
'''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 			A							(       unittest.TestCase				):
			lowerCamelCase							:							Tuple       		=  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase							:							Optional[int]       		=  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       translator("""Some string"""       )
									self.assertEqual(lowerCamelCase__       ,     [{"""translation_text""": ANY(lowerCamelCase__       )}]       )

									lowercase__							 =       translator(["""Some string"""]       )
									self.assertEqual(lowerCamelCase__       ,     [{"""translation_text""": ANY(lowerCamelCase__       )}]       )

									lowercase__							 =       translator(["""Some string""", """other string"""]       )
									self.assertEqual(lowerCamelCase__       ,     [{"""translation_text""": ANY(lowerCamelCase__       )}, {"""translation_text""": ANY(lowerCamelCase__       )}]       )



			@require_torch
			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__							 =       pipeline("""translation_en_to_ro"""       ,     model="""patrickvonplaten/t5-tiny-random"""       ,     framework="""pt"""       )
									lowercase__							 =       translator("""This is a test string"""       ,     max_length=20       )
									self.assertEqual(
									    lowerCamelCase__       ,     [
									        {
									            """translation_text""": (
									                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
									                """ Beide Beide"""
									            )
									        }
									    ]       ,     )



			@require_tf
			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       pipeline("""translation_en_to_ro"""       ,     model="""patrickvonplaten/t5-tiny-random"""       ,     framework="""tf"""       )
									lowercase__							 =       translator("""This is a test string"""       ,     max_length=20       )
									self.assertEqual(
									    lowerCamelCase__       ,     [
									        {
									            """translation_text""": (
									                """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
									                """ Beide Beide"""
									            )
									        }
									    ]       ,     )



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




									'''simple docstring'''



									lowercase__							 =       pipeline("""translation_en_to_de"""       ,     model="""patrickvonplaten/t5-tiny-random"""       ,     framework="""pt"""       )
									lowercase__							 =       translator("""This is a test string"""       ,     max_length=20       )
									self.assertEqual(
									    lowerCamelCase__       ,     [
									        {
									            """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'''



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





class 			A							(       unittest.TestCase				):



			@require_torch
			@slow
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



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

									# but we do for this one
									lowercase__							 =       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'''



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

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

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

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

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



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




									'''simple docstring'''



									lowercase__							 =       """patrickvonplaten/t5-tiny-random"""
									lowercase__							 =       pipeline(task="""translation_cn_to_ar"""       ,     model=lowerCamelCase__       )
									self.assertEqual(translator._preprocess_params["""src_lang"""]       ,     """cn"""       )
									self.assertEqual(translator._preprocess_params["""tgt_lang"""]       ,     """ar"""       )



			@require_torch
			def A__ (					self       )			->		Tuple:




									'''simple docstring'''



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




									'''simple docstring'''



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








'''simple docstring'''



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





def  _A		(							lowercase__		,		lowercase__=0	):
						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
						# count number of elements per group
						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
						lowercase__							 =       [0] + bins + [np.inf]
						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
						return groups







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       self.group_ids[idx]
															buffer_per_group[group_id].append(lowerCamelCase__       )
															samples_per_group[group_id].append(lowerCamelCase__       )
															if len(buffer_per_group[group_id]       ) == self.batch_size:
																					yield buffer_per_group[group_id]  # TODO
																					num_batches += 1
																					del buffer_per_group[group_id]
															assert len(buffer_per_group[group_id]       ) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
																					batch_idx.extend(lowerCamelCase__       )
																					if len(lowerCamelCase__       ) >= self.batch_size:
																											yield batch_idx[: self.batch_size]
																											lowercase__							 =       batch_idx[self.batch_size :]
																											num_remaining -= 1
															if len(lowerCamelCase__       ) > 0:
																					yield batch_idx
																					num_remaining -= 1
									assert num_remaining == 0



			def __len__(					self       )			->		Any:




									'''simple docstring'''



									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size






