





'''simple docstring'''

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


lowercase_					= logging.get_logger(__name__)


lowercase_					= OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_MAPPING




lowercase_					= auto_class_update(FlaxAutoModel)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_PRETRAINING_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_MASKED_LM_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)








import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


A__								=						'''http://www.mocksite.com/file1.txt'''
A__								=						'''"text": ["foo", "foo"]'''
A__								=						'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''


class       a							:
		__lowerCAmelCase	:    Optional[int]											=					2_00
		__lowerCAmelCase	:    List[str]											=					{"""Content-Length""": """100"""}
		__lowerCAmelCase	:    Dict											=					{}



		def 	__lowerCamelCase			(     self     :Dict   ,**__lowercase     :List[Any]  ):
						return [bytes(__lowercase   ,'''utf-8'''  )]



def 			_lowerCAmelCase       (					*__lowerCAmelCase  ,  **__lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				return MockResponse()



@pytest.mark.parametrize('''urls_type'''  ,  [str, list, dict]      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				import requests

				monkeypatch.setattr(__lowerCAmelCase  ,  '''request'''  ,  __lowerCAmelCase      )

				snake_case__	:      Union[str, Any]     		=	URL
				if issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Optional[Any]     		=	url
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      int     		=	[url]
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      int     		=	{'''train''': url}
				snake_case__	:      Dict     		=	'''dummy'''
				snake_case__	:      Any     		=	'''downloads'''
				snake_case__	:      int     		=	tmp_path
				snake_case__	:      Any     		=	DownloadConfig(
				    cache_dir=os.path.join(__lowerCAmelCase  ,  __lowerCAmelCase      )  ,  use_etag=__lowerCAmelCase  ,  )
				snake_case__	:      Tuple     		=	DownloadManager(dataset_name=__lowerCAmelCase  ,  download_config=__lowerCAmelCase      )
				snake_case__	:      List[Any]     		=	dl_manager.download(__lowerCAmelCase      )
				snake_case__	:      Union[str, Any]     		=	urls
				for downloaded_paths in [downloaded_paths]:
								if isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												snake_case__	:      Optional[int]     		=	[downloaded_paths]
												snake_case__	:      Dict     		=	[urls]
								elif isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert "train" in downloaded_paths.keys()
												snake_case__	:      str     		=	downloaded_paths.values()
												snake_case__	:      List[str]     		=	urls.values()
								assert downloaded_paths
								for downloaded_path, input_url in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert downloaded_path == dl_manager.downloaded_paths[input_url]
												snake_case__	:      List[Any]     		=	Path(__lowerCAmelCase      )
												snake_case__	:      Any     		=	downloaded_path.parts
												assert parts[-1] == HASH
												assert parts[-2] == cache_subdir
												assert downloaded_path.exists()
												snake_case__	:      List[str]     		=	downloaded_path.read_text()
												assert content == CONTENT
												snake_case__	:      List[str]     		=	downloaded_path.with_suffix('''.json'''      )
												assert metadata_downloaded_path.exists()
												snake_case__	:      str     		=	json.loads(metadata_downloaded_path.read_text()      )
												assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('''paths_type'''  ,  [str, list, dict]      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	str(__lowerCAmelCase      )
				if issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Tuple     		=	filename
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Dict     		=	[filename]
				elif issubclass(__lowerCAmelCase  ,  __lowerCAmelCase      ):
								snake_case__	:      Dict     		=	{'''train''': filename}
				snake_case__	:      Any     		=	'''dummy'''
				snake_case__	:      Any     		=	xz_file.parent
				snake_case__	:      List[str]     		=	'''extracted'''
				snake_case__	:      Dict     		=	DownloadConfig(
				    cache_dir=__lowerCAmelCase  ,  use_etag=__lowerCAmelCase  ,  )
				snake_case__	:      Dict     		=	DownloadManager(dataset_name=__lowerCAmelCase  ,  download_config=__lowerCAmelCase      )
				snake_case__	:      str     		=	dl_manager.extract(__lowerCAmelCase      )
				snake_case__	:      int     		=	paths
				for extracted_paths in [extracted_paths]:
								if isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												snake_case__	:      Dict     		=	[extracted_paths]
												snake_case__	:      Optional[Any]     		=	[paths]
								elif isinstance(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert "train" in extracted_paths.keys()
												snake_case__	:      int     		=	extracted_paths.values()
												snake_case__	:      int     		=	paths.values()
								assert extracted_paths
								for extracted_path, input_path in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
												assert extracted_path == dl_manager.extracted_paths[input_path]
												snake_case__	:      Optional[int]     		=	Path(__lowerCAmelCase      )
												snake_case__	:      int     		=	extracted_path.parts
												assert parts[-1] == hash_url_to_filename(__lowerCAmelCase  ,  etag=__lowerCAmelCase      )
												assert parts[-2] == extracted_subdir
												assert extracted_path.exists()
												snake_case__	:      List[Any]     		=	extracted_path.read_text()
												snake_case__	:      List[str]     		=	text_file.read_text()
												assert extracted_file_content == expected_file_content



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				assert path.endswith('''.jsonl'''      )
				for num_items, line in enumerate(__lowerCAmelCase  ,  start=1      ):
								snake_case__	:      Any     		=	json.loads(line.decode('''utf-8'''      )      )
								assert item.keys() == {"col_1", "col_2", "col_3"}
				assert num_items == 4



@pytest.mark.parametrize('''archive_jsonl'''  ,  ['''tar_jsonl_path''', '''zip_jsonl_path''']      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      Any     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Union[str, Any]     		=	DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_jsonl == 2



@pytest.mark.parametrize('''archive_nested_jsonl'''  ,  ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']      )
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	request.getfixturevalue(__lowerCAmelCase      )
				snake_case__	:      Optional[int]     		=	DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCAmelCase      )  ,  start=1      ):
												_test_jsonl(__lowerCAmelCase  ,  __lowerCAmelCase      )
				assert num_tar == 1
				assert num_jsonl == 2





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	DownloadManager()
				for num_file, file in enumerate(dl_manager.iter_files(__lowerCAmelCase      )  ,  start=1      ):
								assert os.path.basename(__lowerCAmelCase      ) == ("test.txt" if num_file == 1 else "train.txt")
				assert num_file == 2

