
'''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      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		lowerCamelCase							=		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Dict	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Dict			       =		translator('''Some string'''	)
				self.assertEqual(UpperCamelCase__   ,     [{'''translation_text''': ANY(UpperCamelCase__	)}]	)

				snake_case   :    Dict			       =		translator(['''Some string''']	)
				self.assertEqual(UpperCamelCase__   ,     [{'''translation_text''': ANY(UpperCamelCase__	)}]	)

				snake_case   :    str			       =		translator(['''Some string''', '''other string''']	)
				self.assertEqual(UpperCamelCase__   ,     [{'''translation_text''': ANY(UpperCamelCase__	)}, {'''translation_text''': ANY(UpperCamelCase__	)}]	)



		@require_torch
		def 	lowerCAmelCase							(	self							:       Dict	)		->       List[str]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		pipeline('''translation_en_to_ro'''   ,     model='''patrickvonplaten/t5-tiny-random'''   ,     framework='''pt'''	)
				snake_case   :    Dict			       =		translator('''This is a test string'''   ,     max_length=20	)
				self.assertEqual(
				    UpperCamelCase__   ,     [
				        {
				            '''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[Any]	)		->       int:






				"""simple docstring"""







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



		@require_torch
		def 	lowerCAmelCase							(	self							:       Any	)		->       Optional[int]:






				"""simple docstring"""







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






				"""simple docstring"""







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







class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		@require_torch
		@slow
		def 	lowerCAmelCase							(	self							:       List[str]	)		->       int:






				"""simple docstring"""







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

				# but we do for this one
				snake_case   :    int			       =		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							:       Any	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    str			       =		MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''	)
				snake_case   :    List[str]			       =		MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''	)

				snake_case   :    List[str]			       =		pipeline(task='''translation'''   ,     model=UpperCamelCase__   ,     tokenizer=UpperCamelCase__	)
				# Missing src_lang, tgt_lang
				with self.assertRaises(UpperCamelCase__	):
						translator('''This is a test'''	)

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

				snake_case   :    List[str]			       =		translator('''This is a test'''   ,     src_lang='''en_XX'''   ,     tgt_lang='''hi_IN'''	)
				self.assertEqual(UpperCamelCase__   ,     [{'''translation_text''': '''यह एक परीक्षण है'''}]	)

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



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






				"""simple docstring"""







				snake_case   :    List[str]			       =		'''patrickvonplaten/t5-tiny-random'''
				snake_case   :    List[Any]			       =		pipeline(task='''translation_cn_to_ar'''   ,     model=UpperCamelCase__	)
				self.assertEqual(translator._preprocess_params['''src_lang''']   ,     '''cn'''	)
				self.assertEqual(translator._preprocess_params['''tgt_lang''']   ,     '''ar'''	)



		@require_torch
		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		'''patrickvonplaten/t5-tiny-random'''
				with pytest.warns(UpperCamelCase__   ,     match=R'''.*translation_en_to_de.*'''	):
						snake_case   :    Union[str, Any]			       =		pipeline(task='''translation'''   ,     model=UpperCamelCase__	)
				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(UpperCamelCase__	):
						pipeline(task='''translation'''	)




'''simple docstring'''
import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
lowercase__		    =	False
lowercase__		    =	Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowercase__		    =	datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       List[str]=sys.argv[1:]	)		->       str:






				"""simple docstring"""







				snake_case   :    List[str]			       =		None
				snake_case   :    Tuple			       =		None
				snake_case   :    List[Any]			       =		None
				snake_case   :    Tuple			       =		1
				snake_case       ,snake_case   :    List[Any]			       =		getopt.getopt(UpperCamelCase__   ,     '''i:o:b:s'''   ,     ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
				for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
								snake_case   :    Any			       =		arg
						elif opt in ("-o", "--outfile"):
								snake_case   :    str			       =		arg
						elif opt in ("-b", "--batch_size"):
								snake_case   :    Optional[Any]			       =		int(UpperCamelCase__	)
						elif opt in ("-s", "--subset_list"):
								snake_case   :    List[str]			       =		arg

				assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
				assert outputfile is not None and not os.path.isfile(UpperCamelCase__	), f'{outputfile}'
				if subset_list is not None:
						with open(os.path.realpath(UpperCamelCase__	)	) as f:
								snake_case   :    Optional[Any]			       =		{self._vqa_file_split()[0] for x in tryload(UpperCamelCase__	)}
				else:
						snake_case   :    Union[str, Any]			       =		None

				snake_case   :    Any			       =		CONFIG
				if torch.cuda.is_available():
						snake_case   :    List[Any]			       =		'''cuda'''
				snake_case   :    Dict			       =		os.path.realpath(UpperCamelCase__	)
				snake_case   :    List[str]			       =		os.path.realpath(UpperCamelCase__	)
				snake_case   :    Optional[int]			       =		Preprocess(self.config	)
				snake_case   :    int			       =		GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''   ,     config=self.config	)
				snake_case   :    Optional[Any]			       =		batch_size if batch_size != 0 else 1
				snake_case   :    Tuple			       =		DEFAULT_SCHEMA



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[str]	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    str			       =		int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
				snake_case   :    Optional[Any]			       =		os.path.join(self.inputdir   ,     UpperCamelCase__	)
				return (img_id, filepath)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		[]
				for i, file in enumerate(os.listdir(self.inputdir	)	):
						if self.subset_list is not None and i not in self.subset_list:
								continue
						batch.append(self._vqa_file_split(UpperCamelCase__	)	)
						if len(UpperCamelCase__	) == self.batch:
								snake_case   :    Optional[int]			       =		batch
								snake_case   :    int			       =		[]
								yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)

				for i in range(1	):
						yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)



		def __call__(	self							:       Dict	)		->       str:






				"""simple docstring"""







				if not TEST:
						snake_case   :    Any			       =		datasets.ArrowWriter(features=self.schema   ,     path=self.outputfile	)
				# do file generator
				for i, (img_ids, filepaths) in enumerate(self.file_generator	):
						snake_case       ,snake_case       ,snake_case   :    Dict			       =		self.preprocess(UpperCamelCase__	)
						snake_case   :    List[Any]			       =		self.model(
						    UpperCamelCase__   ,     UpperCamelCase__   ,     scales_yx=UpperCamelCase__   ,     padding='''max_detections'''   ,     max_detections=self.config.MAX_DETECTIONS   ,     pad_value=0   ,     return_tensors='''np'''   ,     location='''cpu'''   ,     )
						snake_case   :    Dict			       =		output_dict.pop('''normalized_boxes'''	)
						if not TEST:
								snake_case   :    str			       =		np.array(UpperCamelCase__	)
								snake_case   :    Tuple			       =		self.schema.encode_batch(UpperCamelCase__	)
								writer.write_batch(UpperCamelCase__	)
						if TEST:
								break
						# finalizer the writer
				if not TEST:
						snake_case       ,snake_case   :    int			       =		writer.finalize()
						print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		try:
				snake_case   :    Optional[int]			       =		json.load(SCREAMING_SNAKE_CASE__       )
				try:
						snake_case   :    Union[str, Any]			       =		list(data.keys()       )
				except Exception:
						snake_case   :    Any			       =		[d['''img_id'''] for d in data]
		except Exception:
				try:
						snake_case   :    Tuple			       =		eval(stream.read()       )
				except Exception:
						snake_case   :    List[Any]			       =		stream.read().split('''\n'''       )
		return data


if __name__ == "__main__":
						lowercase__		    =	Extract(sys.argv[1:])
						extract()
						if not TEST:
												lowercase__		    =	datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)


