

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'''







			lowerCamelCase__          =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase__          =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Union[str, Any] , a							:	int , a							:	Optional[int]		)							->    Dict:

				"""simple docstring"""

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

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE				: List[str]			       =    translator(["Some string", "other string"]		)
				self.assertEqual(a , [{"translation_text": ANY(a		)}, {"translation_text": ANY(a		)}]		)
			@require_torch
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    pipeline("translation_en_to_ro" , model="patrickvonplaten/t5-tiny-random" , framework="pt"		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    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							:	List[Any]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    pipeline("translation_en_to_ro" , model="patrickvonplaten/t5-tiny-random" , framework="tf"		)
				SCREAMING_SNAKE_CASE				: Dict			       =    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							:	int		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    pipeline("translation_en_to_de" , model="patrickvonplaten/t5-tiny-random" , framework="pt"		)
				SCREAMING_SNAKE_CASE				: Dict			       =    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							:	Union[str, Any]		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    pipeline("translation_en_to_de" , model="patrickvonplaten/t5-tiny-random" , framework="tf"		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    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							:	str		)							->    List[Any]:

				"""simple docstring"""

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

				# but we do for this one
				SCREAMING_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 							__UpperCamelCase	(     self							:	int		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"		)

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

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

				SCREAMING_SNAKE_CASE				: str			       =    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
				SCREAMING_SNAKE_CASE				: str			       =    pipeline(task="translation" , model=a , tokenizer=a , src_lang="en_XX" , tgt_lang="ar_AR"		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    translator("This is a test"		)
				self.assertEqual(a , [{"translation_text": "هذا إختبار"}]		)
			@require_torch
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    "patrickvonplaten/t5-tiny-random"
				SCREAMING_SNAKE_CASE				: List[str]			       =    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							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    "patrickvonplaten/t5-tiny-random"
				with pytest.warns(a , match=R".*translation_en_to_de.*"		):
					SCREAMING_SNAKE_CASE				: str			       =    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]		)							->    Any:

				"""simple docstring"""

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


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(_a))

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "image_data/PSNR-example-comp-10.jpg")							,     1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")


if __name__ == "__main__":
			main()