



"""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        __snake_case       :




  def __init__( self     :						List[Any]						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Any=1_3						,					__lowerCAmelCase     :						Tuple=7						,					__lowerCAmelCase     :						Dict=True						,					__lowerCAmelCase     :						Optional[int]=True						,					__lowerCAmelCase     :						List[str]=True						,					__lowerCAmelCase     :						Union[str, Any]=True						,					__lowerCAmelCase     :						Tuple=9_9						,					__lowerCAmelCase     :						Tuple=3_2						,					__lowerCAmelCase     :						Any=2						,					__lowerCAmelCase     :						Optional[int]=4						,					__lowerCAmelCase     :						List[str]=3_7						,					__lowerCAmelCase     :						str="gelu"						,					__lowerCAmelCase     :						str=0.1						,					__lowerCAmelCase     :						Tuple=0.1						,					__lowerCAmelCase     :						Dict=5_1_2						,					__lowerCAmelCase     :						List[Any]=1_6						,					__lowerCAmelCase     :						List[Any]=2						,					__lowerCAmelCase     :						List[str]=0.02						,					__lowerCAmelCase     :						Dict=3						,					__lowerCAmelCase     :						int=4						,					__lowerCAmelCase     :						str=None						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= parent
         _lowerCamelCase						:				Any    			= 1_3
         _lowerCamelCase						:				List[str]    			= 7
         _lowerCamelCase						:				List[str]    			= True
         _lowerCamelCase						:				Optional[int]    			= True
         _lowerCamelCase						:				Dict    			= True
         _lowerCamelCase						:				Dict    			= True
         _lowerCamelCase						:				Union[str, Any]    			= 9_9
         _lowerCamelCase						:				str    			= 3_2
         _lowerCamelCase						:				Optional[int]    			= 2
         _lowerCamelCase						:				Union[str, Any]    			= 4
         _lowerCamelCase						:				Tuple    			= 3_7
         _lowerCamelCase						:				Optional[int]    			= '''gelu'''
         _lowerCamelCase						:				List[str]    			= 0.1
         _lowerCamelCase						:				str    			= 0.1
         _lowerCamelCase						:				List[str]    			= 5_1_2
         _lowerCamelCase						:				Union[str, Any]    			= 1_6
         _lowerCamelCase						:				Any    			= 2
         _lowerCamelCase						:				Any    			= 0.02
         _lowerCamelCase						:				int    			= 3
         _lowerCamelCase						:				Optional[Any]    			= 4
         _lowerCamelCase						:				Optional[Any]    			= None




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size		)

         _lowerCamelCase						:				List[str]    			= None
         if self.use_input_mask:
                _lowerCamelCase						:				List[Any]    			= random_attention_mask([self.batch_size, self.seq_length]		)

         _lowerCamelCase						:				Optional[int]    			= None
         if self.use_token_type_ids:
                _lowerCamelCase						:				Tuple    			= ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size		)

         _lowerCamelCase						:				Any    			= None
         _lowerCamelCase						:				Any    			= None
         _lowerCamelCase						:				List[Any]    			= None
         if self.use_labels:
                _lowerCamelCase						:				Union[str, Any]    			= ids_tensor([self.batch_size]						,					self.type_sequence_label_size		)
                _lowerCamelCase						:				Union[str, Any]    			= ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels		)
                _lowerCamelCase						:				str    			= ids_tensor([self.batch_size]						,					self.num_choices		)

         _lowerCamelCase						:				Dict    			= 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=__lowerCAmelCase						,					)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= TFRoFormerModel(config=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         _lowerCamelCase						:				Dict    			= [input_ids, input_mask]
         _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase		)

         _lowerCamelCase						:				Union[str, Any]    			= model(__lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= True
         _lowerCamelCase						:				Any    			= TFRoFormerForCausalLM(config=__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCamelCase						:				Union[str, Any]    			= model(__lowerCAmelCase		)['''logits''']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape		)						,					[self.batch_size, self.seq_length, self.vocab_size]		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= TFRoFormerForMaskedLM(config=__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCamelCase						:				Any    			= model(__lowerCAmelCase		)
         self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= self.num_labels
         _lowerCamelCase						:				str    			= TFRoFormerForSequenceClassification(config=__lowerCAmelCase		)
         _lowerCamelCase						:				Any    			= {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         _lowerCamelCase						:				Union[str, Any]    			= model(__lowerCAmelCase		)
         self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.num_choices
         _lowerCamelCase						:				Union[str, Any]    			= TFRoFormerForMultipleChoice(config=__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[int]    			= tf.tile(tf.expand_dims(__lowerCAmelCase						,					1		)						,					(1, self.num_choices, 1)		)
         _lowerCamelCase						:				Union[str, Any]    			= tf.tile(tf.expand_dims(__lowerCAmelCase						,					1		)						,					(1, self.num_choices, 1)		)
         _lowerCamelCase						:				List[Any]    			= tf.tile(tf.expand_dims(__lowerCAmelCase						,					1		)						,					(1, self.num_choices, 1)		)
         _lowerCamelCase						:				str    			= {
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         _lowerCamelCase						:				Union[str, Any]    			= model(__lowerCAmelCase		)
         self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_choices)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= self.num_labels
         _lowerCamelCase						:				Dict    			= TFRoFormerForTokenClassification(config=__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase		)
         self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= TFRoFormerForQuestionAnswering(config=__lowerCAmelCase		)
         _lowerCamelCase						:				str    			= {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         _lowerCamelCase						:				Dict    			= model(__lowerCAmelCase		)
         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     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= self.prepare_config_and_inputs()
         (
             (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    (
             _lowerCamelCase
         )			,    
         )						:				str    			= config_and_inputs
         _lowerCamelCase						:				Optional[Any]    			= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_tf
class        __snake_case       (	_lowercase  ,     _lowercase  ,     unittest.TestCase):
  snake_case__				:   Tuple  			=						(
      (
          TFRoFormerModel,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  snake_case__				:   int  			=						(
      {
          "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 {}
  )

  snake_case__				:   Tuple  			=						False
  snake_case__				:   Optional[Any]  			=						False




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

         return False




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= TFRoFormerModelTester(self		)
         _lowerCamelCase						:				Any    			= ConfigTester(self						,					config_class=__lowerCAmelCase						,					hidden_size=3_7		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         self.config_tester.run_common_tests()




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase		)




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''		)
         self.assertIsNotNone(__lowerCAmelCase		)






@require_tf
class        __snake_case       (	unittest.TestCase):




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''		)
         _lowerCamelCase						:				List[Any]    			= tf.constant([[0, 1, 2, 3, 4, 5]]		)
         _lowerCamelCase						:				List[Any]    			= model(__lowerCAmelCase		)[0]

         # TODO Replace vocab size
         _lowerCamelCase						:				int    			= 5_0_0_0_0

         _lowerCamelCase						:				Optional[int]    			= [1, 6, vocab_size]
         self.assertEqual(output.shape						,					__lowerCAmelCase		)

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

         # TODO Replace values below with what was printed above.
         _lowerCamelCase						:				str    			= tf.constant(
             [
                 [
                     [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                     [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                     [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
                 ]
             ]		)
         tf.debugging.assert_near(output[:, :3, :3]						,					__lowerCAmelCase						,					atol=1E-4		)






@require_tf
class        __snake_case       (	unittest.TestCase):
  snake_case__				:   Union[str, Any]  			=						1e-4




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= tf.constant([[4, 1_0]]		)
         _lowerCamelCase						:				Tuple    			= TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,					embedding_dim=6		)

         _lowerCamelCase						:				Any    			= emba(input_ids.shape		)
         _lowerCamelCase						:				Union[str, Any]    			= tf.constant(
             [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= tf.constant(
             [
                 [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
                 [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
                 [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
             ]		)
         _lowerCamelCase						:				Any    			= TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2						,					embedding_dim=5_1_2		)
         emba([2, 1_6, 5_1_2]		)
         _lowerCamelCase						:				Optional[Any]    			= emba.weight[:3, :5]

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






@require_tf
class        __snake_case       (	unittest.TestCase):
  snake_case__				:   List[str]  			=						1e-4




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4						,					dtype=tf.floataa		)						,					shape=(2, 1_2, 1_6, 6_4)		) / 1_0_0

         _lowerCamelCase						:				Optional[int]    			= -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4						,					dtype=tf.floataa		)						,					shape=(2, 1_2, 1_6, 6_4)		) / 1_0_0

         _lowerCamelCase						:				Optional[Any]    			= TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2						,					embedding_dim=6_4		)
         _lowerCamelCase						:				Optional[Any]    			= embed_positions([2, 1_6, 7_6_8]		)[None, None, :, :]

         _lowerCamelCase			,    _lowerCamelCase						:				str    			= TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             __lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase		)

         _lowerCamelCase						:				Union[str, Any]    			= tf.constant(
             [
                 [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
                 [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
                 [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
                 [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
                 [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
                 [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
             ]		)
         _lowerCamelCase						:				Any    			= tf.constant(
             [
                 [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
                 [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
                 [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
                 [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
                 [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
                 [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
             ]		)

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








"""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  )