



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	:      Dict      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      int=13      ,					lowerCAmelCase	:      str=7      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      int=True      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      Optional[Any]=True      ,					lowerCAmelCase	:      Optional[Any]=99      ,					lowerCAmelCase	:      Any=32      ,					lowerCAmelCase	:      List[str]=2      ,					lowerCAmelCase	:      Optional[int]=4      ,					lowerCAmelCase	:      Dict=37      ,					lowerCAmelCase	:      List[Any]="gelu"      ,					lowerCAmelCase	:      Any=0.1      ,					lowerCAmelCase	:      Any=0.1      ,					lowerCAmelCase	:      List[Any]=5_12      ,					lowerCAmelCase	:      Union[str, Any]=16      ,					lowerCAmelCase	:      Dict=2      ,					lowerCAmelCase	:      Dict=0.02      ,					lowerCAmelCase	:      Any=3      ,					lowerCAmelCase	:      Union[str, Any]=4      ,					lowerCAmelCase	:      Optional[Any]=None      ,					)						->					str:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	13
         lowercase__  =	7
         lowercase__  =	True
         lowercase__  =	True
         lowercase__  =	True
         lowercase__  =	True
         lowercase__  =	99
         lowercase__  =	32
         lowercase__  =	2
         lowercase__  =	4
         lowercase__  =	37
         lowercase__  =	'gelu'
         lowercase__  =	0.1
         lowercase__  =	0.1
         lowercase__  =	5_12
         lowercase__  =	16
         lowercase__  =	2
         lowercase__  =	0.02
         lowercase__  =	3
         lowercase__  =	4
         lowercase__  =	None


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Tuple:




         """simple docstring"""

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

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

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

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

         lowercase__  =	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       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[Any])						->					Tuple:




         """simple docstring"""

         lowercase__  =	TFRoFormerModel(config=lowerCAmelCase)
         lowercase__  =	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

         lowercase__  =	[input_ids, input_mask]
         lowercase__  =	model(lowerCAmelCase)

         lowercase__  =	model(lowerCAmelCase)

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


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Tuple)						->					Tuple:




         """simple docstring"""

         lowercase__  =	True
         lowercase__  =	TFRoFormerForCausalLM(config=lowerCAmelCase)
         lowercase__  =	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         lowercase__  =	model(lowerCAmelCase)['logits']
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape)      ,					[self.batch_size, self.seq_length, self.vocab_size])


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Optional[Any])						->					Dict:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int])						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	TFRoFormerForSequenceClassification(config=lowerCAmelCase)
         lowercase__  =	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels))


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      str)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.num_choices
         lowercase__  =	TFRoFormerForMultipleChoice(config=lowerCAmelCase)
         lowercase__  =	tf.tile(tf.expand_dims(lowerCAmelCase      ,					1)      ,					(1, self.num_choices, 1))
         lowercase__  =	tf.tile(tf.expand_dims(lowerCAmelCase      ,					1)      ,					(1, self.num_choices, 1))
         lowercase__  =	tf.tile(tf.expand_dims(lowerCAmelCase      ,					1)      ,					(1, self.num_choices, 1))
         lowercase__  =	{
             'input_ids': multiple_choice_inputs_ids,
             'attention_mask': multiple_choice_input_mask,
             'token_type_ids': multiple_choice_token_type_ids,
         }
         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_choices))


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Any)						->					Any:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	TFRoFormerForTokenClassification(config=lowerCAmelCase)
         lowercase__  =	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }
         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels))


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Tuple)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	TFRoFormerForQuestionAnswering(config=lowerCAmelCase)
         lowercase__  =	{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'token_type_ids': token_type_ids,
         }

         lowercase__  =	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       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

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



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

   '''simple docstring'''




   A     :	Dict												=     (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   A     :	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 {}
   )

   A     :	List[str]												=     False
   A     :	List[str]												=     False


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Optional[Any])						->					Tuple:




         """simple docstring"""

         if pipeline_test_casse_name == "TextGenerationPipelineTests":
               return True

         return False


   def       UpperCAmelCase		(    self	:      Tuple)						->					int:




         """simple docstring"""

         lowercase__  =	TFRoFormerModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					hidden_size=37)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

         self.config_tester.run_common_tests()


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Dict:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Any)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      int)						->					str:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Dict)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase)



   @slow
   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
         self.assertIsNotNone(lowerCAmelCase)



@require_tf
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   @slow
   def       UpperCAmelCase		(    self	:      Optional[int])						->					List[str]:




         """simple docstring"""

         lowercase__  =	TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
         lowercase__  =	tf.constant([[0, 1, 2, 3, 4, 5]])
         lowercase__  =	model(lowerCAmelCase)[0]

         # TODO Replace vocab size
         lowercase__  =	5_00_00

         lowercase__  =	[1, 6, vocab_size]
         self.assertEqual(output.shape      ,					lowerCAmelCase)

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

         # TODO Replace values below with what was printed above.
         lowercase__  =	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    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   A     :	Any												=     1e-4


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	tf.constant([[4, 10]])
         lowercase__  =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6      ,					embedding_dim=6)

         lowercase__  =	emba(input_ids.shape)
         lowercase__  =	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       UpperCAmelCase		(    self	:      List[str])						->					Dict:




         """simple docstring"""

         lowercase__  =	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],
             ])
         lowercase__  =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12      ,					embedding_dim=5_12)
         emba([2, 16, 5_12])
         lowercase__  =	emba.weight[:3, :5]

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



@require_tf
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     1e-4


   def       UpperCAmelCase		(    self	:      Dict)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	tf.reshape(tf.range(2 * 12 * 16 * 64      ,					dtype=tf.floataa)      ,					shape=(2, 12, 16, 64)) / 1_00

         lowercase__  =	-tf.reshape(tf.range(2 * 12 * 16 * 64      ,					dtype=tf.floataa)      ,					shape=(2, 12, 16, 64)) / 1_00

         lowercase__  =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=32      ,					embedding_dim=64)
         lowercase__  =	embed_positions([2, 16, 7_68])[None, None, :, :]

         lowercase__,						lowercase__  =	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         lowercase__  =	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],
             ])
         lowercase__  =	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)





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	_lowerCAmelCase (    A__=None						):
      if subparsers is not None:
            lowercase__  =	subparsers.add_parser('test'						)
      else:
            lowercase__  =	argparse.ArgumentParser('Accelerate test command'						)

      parser.add_argument(
          '--config_file'    ,	default=A__    ,	help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )    ,	)

      if subparsers is not None:
            parser.set_defaults(func=A__						)
      return parser

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ['test_utils', 'scripts', 'test_script.py']						)

      if args.config_file is None:
            lowercase__  =	script_name
      else:
            lowercase__  =	F'''--config_file={args.config_file} {script_name}'''

      lowercase__  =	['accelerate-launch'] + test_args.split()
      lowercase__  =	execute_subprocess_async(A__    ,	env=os.environ.copy()						)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'						)

def 	_lowerCAmelCase (    ):
      lowercase__  =	test_command_parser()
      lowercase__  =	parser.parse_args()
      test_command(A__						)


if __name__ == "__main__":
       main()
