





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__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							copy.deepcopy(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							sorted(__lowerCamelCase		)
     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[bisect.bisect_right(__lowerCamelCase,				__lowerCamelCase		) for y in x]
     return quantized






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





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   str:
            if not isinstance(__lowerCamelCase			,  __lowerCamelCase							):
                 raise ValueError(
                     "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							sampler
            _SCREAMING_SNAKE_CASE				:		str  			=							group_ids
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							batch_size




       def __iter__(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							defaultdict(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							defaultdict(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							0
            for idx in self.sampler:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							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
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							len(self							)
            _SCREAMING_SNAKE_CASE				:		str  			=							expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[]
                 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]
                           _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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							)	->   List[str]:
            return (len(self.sampler							) + self.batch_size - 1) // self.batch_size






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)