


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  Tuple				      =     {
                """model_details""": {
                    """Organization""": """testing""",
                    """Model date""": """today""",
                    """Model version""": """v2.1, Developed by Test Corp in 2019.""",
                    """Architecture""": """Convolutional Neural Network.""",
                },
                """metrics""": """BLEU and ROUGE-1""",
                """evaluation_data""": {
                    """Datasets""": {"""BLEU""": """My-great-dataset-v1""", """ROUGE-1""": """My-short-dataset-v2.1"""},
                    """Preprocessing""": """See details on https://arxiv.org/pdf/1810.03993.pdf""",
                },
                """training_data""": {
                    """Dataset""": """English Wikipedia dump dated 2018-12-01""",
                    """Preprocessing""": (
                        """Using SentencePiece vocabulary of size 52k tokens. See details on"""
                        """ https://arxiv.org/pdf/1810.03993.pdf"""
                    ),
                },
                """quantitative_analyses""": {"""BLEU""": 5_5.1, """ROUGE-1""": 7_6},
            }

       def     UpperCAmelCase( self   :  int     ):
            a_     :  Tuple				      =     ModelCard.from_dict(self.inputs_dict     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""model_details"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""intended_use"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""factors"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""metrics"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""evaluation_data"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""training_data"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""quantitative_analyses"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""ethical_considerations"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""caveats_and_recommendations"""     )     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  Optional[int]				      =     ModelCard.from_dict(self.inputs_dict     )
            a_     :  Optional[int]				      =     json.loads(modelcard.to_json_string()     )
            for key, value in self.inputs_dict.items():
                 self.assertEqual(obj[key]   ,				lowerCamelCase_     )

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[str]				      =     ModelCard.from_dict(self.inputs_dict     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_     :  List[Any]				      =     os.path.join(lowerCamelCase_   ,				"""modelcard.json"""     )
                 model_card_first.to_json_file(lowerCamelCase_     )
                 a_     :  Union[str, Any]				      =     ModelCard.from_json_file(lowerCamelCase_     )

            self.assertEqual(model_card_second.to_dict()   ,				model_card_first.to_dict()     )

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  List[Any]				      =     ModelCard.from_dict(self.inputs_dict     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model_card_first.save_pretrained(lowerCamelCase_     )
                 a_     :  Any				      =     ModelCard.from_pretrained(lowerCamelCase_     )

            self.assertEqual(model_card_second.to_dict()   ,				model_card_first.to_dict()     )







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      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  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__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

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



