





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_	 ={
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase_	 ={
    """microsoft/layoutlm-base-uncased""": 512,
    """microsoft/layoutlm-large-uncased""": 512,
}

UpperCamelCase_	 ={
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}




class 					_a		( a_							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=       PRETRAINED_INIT_CONFIGURATION
				UpperCamelCase								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=       LayoutLMTokenizer






				def __init__(					self						:       Union[str, Any],					lowerCAmelCase__						:       Optional[int]=None,					lowerCAmelCase__						:       List[Any]=None,					lowerCAmelCase__						:       Optional[int]=True,					lowerCAmelCase__						:       Dict="[UNK]",					lowerCAmelCase__						:       Union[str, Any]="[SEP]",					lowerCAmelCase__						:       List[Any]="[PAD]",					lowerCAmelCase__						:       List[Any]="[CLS]",					lowerCAmelCase__						:       Optional[Any]="[MASK]",					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Tuple=None,					**lowerCAmelCase__						:       List[str],					)      -> Union[str, Any]:




										'''simple docstring'''



										super().__init__(
										    snake_case__,					tokenizer_file=snake_case__,					do_lower_case=snake_case__,					unk_token=snake_case__,					sep_token=snake_case__,					pad_token=snake_case__,					cls_token=snake_case__,					mask_token=snake_case__,					tokenize_chinese_chars=snake_case__,					strip_accents=snake_case__,					**snake_case__,					)

										_UpperCamelCase     :   List[str]      =       json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
										if (
										    normalizer_state.get('''lowercase''',					snake_case__				) != do_lower_case
										    or normalizer_state.get('''strip_accents''',					snake_case__				) != strip_accents
										    or normalizer_state.get('''handle_chinese_chars''',					snake_case__				) != tokenize_chinese_chars
										):
																_UpperCamelCase     :   Any      =       getattr(snake_case__,					normalizer_state.pop('''type'''				)				)
																_UpperCamelCase     :   Optional[int]      =       do_lower_case
																_UpperCamelCase     :   Any      =       strip_accents
																_UpperCamelCase     :   Optional[Any]      =       tokenize_chinese_chars
																_UpperCamelCase     :   Any      =       normalizer_class(**snake_case__				)

										_UpperCamelCase     :   Dict      =       do_lower_case






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Dict=None				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																output += token_ids_a + [self.sep_token_id]

										return output






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Tuple = None				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       [self.sep_token_id]
										_UpperCamelCase     :   Optional[Any]      =       [self.cls_token_id]
										if token_ids_a is None:
																return len(cls + token_ids_a + sep				) * [0]
										return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Dict = None				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       self._tokenizer.model.save(snake_case__,					name=snake_case__				)
										return tuple(snake_case__				)








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				lowercase__     =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									if isinstance(model.config  ,			snake_case__     ):
														UpperCamelCase_	,     UpperCamelCase_			= list(tokenizer.lang_code_to_id.keys()     )[:2]
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang=snake_case__  ,			tgt_lang=snake_case__     )
									else:
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__     )
									return translator, ["Some string", "Some other text"]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= translator("Some string"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string", "other string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}, {"translation_text": ANY(snake_case__     )}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)




class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation_cn_to_ar"     )

									# but we do for this one
									UpperCamelCase_			= pipeline(task="translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
									UpperCamelCase_			= MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__     )
									# Missing src_lang, tgt_lang
									with self.assertRaises(snake_case__     ):
														translator("This is a test"     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="hi_IN"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "यह एक परीक्षण है"}]     )

									# src_lang, tgt_lang can be defined at pipeline call time
									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									UpperCamelCase_			= translator("This is a test"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									UpperCamelCase_			= pipeline(task="translation_cn_to_ar"  ,			model=snake_case__     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"cn"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"ar"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									with pytest.warns(snake_case__  ,			match=R".*translation_en_to_de.*"     ):
														UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__     )
									self.assertEqual(translator.task  ,			"translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation"     )
