"""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_funnel import FunnelTokenizer


_a					:					Any=			logging.get_logger(__name__)

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

_a					:					Optional[Any]=			[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_a					:					List[Any]=			{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_a					:					str=			{f'''funnel-transformer/{name}''': 512 for name in _model_names}
_a					:					List[Any]=			{f'''funnel-transformer/{name}''': {"do_lower_case": True} for name in _model_names}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				List[str]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				Tuple   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				int   =		PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Tuple   =		FunnelTokenizer
				UpperCAmelCase    :				Tuple   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		2



				def __init__(self    :				int     ,  _A    :				Any=None     ,  _A    :				Union[str, Any]=None     ,  _A    :				Union[str, Any]=True     ,  _A    :				List[str]="<unk>"     ,  _A    :				Any="<sep>"     ,  _A    :				Dict="<pad>"     ,  _A    :				Tuple="<cls>"     ,  _A    :				Dict="<mask>"     ,  _A    :				Optional[Any]="<s>"     ,  _A    :				List[Any]="</s>"     ,  _A    :				Optional[int]=True     ,  _A    :				Dict=True     ,  _A    :				Tuple=None     ,  _A    :				int="##"     ,  **_A    :				Any     ,  )   ->     str:
					super().__init__(
					    _A     ,  tokenizer_file=_A     ,  do_lower_case=_A     ,  unk_token=_A     ,  sep_token=_A     ,  pad_token=_A     ,  cls_token=_A     ,  mask_token=_A     ,  bos_token=_A     ,  eos_token=_A     ,  clean_text=_A     ,  tokenize_chinese_chars=_A     ,  strip_accents=_A     ,  wordpieces_prefix=_A     ,  **_A     ,  )

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

					__snake_case      :							str						=			do_lower_case



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Dict     ,  _A    :				Tuple=None)   ->     Any:
					__snake_case      :							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  _lowercase					(self    :				str     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							Union[str, Any]						=			[self.sep_token_id]
					__snake_case      :							Union[str, Any]						=			[self.cls_token_id]
					if token_ids_a is None:
						return len(cls) * [self.cls_token_type_id] + len(token_ids_a + sep) * [0]
					return len(cls) * [self.cls_token_type_id] + len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]






				def  _lowercase					(self    :				Tuple     ,  _A    :				str     ,  _A    :				Optional[str] = None)   ->     Tuple[str]:
					__snake_case      :							int						=			self._tokenizer.model.save(_A     ,  name=_A)
					return tuple(_A)






"""simple docstring"""







from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Optional[int]   =		"""new-model"""


if is_tf_available():

	class 		UpperCamelCase      (   lowercase						):
					UpperCAmelCase    :				List[str]   =		NewModelConfig

@require_tf
class 		UpperCamelCase      (   unittest.TestCase						):



				@slow
				def  _lowercase					(self    :				List[str])   ->     Dict:
					__snake_case      :							Any						=			'bert-base-cased'
					__snake_case      :							Optional[Any]						=			AutoConfig.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							Union[str, Any]						=			TFAutoModel.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				List[Any])   ->     str:
					__snake_case      :							Optional[int]						=			'bert-base-cased'
					__snake_case      :							List[Any]						=			AutoConfig.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							Dict						=			TFAutoModelForPreTraining.from_pretrained(_A)
					self.assertIsNotNone(_A)
					self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Any)   ->     List[str]:
					for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							str						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							List[str]						=			TFAutoModelForCausalLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							List[str]						=			TFAutoModelForCausalLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Tuple)   ->     Dict:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Union[str, Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Union[str, Any]						=			TFAutoModelWithLMHead.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Union[str, Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Optional[Any]						=			TFAutoModelForMaskedLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Optional[int]						=			TFAutoModelForMaskedLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				str)   ->     Union[str, Any]:
					for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Dict						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							str						=			TFAutoModelForSeqaSeqLM.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Union[str, Any]						=			TFAutoModelForSeqaSeqLM.from_pretrained(_A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				str)   ->     str:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
						__snake_case      :							Tuple						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Tuple						=			TFAutoModelForSequenceClassification.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				def  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["bert-base-uncased"]:
						__snake_case      :							List[str]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							Any						=			TFAutoModelForQuestionAnswering.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				@slow
				@require_tensorflow_probability
				def  _lowercase					(self    :				List[Any])   ->     List[str]:
					for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
						__snake_case      :							Optional[Any]						=			AutoConfig.from_pretrained(_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)

						__snake_case      :							int						=			TFAutoModelForTableQuestionAnswering.from_pretrained(_A)
						__snake_case		,    __snake_case      :							Dict						=			TFAutoModelForTableQuestionAnswering.from_pretrained(
						    _A     ,  output_loading_info=_A)
						self.assertIsNotNone(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[Any])   ->     Optional[Any]:
					__snake_case      :							Optional[int]						=			TFAutoModelWithLMHead.from_pretrained(_A)
					self.assertIsInstance(_A     ,  _A)
					self.assertEqual(model.num_parameters()     ,  1_44_10)
					self.assertEqual(model.num_parameters(only_trainable=_A)     ,  1_44_10)



				def  _lowercase					(self    :				Any)   ->     List[str]:
					__snake_case      :							List[Any]						=			TFAutoModelWithLMHead.from_pretrained(_A)
					self.assertIsInstance(_A     ,  _A)
					self.assertEqual(model.num_parameters()     ,  1_44_10)
					self.assertEqual(model.num_parameters(only_trainable=_A)     ,  1_44_10)



				def  _lowercase					(self    :				Optional[Any])   ->     str:
					# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
					__snake_case      :							Optional[Any]						=			TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
					self.assertIsInstance(_A     ,  _A)

					__snake_case      :							int						=			copy.deepcopy(model.config)
					__snake_case      :							int						=			['FunnelBaseModel']
					__snake_case      :							int						=			TFAutoModel.from_config(_A)
					self.assertIsInstance(_A     ,  _A)

					with tempfile.TemporaryDirectory() as tmp_dir:
						model.save_pretrained(_A)
						__snake_case      :							List[Any]						=			TFAutoModel.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     int:
					try:
						AutoConfig.register('new-model'     ,  _A)

						__snake_case      :							int						=			[
						    TFAutoModel,
						    TFAutoModelForCausalLM,
						    TFAutoModelForMaskedLM,
						    TFAutoModelForPreTraining,
						    TFAutoModelForQuestionAnswering,
						    TFAutoModelForSequenceClassification,
						    TFAutoModelForTokenClassification,
						]

						for auto_class in auto_classes:
							with self.subTest(auto_class.__name__):
								# Wrong config class will raise an error
								with self.assertRaises(_A):
									auto_class.register(_A     ,  _A)
								auto_class.register(_A     ,  _A)
								# Trying to register something existing in the Transformers library will raise an error
								with self.assertRaises(_A):
									auto_class.register(_A     ,  _A)

								# Now that the config is registered, it can be used as any other config with the auto-API
								__snake_case      :							Union[str, Any]						=			BertModelTester(self).get_config()
								__snake_case      :							Optional[int]						=			NewModelConfig(**tiny_config.to_dict())
								__snake_case      :							List[str]						=			auto_class.from_config(_A)
								self.assertIsInstance(_A     ,  _A)

								with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(_A)
									__snake_case      :							Tuple						=			auto_class.from_pretrained(_A)
									self.assertIsInstance(_A     ,  _A)

					finally:
						if "new-model" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["new-model"]
						for mapping in (
						    TF_MODEL_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						):
							if NewModelConfig in mapping._extra_content:
								del mapping._extra_content[NewModelConfig]



				def  _lowercase					(self    :				Optional[int])   ->     Union[str, Any]:
					with self.assertRaisesRegex(
					    _A     ,  'bert-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Any						=			TFAutoModel.from_pretrained('bert-base')



				def  _lowercase					(self    :				str)   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							Optional[Any]						=			TFAutoModel.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				int)   ->     Any:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'     ,  ):
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[Any])   ->     Any:
					with self.assertRaisesRegex(_A     ,  'Use `from_pt=True` to load this model'):
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')






				def  _lowercase					(self    :				str)   ->     Any:
					# Make sure we have cached the model.
					__snake_case      :							str						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
					with RequestCounter() as counter:
						__snake_case      :							List[str]						=			TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
						self.assertEqual(counter.get_request_count     ,  0)
						self.assertEqual(counter.head_request_count     ,  1)
						self.assertEqual(counter.other_request_count     ,  0)

					# With a sharded checkpoint
					__snake_case      :							Optional[int]						=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
					with RequestCounter() as counter:
						__snake_case      :							Any						=			TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
						self.assertEqual(counter.get_request_count     ,  0)
						self.assertEqual(counter.head_request_count     ,  1)
						self.assertEqual(counter.other_request_count     ,  0)





