"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFRoFormerForCausalLM,
			    TFRoFormerForMaskedLM,
			    TFRoFormerForMultipleChoice,
			    TFRoFormerForQuestionAnswering,
			    TFRoFormerForSequenceClassification,
			    TFRoFormerForTokenClassification,
			    TFRoFormerModel,
			)
			from transformers.models.roformer.modeling_tf_roformer import (
			    TFRoFormerSelfAttention,
			    TFRoFormerSinusoidalPositionalEmbedding,
			)

class    a_   :




					def __init__(						self    :  Any	,    snake_case__    :  Tuple	,    snake_case__    :  Optional[int]=13	,    snake_case__    :  Tuple=7	,    snake_case__    :  List[str]=True	,    snake_case__    :  Optional[Any]=True	,    snake_case__    :  Optional[Any]=True	,    snake_case__    :  List[str]=True	,    snake_case__    :  Optional[int]=99	,    snake_case__    :  Optional[int]=32	,    snake_case__    :  str=2	,    snake_case__    :  Dict=4	,    snake_case__    :  Optional[int]=37	,    snake_case__    :  int="gelu"	,    snake_case__    :  Tuple=0.1	,    snake_case__    :  Any=0.1	,    snake_case__    :  Union[str, Any]=512	,    snake_case__    :  Union[str, Any]=16	,    snake_case__    :  str=2	,    snake_case__    :  str=0.02	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  Any=4	,    snake_case__    :  Tuple=None	,    ):
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = 13
							lowerCAmelCase__   = 7
							lowerCAmelCase__   = True
							lowerCAmelCase__   = True
							lowerCAmelCase__   = True
							lowerCAmelCase__   = True
							lowerCAmelCase__   = 99
							lowerCAmelCase__   = 32
							lowerCAmelCase__   = 2
							lowerCAmelCase__   = 4
							lowerCAmelCase__   = 37
							lowerCAmelCase__   = """gelu"""
							lowerCAmelCase__   = 0.1
							lowerCAmelCase__   = 0.1
							lowerCAmelCase__   = 512
							lowerCAmelCase__   = 16
							lowerCAmelCase__   = 2
							lowerCAmelCase__   = 0.02
							lowerCAmelCase__   = 3
							lowerCAmelCase__   = 4
							lowerCAmelCase__   = None




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

							lowerCAmelCase__   = None
							if self.use_input_mask:
									lowerCAmelCase__   = random_attention_mask([self.batch_size, self.seq_length]  )

							lowerCAmelCase__   = None
							if self.use_token_type_ids:
									lowerCAmelCase__   = ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							if self.use_labels:
									lowerCAmelCase__   = ids_tensor([self.batch_size]	,    self.type_sequence_label_size  )
									lowerCAmelCase__   = ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels  )
									lowerCAmelCase__   = ids_tensor([self.batch_size]	,    self.num_choices  )

							lowerCAmelCase__   = RoFormerConfig(
							    vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    initializer_range=self.initializer_range	,    return_dict=snake_case__	,    )

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Dict	,    snake_case__    :  int	,    snake_case__    :  List[str]	,    snake_case__    :  Any	,    snake_case__    :  int	,    snake_case__    :  Optional[int]	,    snake_case__    :  Tuple  ):
							lowerCAmelCase__   = TFRoFormerModel(config=snake_case__  )
							lowerCAmelCase__   = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowerCAmelCase__   = [input_ids, input_mask]
							lowerCAmelCase__   = model(snake_case__  )

							lowerCAmelCase__   = model(snake_case__  )

							self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Optional[Any]	,    snake_case__    :  List[str]	,    snake_case__    :  Any	,    snake_case__    :  int	,    snake_case__    :  str	,    snake_case__    :  int	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = True
							lowerCAmelCase__   = TFRoFormerForCausalLM(config=snake_case__  )
							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase__   = model(snake_case__  )["""logits"""]
							self.parent.assertListEqual(
							    list(prediction_scores.numpy().shape  )	,    [self.batch_size, self.seq_length, self.vocab_size]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Tuple	,    snake_case__    :  List[str]	,    snake_case__    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = TFRoFormerForMaskedLM(config=snake_case__  )
							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Dict	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  List[str]	,    snake_case__    :  Dict	,    snake_case__    :  Optional[Any]  ):
							lowerCAmelCase__   = self.num_labels
							lowerCAmelCase__   = TFRoFormerForSequenceClassification(config=snake_case__  )
							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  str	,    snake_case__    :  List[str]	,    snake_case__    :  str	,    snake_case__    :  List[Any]	,    snake_case__    :  List[str]	,    snake_case__    :  Any	,    snake_case__    :  List[str]  ):
							lowerCAmelCase__   = self.num_choices
							lowerCAmelCase__   = TFRoFormerForMultipleChoice(config=snake_case__  )
							lowerCAmelCase__   = tf.tile(tf.expand_dims(snake_case__	,    1  )	,    (1, self.num_choices, 1)  )
							lowerCAmelCase__   = tf.tile(tf.expand_dims(snake_case__	,    1  )	,    (1, self.num_choices, 1)  )
							lowerCAmelCase__   = tf.tile(tf.expand_dims(snake_case__	,    1  )	,    (1, self.num_choices, 1)  )
							lowerCAmelCase__   = {
							    """input_ids""": multiple_choice_inputs_ids,
							    """attention_mask""": multiple_choice_input_mask,
							    """token_type_ids""": multiple_choice_token_type_ids,
							}
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  int	,    snake_case__    :  Tuple	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  str	,    snake_case__    :  int  ):
							lowerCAmelCase__   = self.num_labels
							lowerCAmelCase__   = TFRoFormerForTokenClassification(config=snake_case__  )
							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  Any	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  int	,    snake_case__    :  int	,    snake_case__    :  List[str]	,    snake_case__    :  str  ):
							lowerCAmelCase__   = TFRoFormerForQuestionAnswering(config=snake_case__  )
							lowerCAmelCase__   = {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.start_logits.shape	,    (self.batch_size, self.seq_length)  )
							self.parent.assertEqual(result.end_logits.shape	,    (self.batch_size, self.seq_length)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       (
							    lowerCAmelCase__
							) ,       
							)   = config_and_inputs
							lowerCAmelCase__   = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict

@require_tf
class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						str      =     (
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_			:						Optional[Any]      =     (
					    {
					        "feature-extraction": TFRoFormerModel,
					        "fill-mask": TFRoFormerForMaskedLM,
					        "question-answering": TFRoFormerForQuestionAnswering,
					        "text-classification": TFRoFormerForSequenceClassification,
					        "text-generation": TFRoFormerForCausalLM,
					        "token-classification": TFRoFormerForTokenClassification,
					        "zero-shot": TFRoFormerForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_			:						Dict      =     False
					UpperCamelCase_			:						Union[str, Any]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Tuple	,    snake_case__    :  Dict	,    snake_case__    :  int	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]  ):
							if pipeline_test_casse_name == "TextGenerationPipelineTests":
									return True

							return False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = TFRoFormerModelTester(self  )
							lowerCAmelCase__   = ConfigTester(self	,    config_class=snake_case__	,    hidden_size=37  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							self.config_tester.run_common_tests()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*snake_case__  )




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""  )
							self.assertIsNotNone(snake_case__  )

@require_tf
class    a_   (  unittest.TestCase      ):




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
							lowerCAmelCase__   = tf.constant([[0, 1, 2, 3, 4, 5]]  )
							lowerCAmelCase__   = model(snake_case__  )[0]

							# TODO Replace vocab size
							lowerCAmelCase__   = 50000

							lowerCAmelCase__   = [1, 6, vocab_size]
							self.assertEqual(output.shape	,    snake_case__  )

							print(output[:, :3, :3]  )

							# TODO Replace values below with what was printed above.
							lowerCAmelCase__   = tf.constant(
							    [
							        [
							            [-0.1205_3341, -1.026_4901, 0.2922_1946],
							            [-1.513_3783, 0.19_7433, 0.1519_0607],
							            [-5.013_5403, -3.90_0256, -0.8403_8764],
							        ]
							    ]  )
							tf.debugging.assert_near(output[:, :3, :3]	,    snake_case__	,    atol=1E-4  )

@require_tf
class    a_   (  unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     1e-4




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = tf.constant([[4, 10]]  )
							lowerCAmelCase__   = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,    embedding_dim=6  )

							lowerCAmelCase__   = emba(input_ids.shape  )
							lowerCAmelCase__   = tf.constant(
							    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]  )

							tf.debugging.assert_near(snake_case__	,    snake_case__	,    atol=self.tolerance  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = tf.constant(
							    [
							        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
							        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
							        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
							    ]  )
							lowerCAmelCase__   = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512	,    embedding_dim=512  )
							emba([2, 16, 512]  )
							lowerCAmelCase__   = emba.weight[:3, :5]

							tf.debugging.assert_near(snake_case__	,    snake_case__	,    atol=self.tolerance  )

@require_tf
class    a_   (  unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     1e-4




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# 2,12,16,64
							lowerCAmelCase__   = tf.reshape(tf.range(2 * 12 * 16 * 64	,    dtype=tf.floataa  )	,    shape=(2, 12, 16, 64)  ) / 100

							lowerCAmelCase__   = -tf.reshape(tf.range(2 * 12 * 16 * 64	,    dtype=tf.floataa  )	,    shape=(2, 12, 16, 64)  ) / 100

							lowerCAmelCase__   = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32	,    embedding_dim=64  )
							lowerCAmelCase__   = embed_positions([2, 16, 768]  )[None, None, :, :]

							lowerCAmelCase__ ,       lowerCAmelCase__   = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    snake_case__	,    snake_case__	,    snake_case__  )

							lowerCAmelCase__   = tf.constant(
							    [
							        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
							        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
							        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
							        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
							        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
							        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
							    ]  )
							lowerCAmelCase__   = tf.constant(
							    [
							        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
							        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
							        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
							        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
							        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
							        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
							    ]  )

							tf.debugging.assert_near(query_layer[0, 0, :6, :8]	,    snake_case__	,    atol=self.tolerance  )
							tf.debugging.assert_near(key_layer[0, 0, :6, :8]	,    snake_case__	,    atol=self.tolerance  )





"""simple docstring"""




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


__lowerCAmelCase     :				str    				=    "__DUMMY_TRANSFORMERS_USER__"
__lowerCAmelCase     :				Dict    				=    "Dummy User"
__lowerCAmelCase     :				Optional[Any]    				=    "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"

__lowerCAmelCase     :				str    				=    "https://hub-ci.huggingface.co"
__lowerCAmelCase     :				int    				=    CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
__lowerCAmelCase     :				Dict    				=    CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
__lowerCAmelCase     :				Optional[Any]    				=    Path("~/.huggingface/hub_ci_token").expanduser()







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr(
		    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""      ,	lowerCamelCase__  )
		monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		HfFolder.save_token(lowerCamelCase__  )
		yield
		HfFolder.delete_token()







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		return HfApi(endpoint=lowerCamelCase__  )







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = HfFolder.get_token()
		HfFolder.save_token(lowerCamelCase__  )
		yield CI_HUB_USER_TOKEN
		if previous_token is not None:
				HfFolder.save_token(lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		def _cleanup_repo(lowerCamelCase__  ):
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )

		return _cleanup_repo







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		@contextmanager
		def _temporary_repo(lowerCamelCase__  ):
				try:
						yield repo_id
				finally:
						cleanup_repo(lowerCamelCase__  )

		return _temporary_repo







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_txt_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data/text_data.txt"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_txt_data_







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_zipped_txt_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data.zip"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_zipped_txt_data_







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_zipped_img_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data.zip"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_zipped_img_data_




