
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  ):
       lowerCamelCase		:	Any							=      parent
       lowerCamelCase		:	Any							=      1_3
       lowerCamelCase		:	Dict							=      7
       lowerCamelCase		:	List[str]							=      3_0
       lowerCamelCase		:	str							=      self.seq_length + self.mem_len
       lowerCamelCase		:	int							=      1_5
       lowerCamelCase		:	List[Any]							=      True
       lowerCamelCase		:	List[str]							=      True
       lowerCamelCase		:	List[str]							=      9_9
       lowerCamelCase		:	Optional[int]							=      [1_0, 5_0, 8_0]
       lowerCamelCase		:	Dict							=      3_2
       lowerCamelCase		:	List[Any]							=      3_2
       lowerCamelCase		:	Optional[int]							=      4
       lowerCamelCase		:	Optional[Any]							=      8
       lowerCamelCase		:	Union[str, Any]							=      1_2_8
       lowerCamelCase		:	Any							=      2
       lowerCamelCase		:	List[str]							=      2
       lowerCamelCase		:	Optional[int]							=      None
       lowerCamelCase		:	Optional[int]							=      1
       lowerCamelCase		:	int							=      0
       lowerCamelCase		:	Tuple							=      3
       lowerCamelCase		:	List[str]							=      self.vocab_size - 1
       lowerCamelCase		:	Tuple							=      0.01
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )
       lowerCamelCase		:	Union[str, Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

       lowerCamelCase		:	Union[str, Any]							=      None
       if self.use_labels:
        lowerCamelCase		:	Dict							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

       lowerCamelCase		:	Tuple							=      TransfoXLConfig(
           vocab_size=self.vocab_size    ,  mem_len=self.mem_len    ,  clamp_len=self.clamp_len    ,  cutoffs=self.cutoffs    ,  d_model=self.hidden_size    ,  d_embed=self.d_embed    ,  n_head=self.num_attention_heads    ,  d_head=self.d_head    ,  d_inner=self.d_inner    ,  div_val=self.div_val    ,  n_layer=self.num_hidden_layers    ,  eos_token_id=self.eos_token_id    ,  pad_token_id=self.vocab_size - 1    ,  init_range=self.init_range    ,  num_labels=self.num_labels    ,  )

       return (config, input_ids_a, input_ids_a, lm_labels)
      def 						UpperCamelCase__							(      self      ):
       random.seed(self.seed      )
       tf.random.set_seed(self.seed      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[Any]							=      TFTransfoXLModel(__a      )

       lowerCamelCase		:	Optional[Any]							=      model(__a      ).to_tuple()

       lowerCamelCase		:	Union[str, Any]							=      {'input_ids': input_ids_a, 'mems': mems_a}

       lowerCamelCase		:	Optional[Any]							=      model(__a      ).to_tuple()

       self.parent.assertEqual(hidden_states_a.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertEqual(hidden_states_a.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Tuple							=      TFTransfoXLLMHeadModel(__a      )

       lowerCamelCase		:	Optional[int]							=      model(__a      ).to_tuple()

       lowerCamelCase		:	Any							=      {'input_ids': input_ids_a, 'labels': lm_labels}
       lowerCamelCase		:	Any							=      model(__a      ).to_tuple()

       lowerCamelCase		:	List[Any]							=      model([input_ids_a, mems_a]      ).to_tuple()

       lowerCamelCase		:	Dict							=      {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

       lowerCamelCase		:	Tuple							=      model(__a      ).to_tuple()

       self.parent.assertEqual(lm_logits_a.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )

       self.parent.assertEqual(lm_logits_a.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)      )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      TFTransfoXLForSequenceClassification(__a      )
       lowerCamelCase		:	str							=      model(__a      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.prepare_config_and_inputs()
       (lowerCamelCase)		:	str							=      config_and_inputs
       lowerCamelCase		:	Optional[int]							=      {'input_ids': input_ids_a}
       return config, inputs_dict





@require_tf
class 							A__		(	lowerCAmelCase_  ,   lowerCAmelCase_  ,   unittest.TestCase):
      _UpperCAmelCase				:    Optional[int]           =			(
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      _UpperCAmelCase				:    int           =			() if is_tf_available() else ()
      _UpperCAmelCase				:    Tuple           =			(
          {
              """feature-extraction""": TFTransfoXLModel,
              """text-classification""": TFTransfoXLForSequenceClassification,
              """text-generation""": TFTransfoXLLMHeadModel,
              """zero-shot""": TFTransfoXLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
      _UpperCAmelCase				:    Union[str, Any]           =			False
      _UpperCAmelCase				:    Optional[int]           =			False
      _UpperCAmelCase				:    Any           =			False
      _UpperCAmelCase				:    Optional[Any]           =			False
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):





       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
        # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
        # tokenizer.
        return True

       return False
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      TFTransfoXLModelTester(self      )
       lowerCamelCase		:	Optional[Any]							=      ConfigTester(self    ,  config_class=__a    ,  d_embed=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      def 						UpperCamelCase__							(      self      ):
       self.model_tester.set_seed()
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*__a      )
      def 						UpperCamelCase__							(      self      ):
       self.model_tester.set_seed()
       lowerCamelCase		:	str							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase		:	str							=      [TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
        lowerCamelCase		:	Tuple							=      model_class(__a      )
        assert isinstance(model.get_input_embeddings()    ,  tf.keras.layers.Layer      )
        if model_class in list_other_models_with_output_ebd:
         lowerCamelCase		:	Union[str, Any]							=      model.get_output_embeddings()
         assert isinstance(__a    ,  tf.keras.layers.Layer      )
         lowerCamelCase		:	List[str]							=      model.get_bias()
         assert name is None
        else:
         lowerCamelCase		:	Any							=      model.get_output_embeddings()
         assert x is None
         lowerCamelCase		:	str							=      model.get_bias()
         assert name is None
      def 						UpperCamelCase__							(      self      ):
       pass
      @slow
      def 						UpperCamelCase__							(      self      ):





       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:	Tuple							=      TFTransfoXLModel.from_pretrained(__a      )
        self.assertIsNotNone(__a      )






      @unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss."""      )
      def 						UpperCamelCase__							(      self      ):
       pass





@require_tf
class 							A__		(	unittest.TestCase):
      @unittest.skip("""Skip test until #12651 is resolved."""      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""      )
       # fmt: off
       lowerCamelCase		:	List[Any]							=      tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]    ,  dtype=tf.intaa      )  # noqa: E231
       # fmt: on
       #  In 1991 , the remains of Russian Tsar Nicholas II and his family
       #  ( except for Alexei and Maria ) are discovered .
       #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
       #  remainder of the story . 1883 Western Siberia ,
       #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
       #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
       #  father initially slaps him for making such an accusation , Rasputin watches as the
       #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
       #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
       #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

       # fmt: off
       lowerCamelCase		:	List[Any]							=      [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
       # fmt: on
       #  In 1991, the remains of Russian Tsar Nicholas II and his family (
       #  except for Alexei and Maria ) are discovered. The voice of young son,
       #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
       #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
       #  and a group of men to perform magic. Rasputin has a vision and
       #  denounces one of the men as a horse thief. Although his father initially
       #  slaps him for making such an accusation, Rasputin watches as the man
       #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
       #  of the Virgin Mary, prompting him to become a priest.
       #  Rasputin quickly becomes famous, with people, even a bishop, begging for
       #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
       # Nicholas II and his family were discovered. The voice of <unk> young son,
       # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

       lowerCamelCase		:	Optional[Any]							=      model.generate(__a    ,  max_length=2_0_0    ,  do_sample=__a      )
       self.assertListEqual(output_ids[0].numpy().tolist()    ,  __a      )








'''simple docstring'''



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



