




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   ):
						lowerCAmelCase__ 			=MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						lowerCAmelCase__ 			=TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



						def   __a				( self  :      Any				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Dict				,       snake_case__  :      int     ):

								"""simple docstring"""

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



						def   __a				( self  :      Optional[int]				,       snake_case__  :      Tuple				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

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

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

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



						@require_torch
						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline('translation_en_to_ro'				,       model='patrickvonplaten/t5-tiny-random'				,       framework='pt'     )
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline('translation_en_to_ro'				,       model='patrickvonplaten/t5-tiny-random'				,       framework='tf'     )
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline('translation_en_to_de'				,       model='patrickvonplaten/t5-tiny-random'				,       framework='pt'     )
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline('translation_en_to_de'				,       model='patrickvonplaten/t5-tiny-random'				,       framework='tf'     )
								SCREAMING_SNAKE_CASE_							=       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   ):



						@require_torch
						@slow
						def   __a				( self  :      Any     ):

								"""simple docstring"""

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

								# but we do for this one
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

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

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

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

								SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       pipeline(task='translation'				,       model=snake_case__				,       tokenizer=snake_case__				,       src_lang='en_XX'				,       tgt_lang='ar_AR'     )
								SCREAMING_SNAKE_CASE_							=       translator('This is a test'     )
								self.assertEqual(snake_case__				,       [{'translation_text': 'هذا إختبار'}]     )



						@require_torch
						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       'patrickvonplaten/t5-tiny-random'
								SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       'patrickvonplaten/t5-tiny-random'
								with pytest.warns(snake_case__				,       match=R'.*translation_en_to_de.*'     ):
										SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str     ):

								"""simple docstring"""

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





SCREAMING_SNAKE_CASE:    Optional[Any]											=   [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE:    int											=   3
SCREAMING_SNAKE_CASE:    str											=   5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE:    list[float]											=   []
SCREAMING_SNAKE_CASE:    list[float]											=   []

# Machine Time
SCREAMING_SNAKE_CASE:    str											=   0


def 				_a       (				lowerCAmelCase   )->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(lowerCAmelCase   ):
				# Evolution Parameter
				SCREAMING_SNAKE_CASE_							=       float(seed / value   )

				# Control Theory: Orbit Change
				SCREAMING_SNAKE_CASE_							=       (buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				SCREAMING_SNAKE_CASE_							=       (params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       r  # Saving to Parameters Space

		# Logistic Map
		assert max(lowerCAmelCase   ) < 1
		assert max(lowerCAmelCase   ) < 4

		# Machine Time
		machine_time += 1


def 				_a       (				)->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(lowerCAmelCase						,	lowerCAmelCase   ):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		SCREAMING_SNAKE_CASE_							=       machine_time % m

		# Evolution (Time Length)
		for _ in range(0						,	lowerCAmelCase   ):
				# Variables (Position + Parameters)
				SCREAMING_SNAKE_CASE_							=       params_space[key]
				SCREAMING_SNAKE_CASE_							=       buffer_space[key]

				# Modification (Transition Function) - Flow
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

		# Choosing Chaotic Data
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key + 2) % m] * (10**10)   )
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key - 2) % m] * (10**10)   )

		# Machine Time
		machine_time += 1

		return xorshift(lowerCAmelCase						,	lowerCAmelCase   ) % 0xFFFF_FFFF


def 				_a       (				)->  Optional[Any]:
		global buffer_space, params_space, machine_time, K, m, t

		SCREAMING_SNAKE_CASE_							=       K
		SCREAMING_SNAKE_CASE_							=       [0] * m
		SCREAMING_SNAKE_CASE_							=       0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						SCREAMING_SNAKE_CASE:    List[Any]											=   random.sample(range(0xff_ff_ff_ff), 1_0_0)
						for chunk in message:
												push(chunk)

						# for controlling
						SCREAMING_SNAKE_CASE:    List[str]											=   ''''''

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f"""{format(pull(), '#04x')}""")
												print(buffer_space)
												print(params_space)
												SCREAMING_SNAKE_CASE:    List[Any]											=   input('''(e)exit? ''').strip()