




"""simple docstring"""






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase__     						=						get_logger(__name__)






class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			"""dummy_data"""
			lowerCamelCase__      =			"""datasets"""
			lowerCamelCase__      =			False
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    0
							_lowerCamelCase     :			Dict    =    dataset_name
							_lowerCamelCase     :			Union[str, Any]    =    cache_dir
							_lowerCamelCase     :			Dict    =    use_local_dummy_data
							_lowerCamelCase     :			Tuple    =    config
							# download_callbacks take a single url as input
							_lowerCamelCase     :			List[Callable]    =    download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							_lowerCamelCase     :			Any    =    load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							_lowerCamelCase     :			str    =    str(lowercase  )
							# to be downloaded
							_lowerCamelCase     :			Union[str, Any]    =    None
							_lowerCamelCase     :			int    =    None
			@property
			def 					A_    (  self  ):
							if self._dummy_file is None:
											_lowerCamelCase     :			Tuple    =    self.download_dummy_data()
							return self._dummy_file
			@property
			def 					A_    (  self  ):
							if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('dummy'  ,      self.config.name  ,      self.version_name  )
							# structure is dummy / version_name
							return os.path.join('dummy'  ,      self.version_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.dummy_data_folder  ,      'dummy_data.zip'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    (
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							_lowerCamelCase     :			int    =    cached_path(
							    lowercase  ,      cache_dir=self.cache_dir  ,      extract_compressed_file=lowercase  ,      force_extract=lowercase  )

							return os.path.join(lowercase  ,      self.dummy_file_name  )
			@property
			def 					A_    (  self  ):
							return os.path.join(self.datasets_scripts_dir  ,      self.dataset_name  ,      self.dummy_zip_file  )
			@property
			def 					A_    (  self  ):
							if self._bucket_url is None:
											_lowerCamelCase     :			List[Any]    =    hf_github_url(self.dataset_name  ,      self.dummy_zip_file.replace(os.sep  ,      '/'  )  )
							return self._bucket_url
			@property
			def 					A_    (  self  ):
							# return full path if its a dir
							if os.path.isdir(self.dummy_file  ):
											return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep  ,      '/'  ).split('/'  )[:-1]  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file
							else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											_lowerCamelCase     :			Union[str, Any]    =    self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(lowercase  ,      lowercase  ):
											return self.create_dummy_data_dict(lowercase  ,      lowercase  )
							elif isinstance(lowercase  ,      (list, tuple)  ):
											return self.create_dummy_data_list(lowercase  ,      lowercase  )
							else:
											return self.create_dummy_data_single(lowercase  ,      lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							return self.download_and_extract(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      *lowercase  ,      **lowercase  ):
							return path
			def 					A_    (  self  ):
							return {}
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    {}
							for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
															if isinstance(lowercase  ,      lowercase  ):
																			for single_url in single_urls:
																							download_callback(lowercase  )
															else:
																			_lowerCamelCase     :			List[Any]    =    single_urls
																			download_callback(lowercase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(lowercase  ,      lowercase  ):
															_lowerCamelCase     :			List[Any]    =    [os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  ) for x in single_urls]
											else:
															_lowerCamelCase     :			Optional[int]    =    single_urls
															_lowerCamelCase     :			List[Any]    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(Path(lowercase  ).name  )  )
											_lowerCamelCase     :			int    =    value

							# make sure that values are unique
							if all(isinstance(lowercase  ,      lowercase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
							    dummy_data_dict.values()  ):
											# append key to value to make its name unique
											_lowerCamelCase     :			List[Any]    =    {key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[Any]    =    []
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							_lowerCamelCase     :			List[str]    =    all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'  ,      lowercase  )  ) for url in data_url  )
							_lowerCamelCase     :			int    =    all(
							    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'  ) for url in data_url  )
							if data_url and (is_tf_records or is_pubmed_records):
											_lowerCamelCase     :			List[str]    =    [data_url[0]] * len(lowercase  )
							for single_url in data_url:
											for download_callback in self.download_callbacks:
															download_callback(lowercase  )
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											_lowerCamelCase     :			str    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(single_url.split('/'  )[-1]  )  )
											dummy_data_list.append(lowercase  )
							return dummy_data_list
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							for download_callback in self.download_callbacks:
											download_callback(lowercase  )
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							_lowerCamelCase     :			Tuple    =    os.path.join(lowercase  ,      urllib.parse.quote_plus(data_url.split('/'  )[-1]  )  )
							if os.path.exists(lowercase  ) or not self.load_existing_dummy_data:
											return value
							else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ,      lowercase  ):
							def _iter_archive_members(lowercase  ):
											# this preserves the order of the members inside the ZIP archive
											_lowerCamelCase     :			str    =    Path(self.dummy_file  ).parent
											_lowerCamelCase     :			Union[str, Any]    =    path.relative_to(lowercase  )
											with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
															_lowerCamelCase     :			List[str]    =    zip_file.namelist()
											for member in members:
															if member.startswith(relative_path.as_posix()  ):
																			yield dummy_parent_path.joinpath(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    Path(lowercase  )
							_lowerCamelCase     :			Dict    =    _iter_archive_members(lowercase  ) if self.use_local_dummy_data else path.rglob('*'  )
							for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('.', '__')  ):
															yield file_path.relative_to(lowercase  ).as_posix(), file_path.open('rb'  )






			def 					A_    (  self  ,      lowercase  ):
							if not isinstance(lowercase  ,      lowercase  ):
											_lowerCamelCase     :			List[str]    =    [paths]
							for path in paths:
											if os.path.isfile(lowercase  ):
															if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																			return
															yield path
											else:
															for dirpath, dirnames, filenames in os.walk(lowercase  ):
																			if os.path.basename(lowercase  ).startswith(('.', '__')  ):
																							continue
																			dirnames.sort()
																			for filename in sorted(lowercase  ):
																							if filename.startswith(('.', '__')  ):
																											continue
																							yield os.path.join(lowercase  ,      lowercase  )





"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""new-model"""


if is_tf_available():






			class lowerCAmelCase__	(     lowercase						):






						'''simple docstring'''



						lowerCamelCase__      =			NewModelConfig






@require_tf
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    'bert-base-cased'
							_lowerCamelCase     :			Union[str, Any]    =    AutoConfig.from_pretrained(lowercase  )
							self.assertIsNotNone(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    TFAutoModel.from_pretrained(lowercase  )
							self.assertIsNotNone(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    'bert-base-cased'
							_lowerCamelCase     :			int    =    AutoConfig.from_pretrained(lowercase  )
							self.assertIsNotNone(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							_lowerCamelCase     :			int    =    TFAutoModelForPreTraining.from_pretrained(lowercase  )
							self.assertIsNotNone(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			Dict    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			int    =    TFAutoModelForCausalLM.from_pretrained(lowercase  )
											_lowerCamelCase, _lowerCamelCase     :			str    =    TFAutoModelForCausalLM.from_pretrained(lowercase  ,      output_loading_info=lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			List[Any]    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			str    =    TFAutoModelWithLMHead.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			Tuple    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			Optional[int]    =    TFAutoModelForMaskedLM.from_pretrained(lowercase  )
											_lowerCamelCase, _lowerCamelCase     :			Tuple    =    TFAutoModelForMaskedLM.from_pretrained(lowercase  ,      output_loading_info=lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_lowerCamelCase     :			Optional[int]    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			Tuple    =    TFAutoModelForSeqaSeqLM.from_pretrained(lowercase  )
											_lowerCamelCase, _lowerCamelCase     :			Tuple    =    TFAutoModelForSeqaSeqLM.from_pretrained(lowercase  ,      output_loading_info=lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
											_lowerCamelCase     :			str    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			Union[str, Any]    =    TFAutoModelForSequenceClassification.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			def 					A_    (  self  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
											_lowerCamelCase     :			Optional[Any]    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			List[str]    =    TFAutoModelForQuestionAnswering.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			@slow
			@require_tensorflow_probability
			def 					A_    (  self  ):
							for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
											_lowerCamelCase     :			Dict    =    AutoConfig.from_pretrained(lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )

											_lowerCamelCase     :			Dict    =    TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase  )
											_lowerCamelCase, _lowerCamelCase     :			List[Any]    =    TFAutoModelForTableQuestionAnswering.from_pretrained(
											    lowercase  ,      output_loading_info=lowercase  )
											self.assertIsNotNone(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    TFAutoModelWithLMHead.from_pretrained(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )
							self.assertEqual(model.num_parameters()  ,      14410  )
							self.assertEqual(model.num_parameters(only_trainable=lowercase  )  ,      14410  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    TFAutoModelWithLMHead.from_pretrained(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )
							self.assertEqual(model.num_parameters()  ,      14410  )
							self.assertEqual(model.num_parameters(only_trainable=lowercase  )  ,      14410  )
			def 					A_    (  self  ):
							# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
							_lowerCamelCase     :			List[str]    =    TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Optional[int]    =    copy.deepcopy(model.config  )
							_lowerCamelCase     :			Dict    =    ['FunnelBaseModel']
							_lowerCamelCase     :			List[Any]    =    TFAutoModel.from_config(lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							with tempfile.TemporaryDirectory() as tmp_dir:
											model.save_pretrained(lowercase  )
											_lowerCamelCase     :			Tuple    =    TFAutoModel.from_pretrained(lowercase  )
											self.assertIsInstance(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							try:
											AutoConfig.register('new-model'  ,      lowercase  )

											_lowerCamelCase     :			Tuple    =    [
											    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(lowercase  ):
																							auto_class.register(lowercase  ,      lowercase  )
																			auto_class.register(lowercase  ,      lowercase  )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(lowercase  ):
																							auto_class.register(lowercase  ,      lowercase  )

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

																			with tempfile.TemporaryDirectory() as tmp_dir:
																							model.save_pretrained(lowercase  )
																							_lowerCamelCase     :			List[Any]    =    auto_class.from_pretrained(lowercase  )
																							self.assertIsInstance(lowercase  ,      lowercase  )

							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 					A_    (  self  ):
							with self.assertRaisesRegex(
							    lowercase  ,      'bert-base is not a local folder and is not a valid model identifier'  ):
											_lowerCamelCase     :			Union[str, Any]    =    TFAutoModel.from_pretrained('bert-base'  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(
							    lowercase  ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
											_lowerCamelCase     :			str    =    TFAutoModel.from_pretrained(lowercase  ,      revision='aaaaaa'  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(
							    lowercase  ,      'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'  ,      ):
											_lowerCamelCase     :			Tuple    =    TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'  )
			def 					A_    (  self  ):
							with self.assertRaisesRegex(lowercase  ,      'Use `from_pt=True` to load this model'  ):
											_lowerCamelCase     :			Tuple    =    TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'  )






			def 					A_    (  self  ):
							# Make sure we have cached the model.
							_lowerCamelCase     :			Optional[int]    =    TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'  )
							with RequestCounter() as counter:
											_lowerCamelCase     :			Optional[int]    =    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
							_lowerCamelCase     :			int    =    TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'  )
							with RequestCounter() as counter:
											_lowerCamelCase     :			List[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  )