
"""simple docstring"""

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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.models.layoutlm.modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMModel,
      )

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=7			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=99			,  __UpperCAmelCase=32			,  __UpperCAmelCase=2			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=512			,  __UpperCAmelCase=16			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=4			,  __UpperCAmelCase=None			,  __UpperCAmelCase=1000			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= seq_length
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_input_mask
      __UpperCamelCase       						= use_token_type_ids
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= intermediate_size
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= max_position_embeddings
      __UpperCamelCase       						= type_vocab_size
      __UpperCamelCase       						= type_sequence_label_size
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= num_labels
      __UpperCamelCase       						= num_choices
      __UpperCamelCase       						= scope
      __UpperCamelCase       						= range_bbox





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size   )

      # convert bbox to numpy since TF does not support item assignment
      __UpperCamelCase       						= ids_tensor([self.batch_size, self.seq_length, 4]			,  self.range_bbox   ).numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]   ):
       for j in range(bbox.shape[1]   ):
        if bbox[i, j, 3] < bbox[i, j, 1]:
         __UpperCamelCase       						= bbox[i, j, 3]
         __UpperCamelCase       						= bbox[i, j, 1]
         __UpperCamelCase       						= t
        if bbox[i, j, 2] < bbox[i, j, 0]:
         __UpperCamelCase       						= bbox[i, j, 2]
         __UpperCamelCase       						= bbox[i, j, 0]
         __UpperCamelCase       						= t
      __UpperCamelCase       						= tf.convert_to_tensor(__UpperCAmelCase   )

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

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

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

      __UpperCamelCase       						= LayoutLMConfig(
          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 config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMModel(config=__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)   )
      self.parent.assertEqual(result.pooler_output.shape			,  (self.batch_size, self.hidden_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMForMaskedLM(config=__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= TFLayoutLMForSequenceClassification(config=__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= TFLayoutLMForTokenClassification(config=__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMForQuestionAnswering(config=__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase			,  __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )
      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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.prepare_config_and_inputs()
      (
          (
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			
      )       						= config_and_inputs
      __UpperCamelCase       						= {
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict


@require_tf
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     lowercase		     =		(
         {
             "feature-extraction": TFLayoutLMModel,
             "fill-mask": TFLayoutLMForMaskedLM,
             "text-classification": TFLayoutLMForSequenceClassification,
             "token-classification": TFLayoutLMForTokenClassification,
             "zero-shot": TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowercase		     =		False
     lowercase		     =		True
     lowercase		     =		10





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase			,  hidden_size=37   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase       						= TFLayoutLMModel.from_pretrained(__UpperCAmelCase   )
       self.assertIsNotNone(__UpperCAmelCase   )







     @unittest.skip('Onnx compliancy broke with TF 2.10'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass






def 							A		(       )   ->			List[Any]:
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 __UpperCamelCase       						= tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	)  # noqa: E231
 __UpperCamelCase       						= tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
 __UpperCamelCase       						= tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	)  # noqa: E231
 __UpperCamelCase       						= tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 __UpperCamelCase       						= tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class __lowerCAmelCase	(   unittest.TestCase    ):





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   )

      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= prepare_layoutlm_batch_inputs()

      # forward pass
      __UpperCamelCase       						= model(input_ids=__UpperCAmelCase			,  bbox=__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )

      # test the sequence output on [0, :3, :3]
      __UpperCamelCase       						= tf.convert_to_tensor(
          [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]			,  )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			,  __UpperCAmelCase			,  atol=1E-3   )   )

      # test the pooled output on [1, :3]
      __UpperCamelCase       						= tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]   )

      self.assertTrue(np.allclose(outputs.pooler_output[1, :3]			,  __UpperCAmelCase			,  atol=1E-3   )   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,  num_labels=2   )

      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= prepare_layoutlm_batch_inputs()

      # forward pass
      __UpperCamelCase       						= model(
          input_ids=__UpperCAmelCase			,  bbox=__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  labels=tf.convert_to_tensor([1, 1]   )			,  )

      # test whether we get a loss as a scalar
      __UpperCamelCase       						= outputs.loss
      __UpperCamelCase       						= (2,)
      self.assertEqual(loss.shape			,  __UpperCAmelCase   )

      # test the shape of the logits
      __UpperCamelCase       						= outputs.logits
      __UpperCamelCase       						= (2, 2)
      self.assertEqual(logits.shape			,  __UpperCAmelCase   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'			,  num_labels=13   )

      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= prepare_layoutlm_batch_inputs()

      # forward pass
      __UpperCamelCase       						= model(
          input_ids=__UpperCAmelCase			,  bbox=__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase			,  labels=__UpperCAmelCase   )

      # test the shape of the logits
      __UpperCamelCase       						= outputs.logits
      __UpperCamelCase       						= tf.convert_to_tensor((2, 25, 13)   )
      self.assertEqual(logits.shape			,  __UpperCAmelCase   )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   )

      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= prepare_layoutlm_batch_inputs()

      # forward pass
      __UpperCamelCase       						= model(input_ids=__UpperCAmelCase			,  bbox=__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  token_type_ids=__UpperCAmelCase   )

      # test the shape of the logits
      __UpperCamelCase       						= tf.convert_to_tensor((2, 25)   )
      self.assertEqual(outputs.start_logits.shape			,  __UpperCAmelCase   )
      self.assertEqual(outputs.end_logits.shape			,  __UpperCAmelCase   )





"""simple docstring"""

import time
from contextlib import contextmanager
from pathlib import Path

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


UpperCamelCase			:       Union[str, Any]									=				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase			:       List[Any]									=				"Dummy User"
UpperCamelCase			:       List[Any]									=				"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"

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






@pytest.fixture
def 							A		(       snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 monkeypatch.setattr(
     'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			List[Any]:
 monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,      snake_case	)
 monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :Optional[int]	)   ->			Dict:
 monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Tuple	)   ->			Tuple:
 HfFolder.save_token(snake_case	)
 yield
 HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 							A		(       )   ->			List[Any]:
 return HfApi(endpoint=snake_case	)






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi	)   ->			List[Any]:
 __UpperCamelCase       						= HfFolder.get_token()
 HfFolder.save_token(snake_case	)
 yield CI_HUB_USER_TOKEN
 if previous_token is not None:
  HfFolder.save_token(snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			str:
 def _cleanup_repo(snake_case   :Union[str, Any]	):
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)

 return _cleanup_repo






@pytest.fixture
def 							A		(       snake_case   :List[str]	)   ->			Any:
 @contextmanager
 def _temporary_repo(snake_case   :Tuple	):
  try:
   yield repo_id
  finally:
   cleanup_repo(snake_case	)

 return _temporary_repo






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :Dict       ,      snake_case   :Dict	)   ->			List[str]:
 __UpperCamelCase       						= f'repo_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data/text_data.txt'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :Optional[int]       ,      snake_case   :str       ,      snake_case   :Tuple	)   ->			Any:
 return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[Any]       ,      snake_case   :str	)   ->			Optional[int]:
 __UpperCamelCase       						= f'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]	)   ->			str:
 return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[str]       ,      snake_case   :Any	)   ->			List[Any]:
 __UpperCamelCase       						= f'repo_zipped_img_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int]	)   ->			Optional[int]:
 return hf_private_dataset_repo_zipped_img_data_



