




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=7       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					str=True       ,     SCREAMING_SNAKE_CASE__					:					Tuple=True       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					str=99       ,     SCREAMING_SNAKE_CASE__					:					Any=32       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=5       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=4       ,     SCREAMING_SNAKE_CASE__					:					List[str]=37       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=0.1       ,     SCREAMING_SNAKE_CASE__					:					Any=0.1       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=512       ,     SCREAMING_SNAKE_CASE__					:					int=16       ,     SCREAMING_SNAKE_CASE__					:					str=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.02       ,     SCREAMING_SNAKE_CASE__					:					Dict=4       ,     )				->	List[str]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_attention_mask
         lowerCAmelCase__          =			use_token_type_ids
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			vocab_size
         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_attention_heads
         lowerCAmelCase__          =			intermediate_size
         lowerCAmelCase__          =			hidden_act
         lowerCAmelCase__          =			hidden_dropout_prob
         lowerCAmelCase__          =			attention_probs_dropout_prob
         lowerCAmelCase__          =			max_position_embeddings
         lowerCAmelCase__          =			type_vocab_size
         lowerCAmelCase__          =			type_sequence_label_size
         lowerCAmelCase__          =			initializer_range
         lowerCAmelCase__          =			num_choices


    def        a				(	self					:					List[Any]       )				->	str:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_attention_mask:
              lowerCAmelCase__          =			random_attention_mask([self.batch_size, self.seq_length]       )

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

         lowerCAmelCase__          =			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       ,     is_decoder=lowercase_       ,     initializer_range=self.initializer_range       ,     )

         return config, input_ids, token_type_ids, attention_mask


    def        a				(	self					:					List[str]       )				->	Tuple:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict






@require_flax
class 			__lowerCamelCase	(   UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ 							=				True

    snake_case__ 							=				(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )


    def        a				(	self					:					Dict       )				->	Dict:
         lowerCAmelCase__          =			FlaxRoFormerModelTester(self       )


    @slow
    def        a				(	self					:					Optional[Any]       )				->	str:







         for model_class_name in self.all_model_classes:
              lowerCAmelCase__          =			model_class_name.from_pretrained("junnyu/roformer_chinese_small"       ,     from_pt=lowercase_       )
              lowerCAmelCase__          =			model(np.ones((1, 1)       )       )
              self.assertIsNotNone(lowercase_       )






@require_flax
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"       )
         lowerCAmelCase__          =			jnp.array([[0, 1, 2, 3, 4, 5]]       )
         lowerCAmelCase__          =			model(lowercase_       )[0]

         lowerCAmelCase__          =			50_000

         lowerCAmelCase__          =			(1, 6, vocab_size)
         self.assertEqual(output.shape       ,     lowercase_       )

         lowerCAmelCase__          =			jnp.array(
             [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]       )

         self.assertTrue(jnp.allclose(output[:, :3, :3]       ,     lowercase_       ,     atol=1e-4       )       )






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	int:
         for model_result in results.values():
              for batch_size, sequence_length in zip(model_result["bs"]       ,     model_result["ss"]       ):
                   lowerCAmelCase__          =			model_result["result"][batch_size][sequence_length]
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sgugger/tiny-distilbert-classification"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     only_pretrain_model=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	int:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     torchscript=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Cant do half precision"       )
    def        a				(	self					:					Dict       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Union[str, Any]       )				->	Tuple:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         # set architectures equal to `None`
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    @unittest.skipIf(torch_device == "cpu"       ,     "Can't do half precision"       )
    def        a				(	self					:					int       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     fpaa=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					Optional[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result       )
         self.check_results_dict_not_empty(results.memory_inference_result       )


    def        a				(	self					:					List[str]       )				->	Dict:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tinier_bart"
         lowerCAmelCase__          =			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			PyTorchBenchmarkArguments(
             models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,     configs=[config]       )
         lowerCAmelCase__          =			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result       )
         self.check_results_dict_not_empty(results.memory_train_result       )


    def        a				(	self					:					List[Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     save_to_csv=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ,     train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ,     inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ,     train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ,     env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              benchmark.run()
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_time.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "inf_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "train_mem.csv"       )       ).exists()       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "env.csv"       )       ).exists()       )


    def        a				(	self					:					Optional[Any]       )				->	Any:
         lowerCAmelCase__          =			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__					:					List[Any]       ):
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "sequential"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "cumulative"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "current"       )       )
              self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,     "total"       )       )

         with tempfile.TemporaryDirectory() as tmp_dir:
              lowerCAmelCase__          =			PyTorchBenchmarkArguments(
                  models=[MODEL_ID]       ,     training=SCREAMING_SNAKE_CASE__       ,     inference=SCREAMING_SNAKE_CASE__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     log_filename=os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ,     log_print=SCREAMING_SNAKE_CASE__       ,     trace_memory_line_by_line=SCREAMING_SNAKE_CASE__       ,     multi_process=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			benchmark.run()
              _check_summary_is_not_empty(result.inference_summary       )
              _check_summary_is_not_empty(result.train_summary       )
              self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,     "log.txt"       )       ).exists()       )
