tmpdirname		,    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 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										kwargs.update(self.special_tokens_map    )
										return GPTaTokenizer.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										kwargs.update(self.special_tokens_map    )
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			'lower newer'
										snake_case								=			'lower newer'
										return input_text, output_text

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			GPTaTokenizer(self.vocab_file		,    self.merges_file		,    **self.special_tokens_map    )
										snake_case								=			'lower newer'
										snake_case								=			['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
										snake_case								=			tokenizer.tokenize(lowerCAmelCase		,    add_prefix_space=lowerCAmelCase    )
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			tokens + [tokenizer.unk_token]
										snake_case								=			[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase    )		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.test_rust_tokenizer:
																return

										snake_case								=			self.get_tokenizer()
										snake_case								=			self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase    )

										snake_case								=			'lower newer'

										# Testing tokenization
										snake_case								=			tokenizer.tokenize(lowerCAmelCase		,    add_prefix_space=lowerCAmelCase    )
										snake_case								=			rust_tokenizer.tokenize(lowerCAmelCase    )
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

										# Testing conversion to ids without special tokens
										snake_case								=			tokenizer.encode(lowerCAmelCase		,    add_special_tokens=lowerCAmelCase		,    add_prefix_space=lowerCAmelCase    )
										snake_case								=			rust_tokenizer.encode(lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    )
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

										# Testing conversion to ids with special tokens
										snake_case								=			self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase    )
										snake_case								=			tokenizer.encode(lowerCAmelCase		,    add_prefix_space=lowerCAmelCase    )
										snake_case								=			rust_tokenizer.encode(lowerCAmelCase    )
										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

										# Testing the unknown token
										snake_case								=			tokens + [rust_tokenizer.unk_token]
										snake_case								=			[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase    )		,    lowerCAmelCase    )

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self		,    lowerCAmelCase=15    ):






										"""simple docstring"""

										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																						snake_case								=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase		,    **lowerCAmelCase    )

																						# Simple input
																						snake_case								=			'This is a simple input'
																						snake_case								=			['This is a simple input 1', 'This is a simple input 2']
																						snake_case								=			('This is a simple input', 'This is a pair')
																						snake_case								=			[
																						    ('This is a simple input 1', 'This is a simple input 2'),
																						    ('This is a simple pair 1', 'This is a simple pair 2'),
																						]

																						# Simple input tests
																						self.assertRaises(lowerCAmelCase		,    tokenizer_r.encode		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'    )

																						# Simple input
																						self.assertRaises(lowerCAmelCase		,    tokenizer_r.encode_plus		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'    )

																						# Simple input
																						self.assertRaises(
																						    lowerCAmelCase		,    tokenizer_r.batch_encode_plus		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'		,    )

																						# Pair input
																						self.assertRaises(lowerCAmelCase		,    tokenizer_r.encode		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'    )

																						# Pair input
																						self.assertRaises(lowerCAmelCase		,    tokenizer_r.encode_plus		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'    )

																						# Pair input
																						self.assertRaises(
																						    lowerCAmelCase		,    tokenizer_r.batch_encode_plus		,    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding='max_length'		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			GPTaTokenizer.from_pretrained(self.tmpdirname		,    pad_token='<pad>'    )

										# Simple input
										snake_case								=			'This is a simple input'
										snake_case								=			['This is a simple input looooooooong', 'This is a simple input']
										snake_case								=			('This is a simple input', 'This is a pair')
										snake_case								=			[
										    ('This is a simple input loooooong', 'This is a simple input'),
										    ('This is a simple pair loooooong', 'This is a simple pair'),
										]

										snake_case								=			tokenizer.pad_token_id

										snake_case								=			tokenizer(lowerCAmelCase		,    padding='max_length'		,    max_length=30		,    return_tensors='np'    )
										snake_case								=			tokenizer(lowerCAmelCase		,    padding=lowerCAmelCase		,    truncate=lowerCAmelCase		,    return_tensors='np'    )
										snake_case								=			tokenizer(*lowerCAmelCase		,    padding='max_length'		,    max_length=60		,    return_tensors='np'    )
										snake_case								=			tokenizer(lowerCAmelCase		,    padding=lowerCAmelCase		,    truncate=lowerCAmelCase		,    return_tensors='np'    )

										# s
										# test single string max_length padding
										self.assertEqual(out_s['input_ids'].shape[-1]		,    30    )
										self.assertTrue(pad_token_id in out_s['input_ids']    )
										self.assertTrue(0 in out_s['attention_mask']    )

										# s2
										# test automatic padding
										self.assertEqual(out_sa['input_ids'].shape[-1]		,    33    )
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa['input_ids'][0]    )
										self.assertFalse(0 in out_sa['attention_mask'][0]    )
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa['input_ids'][1]    )
										self.assertTrue(0 in out_sa['attention_mask'][1]    )

										# p
										# test single pair max_length padding
										self.assertEqual(out_p['input_ids'].shape[-1]		,    60    )
										self.assertTrue(pad_token_id in out_p['input_ids']    )
										self.assertTrue(0 in out_p['attention_mask']    )

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa['input_ids'].shape[-1]		,    52    )
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa['input_ids'][0]    )
										self.assertFalse(0 in out_pa['attention_mask'][0]    )
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa['input_ids'][1]    )
										self.assertTrue(0 in out_pa['attention_mask'][1]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'$$$'
										snake_case								=			GPTaTokenizer.from_pretrained(self.tmpdirname		,    bos_token=lowerCAmelCase		,    add_bos_token=lowerCAmelCase    )

										snake_case								=			'This is a simple input'
										snake_case								=			['This is a simple input 1', 'This is a simple input 2']

										snake_case								=			tokenizer.bos_token_id

										snake_case								=			tokenizer(lowerCAmelCase    )
										snake_case								=			tokenizer(lowerCAmelCase    )

										self.assertEqual(out_s.input_ids[0]		,    lowerCAmelCase    )
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

										snake_case								=			tokenizer.decode(out_s.input_ids    )
										snake_case								=			tokenizer.batch_decode(out_sa.input_ids    )

										self.assertEqual(decode_s.split()[0]		,    lowerCAmelCase    )
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[self.get_tokenizer(do_lower_case=lowerCAmelCase		,    add_bos_token=lowerCAmelCase    )]
										for tokenizer in tokenizers:
																with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
																						snake_case								=			'Encode this.'
																						snake_case								=			'This one too please.'
																						snake_case								=			tokenizer.encode(lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    )
																						encoded_sequence += tokenizer.encode(lowerCAmelCase		,    add_special_tokens=lowerCAmelCase    )
																						snake_case								=			tokenizer.encode_plus(
																						    lowerCAmelCase		,    lowerCAmelCase		,    add_special_tokens=lowerCAmelCase		,    return_special_tokens_mask=lowerCAmelCase		,    )
																						snake_case								=			encoded_sequence_dict['input_ids']
																						snake_case								=			encoded_sequence_dict['special_tokens_mask']
																						self.assertEqual(len(lowerCAmelCase    )		,    len(lowerCAmelCase    )    )

																						snake_case								=			[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase    )
																						]
																						snake_case								=			[x for x in filtered_sequence if x is not None]
																						self.assertEqual(lowerCAmelCase		,    lowerCAmelCase    )





@require_tokenizers
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			AutoTokenizer.from_pretrained('facebook/opt-350m'		,    from_slow=lowerCAmelCase    )
										snake_case								=			'A photo of a cat'

										snake_case								=			tokenizer.encode(
										    lowerCAmelCase		,    )
										self.assertEqual(lowerCAmelCase		,    [2, 2_50, 13_45, 9, 10, 47_58]    )
										tokenizer.save_pretrained('test_opt'    )

										snake_case								=			AutoTokenizer.from_pretrained('./test_opt'    )
										snake_case								=			tokenizer.encode(
										    lowerCAmelCase		,    )
										self.assertEqual(lowerCAmelCase		,    [2, 2_50, 13_45, 9, 10, 47_58]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			AutoTokenizer.from_pretrained('facebook/opt-350m'		,    use_slow=lowerCAmelCase    )
										snake_case								=			'A photo of a cat'

										snake_case								=			tokenizer.encode(
										    lowerCAmelCase		,    )
										# Same as above
										self.assertEqual(lowerCAmelCase		,    [2, 2_50, 13_45, 9, 10, 47_58]    )

				@unittest.skip('This test is failing because of a bug in the fast tokenizer'    )
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			AutoTokenizer.from_pretrained('facebook/opt-350m'		,    from_slow=lowerCAmelCase    )

										snake_case								=			'bos'
										snake_case								=			tokenizer.get_vocab()['bos']

										snake_case								=			'A photo of a cat'
										snake_case								=			tokenizer.encode(
										    lowerCAmelCase		,    )
										# We changed the bos token
										self.assertEqual(lowerCAmelCase		,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]    )
										tokenizer.save_pretrained('./tok'    )
										snake_case								=			AutoTokenizer.from_pretrained('./tok'    )
										self.assertTrue(tokenizer.is_fast    )
										snake_case								=			tokenizer.encode(
										    lowerCAmelCase		,    )
										self.assertEqual(lowerCAmelCase		,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]    )



"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										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		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										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
										snake_case								=			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
										snake_case								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			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
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * 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)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


