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




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Tuple       =    ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size     )

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

         _lowerCAmelCase				:     List[Any]       =    None
         if self.use_token_type_ids:
           _lowerCAmelCase				:     List[str]       =    ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size     )

         _lowerCAmelCase				:     Tuple       =    None
         _lowerCAmelCase				:     Optional[int]       =    None
         _lowerCAmelCase				:     int       =    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				:     Tuple       =    ids_tensor([self.batch_size]      ,      self.num_choices     )

         _lowerCAmelCase				:     Tuple       =    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=lowercase_      ,      )

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




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     Optional[int]       =    TFRoFormerModel(config=lowercase_     )
         _lowerCAmelCase				:     Optional[Any]       =    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         _lowerCAmelCase				:     str       =    [input_ids, input_mask]
         _lowerCAmelCase				:     str       =    model(lowercase_     )

         _lowerCAmelCase				:     Optional[int]       =    model(lowercase_     )

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




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     Dict       =    True
         _lowerCAmelCase				:     Tuple       =    TFRoFormerForCausalLM(config=lowercase_     )
         _lowerCAmelCase				:     List[str]       =    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCAmelCase				:     Dict       =    model(lowercase_     )['''logits''']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape     )      ,      [self.batch_size, self.seq_length, self.vocab_size]     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     Optional[Any]       =    TFRoFormerForMaskedLM(config=lowercase_     )
         _lowerCAmelCase				:     Dict       =    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCAmelCase				:     Union[str, Any]       =    model(lowercase_     )
         self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     List[str]       =    self.num_labels
         _lowerCAmelCase				:     List[str]       =    TFRoFormerForSequenceClassification(config=lowercase_     )
         _lowerCAmelCase				:     int       =    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         _lowerCAmelCase				:     str       =    model(lowercase_     )
         self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     List[Any]       =    self.num_choices
         _lowerCAmelCase				:     Tuple       =    TFRoFormerForMultipleChoice(config=lowercase_     )
         _lowerCAmelCase				:     List[str]       =    tf.tile(tf.expand_dims(lowercase_      ,      1     )      ,      (1, self.num_choices, 1)     )
         _lowerCAmelCase				:     Tuple       =    tf.tile(tf.expand_dims(lowercase_      ,      1     )      ,      (1, self.num_choices, 1)     )
         _lowerCAmelCase				:     Tuple       =    tf.tile(tf.expand_dims(lowercase_      ,      1     )      ,      (1, self.num_choices, 1)     )
         _lowerCAmelCase				:     Optional[Any]       =    {
             '''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(lowercase_     )
         self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     Any       =    self.num_labels
         _lowerCAmelCase				:     List[Any]       =    TFRoFormerForTokenClassification(config=lowercase_     )
         _lowerCAmelCase				:     Optional[int]       =    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         _lowerCAmelCase				:     Optional[int]       =    model(lowercase_     )
         self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)     )




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         _lowerCAmelCase				:     List[Any]       =    TFRoFormerForQuestionAnswering(config=lowercase_     )
         _lowerCAmelCase				:     Optional[int]       =    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

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



@require_tf
class      a_     (a_							,	a_							,	unittest.TestCase			):
       __lowerCAmelCase			: Union[str, Any]			=					(
           (
               TFRoFormerModel,
               TFRoFormerForCausalLM,
               TFRoFormerForMaskedLM,
               TFRoFormerForQuestionAnswering,
               TFRoFormerForSequenceClassification,
               TFRoFormerForTokenClassification,
               TFRoFormerForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __lowerCAmelCase			: 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 {}
       )

       __lowerCAmelCase			: Any			=					False
       __lowerCAmelCase			: Tuple			=					False




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
           return True

         return False




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Any       =    TFRoFormerModelTester(self     )
         _lowerCAmelCase				:     Any       =    ConfigTester(self      ,      config_class=lowercase_      ,      hidden_size=3_7     )




       def 							__UpperCamelCase				(   self     ):
         self.config_tester.run_common_tests()




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Tuple       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     str       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Optional[int]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Union[str, Any]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     List[Any]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowercase_     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     List[Any]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowercase_     )







       @slow
       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""     )
         self.assertIsNotNone(lowercase_     )



@require_tf
class      a_     (unittest.TestCase			):




       @slow
       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""     )
         _lowerCAmelCase				:     Dict       =    tf.constant([[0, 1, 2, 3, 4, 5]]     )
         _lowerCAmelCase				:     Optional[int]       =    model(lowercase_     )[0]

         # TODO Replace vocab size
         _lowerCAmelCase				:     int       =    5_0_0_0_0

         _lowerCAmelCase				:     int       =    [1, 6, vocab_size]
         self.assertEqual(output.shape      ,      lowercase_     )

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

         # TODO Replace values below with what was printed above.
         _lowerCAmelCase				:     Optional[int]       =    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]      ,      lowercase_      ,      atol=1E-4     )



@require_tf
class      a_     (unittest.TestCase			):
       __lowerCAmelCase			: Tuple			=					1E-4




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     List[Any]       =    tf.constant([[4, 1_0]]     )
         _lowerCAmelCase				:     Optional[int]       =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,      embedding_dim=6     )

         _lowerCAmelCase				:     List[Any]       =    emba(input_ids.shape     )
         _lowerCAmelCase				:     int       =    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(lowercase_      ,      lowercase_      ,      atol=self.tolerance     )







       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Tuple       =    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				:     Tuple       =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2      ,      embedding_dim=5_1_2     )
         emba([2, 1_6, 5_1_2]     )
         _lowerCAmelCase				:     int       =    emba.weight[:3, :5]

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



@require_tf
class      a_     (unittest.TestCase			):
       __lowerCAmelCase			: Union[str, Any]			=					1E-4




       def 							__UpperCamelCase				(   self     ):
         # 2,12,16,64
         _lowerCAmelCase				:     Optional[Any]       =    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]       =    -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]       =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2      ,      embedding_dim=6_4     )
         _lowerCAmelCase				:     str       =    embed_positions([2, 1_6, 7_6_8]     )[None, None, :, :]

         _lowerCAmelCase				:     Any       =    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             lowercase_      ,      lowercase_      ,      lowercase_     )

         _lowerCAmelCase				:     str       =    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				:     Union[str, Any]       =    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]      ,      lowercase_      ,      atol=self.tolerance     )
         tf.debugging.assert_near(key_layer[0, 0, :6, :8]      ,      lowercase_      ,      atol=self.tolerance     )





"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




