






'''simple docstring'''





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 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):
		_UpperCAmelCase         =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		_UpperCAmelCase         =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



		def _lowerCAmelCase     (    self:    int				,   a:    Dict				,   a:    Optional[Any]				,   a:    int)  ->Union[str, Any]:






								'''simple docstring'''


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



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






								'''simple docstring'''


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

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

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



		@require_torch
		def _lowerCAmelCase     (    self:    List[str])  ->Optional[int]:






								'''simple docstring'''


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



		@require_tf
		def _lowerCAmelCase     (    self:    List[str])  ->Optional[Any]:






								'''simple docstring'''


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



		@require_torch
		def _lowerCAmelCase     (    self:    Union[str, Any])  ->str:






								'''simple docstring'''


								a_				     =   pipeline("translation_en_to_de"				,   model="patrickvonplaten/t5-tiny-random"				,   framework="pt")
								a_				     =   translator("This is a test string"				,   max_length=20)
								self.assertEqual(
								    a				,   [
								        {
								            "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:    Union[str, Any])  ->Union[str, Any]:






								'''simple docstring'''


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





class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		@require_torch
		@slow
		def _lowerCAmelCase     (    self:    List[Any])  ->str:






								'''simple docstring'''


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

								# but we do for this one
								a_				     =   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:    List[Any])  ->Any:






								'''simple docstring'''


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

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

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

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

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



		@require_torch
		def _lowerCAmelCase     (    self:    Dict)  ->Optional[int]:






								'''simple docstring'''


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



		@require_torch
		def _lowerCAmelCase     (    self:    Union[str, Any])  ->Any:






								'''simple docstring'''


								a_				     =   "patrickvonplaten/t5-tiny-random"
								with pytest.warns(a				,   match=r".*translation_en_to_de.*"):
														a_				     =   pipeline(task="translation"				,   model=a)
								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:    List[Any])  ->Dict:






								'''simple docstring'''


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











'''simple docstring'''





from __future__ import annotations

a_      = [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Optional[int]				,   a:    int)  ->None:






								'''simple docstring'''


								a_				     =   -1
								a_				     =   -1
								a_				     =   [-1] * length
								a_				     =   None
								a_				     =   None




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






								'''simple docstring'''


								return f"""Node(min_value={self.minn} max_value={self.maxx})"""





def 			__UpperCAmelCase				(lowercase__				)		->				Node | None:

						'''simple docstring'''


						a_				     =   Node(len(lowercase__				)				)
						a_     ,       a_				     =   min(lowercase__				), max(lowercase__				)
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						a_				     =   (root.minn + root.maxx) // 2

						a_				     =   []
						a_				     =   []

						for index, num in enumerate(lowercase__				):
												if num <= pivot:
																		left_arr.append(lowercase__				)
												else:
																		right_arr.append(lowercase__				)
												a_				     =   len(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						a_				     =   build_tree(lowercase__				)
						return root



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index < 0 or node is None:
												return 0
						# Leaf node cases
						if node.minn == node.maxx:
												return index + 1 if node.minn == num else 0
						a_				     =   (node.minn + node.maxx) // 2
						if num <= pivot:
												# go the left subtree and map index to the left subtree
												return rank_till_index(node.left	,lowercase__	,node.map_left[index] - 1				)
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right	,lowercase__	,index - node.map_left[index]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if start > end:
												return 0
						a_				     =   rank_till_index(lowercase__	,lowercase__	,lowercase__				)
						a_				     =   rank_till_index(lowercase__	,lowercase__	,start - 1				)
						return rank_till_end - rank_before_start



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if index > (end - start) or start > end or node is None:
												return -1
						# Leaf node case
						if node.minn == node.maxx:
												return node.minn
						# Number of elements in the left subtree in interval [start, end]
						a_				     =   node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left	,lowercase__	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,)
						else:
												return quantile(
												    node.right	,index - num_elements_in_left_tree	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						if (
						    start > end
						    or node is None
						    or start_num > end_num
						    or node.minn > end_num
						    or node.maxx < start_num
						):
												return 0
						if start_num <= node.minn and node.maxx <= end_num:
												return end - start + 1
						a_				     =   range_counting(
						    node.left	,(node.map_left[start - 1] if start else 0)	,node.map_left[end] - 1	,lowercase__	,lowercase__	,)
						a_				     =   range_counting(
						    node.right	,start - (node.map_left[start - 1] if start else 0)	,end - node.map_left[end]	,lowercase__	,lowercase__	,)
						return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()



