




import bisect
import copy
from collections import defaultdict

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

from utils import logger







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							copy.deepcopy(UpperCAmelCase       )
					__magic_name__					:							int		   =							sorted(UpperCAmelCase       )
					__magic_name__					:							Tuple		   =							[bisect.bisect_right(UpperCAmelCase,	UpperCAmelCase       ) for y in x]
					return quantized







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase=0       )  ->int:






					"""simple docstring"""






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





class 		A__				(   __SCREAMING_SNAKE_CASE ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Union[str, Any]:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     lowerCamelCase						):
											raise ValueError(
											    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowerCamelCase						)						)
						__magic_name__					:							int		   =							sampler
						__magic_name__					:							Optional[Any]		   =							group_ids
						__magic_name__					:							Tuple		   =							batch_size






	def __iter__(	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							defaultdict(lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							defaultdict(lowerCamelCase						)

						__magic_name__					:							Optional[Any]		   =							0
						for idx in self.sampler:
											__magic_name__					:							List[Any]		   =							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
						__magic_name__					:							Optional[int]		   =							len(self						)
						__magic_name__					:							Union[str, Any]		   =							expected_num_batches - num_batches
						if num_remaining > 0:
											# for the remaining batches, group the batches by similar lengths
											__magic_name__					:							int		   =							[]
											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]
																					__magic_name__					:							Tuple		   =							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						)      ->   Optional[Any]:




						"""simple docstring"""






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






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  __lowerCamelCase				(	unittest.TestCase	):
      lowerCamelCase_	:      Tuple          =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      lowerCamelCase_	:      int          =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        if isinstance(model.config							,  lowerCamelCase   ):
          snake_case_						,				snake_case_       						=      list(tokenizer.lang_code_to_id.keys()   )[:2]
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang=lowerCamelCase							,  tgt_lang=lowerCamelCase   )
        else:
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        return translator, ["Some string", "Some other text"]







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      translator("""Some string"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

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

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







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      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  __lowerCamelCase				(	unittest.TestCase	):







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # We don't provide a default for this pair
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation_cn_to_ar"""   )

        # but we do for this one
        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 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )
        snake_case_       						=      MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )

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

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

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







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        with pytest.warns(lowerCamelCase							,  match=r""".*translation_en_to_de.*"""   ):
          snake_case_       						=      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 	lowerCAmelCase_    (     self   )						->       Any:
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation"""   )