



"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


snake_case_								=			logging.get_logger(__name__)







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""mt5"""
						__UpperCamelCase													=				["""past_key_values"""]






						def __init__(   self					:Any  ,    lowercase_					:Optional[Any]=25_01_12  ,    lowercase_					:List[str]=5_12  ,    lowercase_					:Any=64  ,    lowercase_					:List[str]=10_24  ,    lowercase_					:int=8  ,    lowercase_					:int=None  ,    lowercase_					:int=6  ,    lowercase_					:Any=32  ,    lowercase_					:List[Any]=1_28  ,    lowercase_					:int=0.1  ,    lowercase_					:List[str]=1E-6  ,    lowercase_					:Optional[Any]=1.0  ,    lowercase_					:Union[str, Any]="gated-gelu"  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:Union[str, Any]="T5Tokenizer"  ,    lowercase_					:Tuple=False  ,    lowercase_					:str=0  ,    lowercase_					:Optional[Any]=1  ,    lowercase_					:Optional[int]=0  ,    **lowercase_					:Tuple  ,    )							->     List[str]:
												super().__init__(
												    is_encoder_decoder=lowercase_  ,    tokenizer_class=lowercase_  ,    tie_word_embeddings=lowercase_  ,    pad_token_id=lowercase_  ,    eos_token_id=lowercase_  ,    decoder_start_token_id=lowercase_  ,    **lowercase_  ,    )
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					d_model
												UpperCAmelCase			 =					d_kv
												UpperCAmelCase			 =					d_ff
												UpperCAmelCase			 =					num_layers
												UpperCAmelCase			 =					(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												UpperCAmelCase			 =					num_heads
												UpperCAmelCase			 =					relative_attention_num_buckets
												UpperCAmelCase			 =					relative_attention_max_distance
												UpperCAmelCase			 =					dropout_rate
												UpperCAmelCase			 =					layer_norm_epsilon
												UpperCAmelCase			 =					initializer_factor
												UpperCAmelCase			 =					feed_forward_proj
												UpperCAmelCase			 =					use_cache

												UpperCAmelCase			 =					self.feed_forward_proj.split('-'   )
												UpperCAmelCase			 =					act_info[-1]
												UpperCAmelCase			 =					act_info[0] == 'gated'

												if len(lowercase_   ) > 1 and act_info[0] != "gated" or len(lowercase_   ) > 2:
																		raise ValueError(
																		    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																		    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																		    '\'gated-gelu\' or \'relu\''   )

												# for backwards compatibility
												if feed_forward_proj == "gated-gelu":
																		UpperCAmelCase			 =					'gelu_new'






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												return self.d_model






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[int]:
												return self.num_heads






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												return self.num_layers







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Mapping[str, Mapping[int, str]]:
												UpperCAmelCase			 =					{
												    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
												    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
												}
												if self.use_past:
																		UpperCAmelCase			 =					'past_encoder_sequence + sequence'
																		UpperCAmelCase			 =					{0: 'batch'}
																		UpperCAmelCase			 =					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
												else:
																		UpperCAmelCase			 =					{0: 'batch', 1: 'decoder_sequence'}
																		UpperCAmelCase			 =					{0: 'batch', 1: 'decoder_sequence'}

												if self.use_past:
																		self.fill_with_past_key_values_(lowercase_  ,    direction='inputs'   )

												return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												return 13






						@property
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     float:
												return 5E-4










import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				lowercase__     =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									if isinstance(model.config  ,			snake_case__     ):
														UpperCamelCase_	,     UpperCamelCase_			= list(tokenizer.lang_code_to_id.keys()     )[:2]
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang=snake_case__  ,			tgt_lang=snake_case__     )
									else:
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__     )
									return translator, ["Some string", "Some other text"]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= translator("Some string"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string", "other string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}, {"translation_text": ANY(snake_case__     )}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)




class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation_cn_to_ar"     )

									# but we do for this one
									UpperCamelCase_			= pipeline(task="translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
									UpperCamelCase_			= MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__     )
									# Missing src_lang, tgt_lang
									with self.assertRaises(snake_case__     ):
														translator("This is a test"     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="hi_IN"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "यह एक परीक्षण है"}]     )

									# src_lang, tgt_lang can be defined at pipeline call time
									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									UpperCamelCase_			= translator("This is a test"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									UpperCamelCase_			= pipeline(task="translation_cn_to_ar"  ,			model=snake_case__     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"cn"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"ar"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									with pytest.warns(snake_case__  ,			match=R".*translation_en_to_de.*"     ):
														UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__     )
									self.assertEqual(translator.task  ,			"translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation"     )
