
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 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	snake_case_      					=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






	def        UpperCamelCase_						( self      :				Any							,A      :				str							,A      :				Any							,A      :				Any				):
					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        UpperCamelCase_						( self      :				List[Any]							,A      :				List[str]							,A      :				List[str]				):
					__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        UpperCamelCase_						( self      :				str				):
					__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        UpperCamelCase_						( self      :				Any				):
					__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        UpperCamelCase_						( self      :				Optional[int]				):
					__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        UpperCamelCase_						( self      :				Tuple				):
					__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 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	@require_torch
	@slow
	def        UpperCamelCase_						( self      :				Any				):
					# We don't provide a default for this pair
					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        UpperCamelCase_						( self      :				Optional[Any]				):
					__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        UpperCamelCase_						( self      :				Dict				):
					__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        UpperCamelCase_						( self      :				List[str]				):
					__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        UpperCamelCase_						( self      :				Optional[Any]				):
					with self.assertRaises(A				):
									pipeline(task="translation"				)


from datetime import date

import requests

SCREAMING_SNAKE_CASE							:str											=       'https://api.carbonintensity.org.uk/intensity'


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				requests.get(a_		).json()["data"][0]
				return last_half_hour["intensity"]["actual"]


def 		UpperCAmelCase    (			a_	,     a_		)	->			list:







				"""simple docstring"""



				return requests.get(F'''{BASE_URL}/{start}/{end}'''		).json()["data"]


if __name__ == "__main__":
					for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
										print('from {from} to {to}: {intensity[actual]}'.format(**entry))
					print(f'''{fetch_last_half_hour() = }''')
