




"""simple docstring"""

from .number_theory.prime_numbers import next_prime
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	):



					"""simple docstring"""







					snake_case						:  str        =			size_table
					snake_case						:  List[str]        =			[None] * self.size_table
					snake_case						:  Dict        =			0.75 if lim_charge is None else lim_charge
					snake_case						:  Optional[Any]        =			1 if charge_factor is None else charge_factor
					snake_case						:  list        =			[]
					snake_case						:  dict        =			{}


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return self._keys


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return sum(1 for slot in self.values if slot is not None							) / (
					    self.size_table * self.charge_factor
					)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return key % self.size_table


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					print(F'''step {step_ord}'''							)
					print(list(range(len(self.values							)							)							)							)
					print(self.values							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  int        =			1
					snake_case						:  Union[str, Any]        =			values
					for value in values:
							self.insert_data(SCREAMING_SNAKE_CASE							)
							self._step_by_step(SCREAMING_SNAKE_CASE							)
							i += 1


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			data
					snake_case						:  Optional[Any]        =			data


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.hash_function(key + 1							)

					while self.values[new_key] is not None and self.values[new_key] != key:
							if self.values.count(SCREAMING_SNAKE_CASE							) > 0:
									snake_case						:  int        =			self.hash_function(new_key + 1							)
							else:
									snake_case						:  str        =			None
									break

					return new_key


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			[value for value in self.values if value is not None]
					snake_case						:  List[str]        =			next_prime(self.size_table			,	factor=2							)
					self._keys.clear()
					snake_case						:  Optional[Any]        =			[None] * self.size_table  # hell's pointers D: don't DRY ;/
					for value in survivor_values:
							self.insert_data(SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.hash_function(SCREAMING_SNAKE_CASE							)

					if self.values[key] is None:
							self._set_value(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					elif self.values[key] == data:
							pass

					else:
							snake_case						:  Optional[Any]        =			self._collision_resolution(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
							if collision_resolution is not None:
									self._set_value(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
							else:
									self.rehashing()
									self.insert_data(SCREAMING_SNAKE_CASE							)







"""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      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_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"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_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"""







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







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







class      lowerCamelCase__      (					unittest.TestCase     ):


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					# but we do for this one
					snake_case						:  List[str]        =			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"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

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

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

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

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


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_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"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_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(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation"							)

