
"""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			:       Any     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       List[str]=13     ,					__UpperCamelCase			:       str=7     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[int]=True     ,					__UpperCamelCase			:       Any=99     ,					__UpperCamelCase			:       List[str]=32     ,					__UpperCamelCase			:       Union[str, Any]=2     ,					__UpperCamelCase			:       Any=4     ,					__UpperCamelCase			:       Dict=37     ,					__UpperCamelCase			:       Optional[Any]="gelu"     ,					__UpperCamelCase			:       Union[str, Any]=0.1     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       List[str]=5_12     ,					__UpperCamelCase			:       List[Any]=16     ,					__UpperCamelCase			:       str=2     ,					__UpperCamelCase			:       Optional[int]=0.0_2     ,					__UpperCamelCase			:       List[Any]=3     ,					__UpperCamelCase			:       List[Any]=4     ,					__UpperCamelCase			:       str=None     ,					)		->List[Any]:






        '''simple docstring'''






        _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          =   5_12
        _UpperCAmelCase          =   16
        _UpperCAmelCase          =   2
        _UpperCAmelCase          =   0.0_2
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   None







    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        _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=__UpperCamelCase     ,					)

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







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   TFRoFormerModel(config=__UpperCamelCase     )
        _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

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

        _UpperCAmelCase          =   model(__UpperCamelCase     )

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







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






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[str]:






        '''simple docstring'''






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







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_labels
        _UpperCAmelCase          =   TFRoFormerForSequenceClassification(config=__UpperCamelCase     )
        _UpperCAmelCase          =   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

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







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_choices
        _UpperCAmelCase          =   TFRoFormerForMultipleChoice(config=__UpperCamelCase     )
        _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					1     )     ,					(1, self.num_choices, 1)     )
        _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					1     )     ,					(1, self.num_choices, 1)     )
        _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					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(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)     )







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






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   TFRoFormerForQuestionAnswering(config=__UpperCamelCase     )
        _UpperCAmelCase          =   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        _UpperCAmelCase          =   model(__UpperCamelCase     )
        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      _snake_case					(			self			:       int     )		->Optional[Any]:






        '''simple docstring'''






        _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 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Any     =				(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a      :				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 {}
    )

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







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[str]:






        '''simple docstring'''






        if pipeline_test_casse_name == "TextGenerationPipelineTests":
            return True

        return False







    def      _snake_case					(			self			:       int     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   TFRoFormerModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					hidden_size=37     )







    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def      _snake_case					(			self			:       str     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase     )







    @slow
    def      _snake_case					(			self			:       Dict     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""     )
        self.assertIsNotNone(__UpperCamelCase     )







@require_tf
class 		a_						( unittest.TestCase	):







    @slow
    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""     )
        _UpperCAmelCase          =   tf.constant([[0, 1, 2, 3, 4, 5]]     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )[0]

        # TODO Replace vocab size
        _UpperCAmelCase          =   5_00_00

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

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

        # TODO Replace values below with what was printed above.
        _UpperCAmelCase          =   tf.constant(
            [
                [
                    [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
                    [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
                    [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
                ]
            ]     )
        tf.debugging.assert_near(output[:, :3, :3]     ,					__UpperCamelCase     ,					atol=1e-4     )







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







    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






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

        _UpperCAmelCase          =   emba(input_ids.shape     )
        _UpperCAmelCase          =   tf.constant(
            [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]     )

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







    def      _snake_case					(			self			:       Tuple     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   tf.constant(
            [
                [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
                [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
                [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
            ]     )
        _UpperCAmelCase          =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12     ,					embedding_dim=5_12     )
        emba([2, 16, 5_12]     )
        _UpperCAmelCase          =   emba.weight[:3, :5]

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







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







    def      _snake_case					(			self			:       List[str]     )		->Optional[Any]:






        '''simple docstring'''






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

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

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

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

        _UpperCAmelCase          =   tf.constant(
            [
                [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
                [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
                [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
                [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
                [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
                [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
            ]     )
        _UpperCAmelCase          =   tf.constant(
            [
                [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
                [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
                [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
                [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
                [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
                [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
            ]     )

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

"""simple docstring"""



import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

a					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""      )
    parser.add_argument(
        """--validation_file"""					,      type=_A					,      default=_A					,      help="""A csv or a json file containing the validation data."""      )
    parser.add_argument(
        """--max_length"""					,      type=_A					,      default=5					,      help="""The maximum total input sequence length after tokenization."""					,      )
    parser.add_argument(
        """--num_beams"""					,      type=_A					,      default=_A					,      help=(
            """Number of beams to use for evaluation. This argument will be """
            """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."""
        )					,      )
    parser.add_argument(
        """--model_name_or_path"""					,      type=_A					,      help="""Path to pretrained model or model identifier from huggingface.co/models."""					,      required=_A					,      )
    parser.add_argument(
        """--config_name"""					,      type=_A					,      default=_A					,      help="""Pretrained config name or path if not the same as model_name"""					,      )
    parser.add_argument(
        """--device"""					,      type=_A					,      default="""cpu"""					,      help="""Device where the model will be run"""					,      )
    parser.add_argument("""--output_file_path"""					,      type=_A					,      default=_A					,      help="""Where to store the final ONNX file."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   model_dict[model_name].from_pretrained(_A      ).to(_A      )
    _UpperCAmelCase          =   tokenizer_dict[model_name].from_pretrained(_A      )

    if model_name in ["facebook/bart-base"]:
        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   0

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

    _UpperCAmelCase          =   None
    _UpperCAmelCase          =   torch.jit.script(BARTBeamSearchGenerator(_A      )      )

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

        _UpperCAmelCase          =   model.generate(
            inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]					,      num_beams=_A					,      max_length=_A					,      early_stopping=_A					,      decoder_start_token_id=model.config.decoder_start_token_id					,      )

        torch.onnx.export(
            _A					,      (
                inputs["""input_ids"""],
                inputs["""attention_mask"""],
                num_beams,
                max_length,
                model.config.decoder_start_token_id,
            )					,      _A					,      opset_version=1_4					,      input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""]					,      output_names=["""output_ids"""]					,      dynamic_axes={
                """input_ids""": {0: """batch""", 1: """seq"""},
                """output_ids""": {0: """batch""", 1: """seq_out"""},
            }					,      example_outputs=_A					,      )

        logger.info("""Model exported to {}""".format(_A      )      )

        _UpperCAmelCase          =   remove_dup_initializers(os.path.abspath(_A      )      )

        logger.info("""Deduplicated and optimized model written to {}""".format(_A      )      )

        _UpperCAmelCase          =   onnxruntime.InferenceSession(_A      )
        _UpperCAmelCase          =   ort_sess.run(
            _A					,      {
                """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                """num_beams""": np.array(_A      ),
                """max_length""": np.array(_A      ),
                """decoder_start_token_id""": np.array(model.config.decoder_start_token_id      ),
            }					,      )

        np.testing.assert_allclose(summary_ids.cpu().numpy()					,      ort_out[0]					,      rtol=1e-3					,      atol=1e-3      )

        logger.info("""Model outputs from torch and ONNX Runtime are similar."""      )
        logger.info("""Success."""      )


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





    """simple docstring"""

    _UpperCAmelCase          =   parse_args()
    _UpperCAmelCase          =   5
    _UpperCAmelCase          =   4

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,      datefmt="""%m/%d/%Y %H:%M:%S"""					,      level=logging.INFO					,      )

    logger.setLevel(logging.INFO      )
    transformers.utils.logging.set_verbosity_error()

    _UpperCAmelCase          =   torch.device(args.device      )

    _UpperCAmelCase    ,_UpperCAmelCase          =   load_model_tokenizer(args.model_name_or_path					,      _A      )

    if model.config.decoder_start_token_id is None:
        raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined"""      )

    model.to(_A      )

    if args.max_length:
        _UpperCAmelCase          =   args.max_length

    if args.num_beams:
        _UpperCAmelCase          =   args.num_beams

    if args.output_file_path:
        _UpperCAmelCase          =   args.output_file_path
    else:
        _UpperCAmelCase          =   """BART.onnx"""

    logger.info("""Exporting model to ONNX"""      )
    export_and_validate_model(_A					,      _A					,      _A					,      _A					,      _A      )


if __name__ == "__main__":
 main()