

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A=None						,		_A=True						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						config_class
      SCREAMING_SNAKE_CASE_									=						has_text_modality
      SCREAMING_SNAKE_CASE_									=						kwargs
      SCREAMING_SNAKE_CASE_									=						common_properties

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)
      SCREAMING_SNAKE_CASE_									=						(
          ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
          common_properties.extend(['vocab_size'])

      # Test that config has the common properties as getters
      for prop in common_properties:
          self.parent.assertTrue(hasattr(_A						,		_A)						,		msg=f"""`{prop}` does not exist""")

      # Test that config has the common properties as setter
      for idx, name in enumerate(_A):
          try:
              setattr(_A						,		_A						,		_A)
              self.parent.assertEqual(
                  getattr(_A						,		_A)						,		_A						,		msg=f"""`{name} value {idx} expected, but was {getattr(_A						,		_A)}""")
          except NotImplementedError:
              # Some models might not be able to implement setters for common_properties
              # In that case, a NotImplementedError is raised
              pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(_A):
          try:
              SCREAMING_SNAKE_CASE_									=						self.config_class(**{name: idx})
              self.parent.assertEqual(
                  getattr(_A						,		_A)						,		_A						,		msg=f"""`{name} value {idx} expected, but was {getattr(_A						,		_A)}""")
          except NotImplementedError:
              # Some models might not be able to implement setters for common_properties
              # In that case, a NotImplementedError is raised
              pass

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)
      SCREAMING_SNAKE_CASE_									=						json.loads(config.to_json_string())
      for key, value in self.inputs_dict.items():
          self.parent.assertEqual(obj[key]						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
          SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		'config.json')
          config_first.to_json_file(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_json_file(_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      with tempfile.TemporaryDirectory() as tmpdirname:
          config_first.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_pretrained(_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict)

      SCREAMING_SNAKE_CASE_									=						'test'
      with tempfile.TemporaryDirectory() as tmpdirname:
          SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		_A)
          config_first.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						self.config_class.from_pretrained(_A						,		subfolder=_A)

      self.parent.assertEqual(config_second.to_dict()						,		config_first.to_dict())

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.config_class(**self.inputs_dict						,		num_labels=5)
      self.parent.assertEqual(len(config.idalabel)						,		5)
      self.parent.assertEqual(len(config.labelaid)						,		5)

      SCREAMING_SNAKE_CASE_									=						3
      self.parent.assertEqual(len(config.idalabel)						,		3)
      self.parent.assertEqual(len(config.labelaid)						,		3)

  def 						lowerCAmelCase__     (					self):
      if self.config_class.is_composition:
          return
      SCREAMING_SNAKE_CASE_									=						self.config_class()
      self.parent.assertIsNotNone(_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						copy.deepcopy(_A)
      SCREAMING_SNAKE_CASE_									=						self.config_class(**_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for key, value in config_common_kwargs.items():
          if key == "torch_dtype":
              if not is_torch_available():
                  continue
              else:
                  import torch

                  if config.torch_dtype != torch.floataa:
                      wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa))
          elif getattr(_A						,		_A) != value:
              wrong_values.append((key, getattr(_A						,		_A), value))

      if len(_A) > 0:
          SCREAMING_SNAKE_CASE_									=						'\n'.join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values])
          raise ValueError(f"""The following keys were not properly set in the config:\n{errors}""")


  def 						lowerCAmelCase__     (					self):
      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()




from typing import List

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


