


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     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=7					,   A_=True					,   A_=True					,   A_=True					,   A_=True					,   A_=99					,   A_=32					,   A_=2					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=16					,   A_=2					,   A_=0.02					,   A_=3					,   A_=4					,   A_=None					,   )  ->       Tuple:
         __UpperCamelCase    							=parent
         __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    							=0.02
         __UpperCamelCase    							=3
         __UpperCamelCase    							=4
         __UpperCamelCase    							=None






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

         __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    							=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=A_					,   )

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






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int:
         __UpperCamelCase    							=TFRoFormerModel(config=A_    )
         __UpperCamelCase    							={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

         __UpperCamelCase    							=[input_ids, input_mask]
         __UpperCamelCase    							=model(A_    )

         __UpperCamelCase    							=model(A_    )

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






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int:
         __UpperCamelCase    							=True
         __UpperCamelCase    							=TFRoFormerForCausalLM(config=A_    )
         __UpperCamelCase    							={
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         __UpperCamelCase    							=model(A_    )['logits']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape    )					,   [self.batch_size, self.seq_length, self.vocab_size]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=TFRoFormerForMaskedLM(config=A_    )
         __UpperCamelCase    							={
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Dict:
         __UpperCamelCase    							=self.num_labels
         __UpperCamelCase    							=TFRoFormerForSequenceClassification(config=A_    )
         __UpperCamelCase    							={
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=self.num_choices
         __UpperCamelCase    							=TFRoFormerForMultipleChoice(config=A_    )
         __UpperCamelCase    							=tf.tile(tf.expand_dims(A_					,   1    )					,   (1, self.num_choices, 1)    )
         __UpperCamelCase    							=tf.tile(tf.expand_dims(A_					,   1    )					,   (1, self.num_choices, 1)    )
         __UpperCamelCase    							=tf.tile(tf.expand_dims(A_					,   1    )					,   (1, self.num_choices, 1)    )
         __UpperCamelCase    							={
             'input_ids': multiple_choice_inputs_ids,
             'attention_mask': multiple_choice_input_mask,
             'token_type_ids': multiple_choice_token_type_ids,
         }
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_choices)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=self.num_labels
         __UpperCamelCase    							=TFRoFormerForTokenClassification(config=A_    )
         __UpperCamelCase    							={
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=TFRoFormerForQuestionAnswering(config=A_    )
         __UpperCamelCase    							={
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         __UpperCamelCase    							=model(A_    )
         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 _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs
         __UpperCamelCase    							={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_tf
class     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  (
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase__						:		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 {}
    )

    UpperCAmelCase__						:		Optional[int]      =  False
    UpperCAmelCase__						:		Optional[Any]      =  False






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       List[Any]:
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              return True

         return False






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=TFRoFormerModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Tuple:
         self.config_tester.run_common_tests()






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A_    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A_    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A_    )






    @slow
    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'    )
         self.assertIsNotNone(A_    )






@require_tf
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @slow
    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
         __UpperCamelCase    							=tf.constant([[0, 1, 2, 3, 4, 5]]    )
         __UpperCamelCase    							=model(A_    )[0]

         # TODO Replace vocab size
         __UpperCamelCase    							=50000

         __UpperCamelCase    							=[1, 6, vocab_size]
         self.assertEqual(output.shape					,   A_    )

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

         # TODO Replace values below with what was printed above.
         __UpperCamelCase    							=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]					,   A_					,   atol=1E-4    )






@require_tf
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  1e-4






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=tf.constant([[4, 10]]    )
         __UpperCamelCase    							=TFRoFormerSinusoidalPositionalEmbedding(num_positions=6					,   embedding_dim=6    )

         __UpperCamelCase    							=emba(input_ids.shape    )
         __UpperCamelCase    							=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(A_					,   A_					,   atol=self.tolerance    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=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],
             ]    )
         __UpperCamelCase    							=TFRoFormerSinusoidalPositionalEmbedding(num_positions=512					,   embedding_dim=512    )
         emba([2, 16, 512]    )
         __UpperCamelCase    							=emba.weight[:3, :5]

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






@require_tf
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[str]      =  1e-4






    def _a					(			self    )  ->       Optional[int]:
         # 2,12,16,64
         __UpperCamelCase    							=tf.reshape(tf.range(2 * 12 * 16 * 64					,   dtype=tf.floataa    )					,   shape=(2, 12, 16, 64)    ) / 100

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

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

         __UpperCamelCase							,      __UpperCamelCase    							=TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             A_					,   A_					,   A_    )

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









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     __UpperCamelCase    							=max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)
     __UpperCamelCase    							=20 * np.logaa(SCREAMING_SNAKE_CASE__				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     # Display within reasonable bounds
     __UpperCamelCase    							=get_bounds(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     plt.ylim(max([-80, bounds[0]]				)			,     min([80, bounds[1]]				)				)
     plt.ylabel('Gain (dB)'				)

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     plt.ylim(-2 * pi			,     2 * pi				)
     plt.ylabel('Phase shift (Radians)'				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__			,     -2 * pi				)				)
     plt.show()





