

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class      UpperCamelCase (     yaml.SafeLoader   ):

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE								=			[tuple(snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else key for key in keys]
     SCREAMING_SNAKE_CASE								=			Counter(snake_case__    )
     SCREAMING_SNAKE_CASE								=			[key for key in counter if counter[key] > 1]
     if duplicate_keys:
      raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""    )




    def UpperCamelCase			(			self :      str							,    snake_case__ :      str							,    snake_case__ :      Any=False    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super().construct_mapping(snake_case__							,    deep=snake_case__    )
     self._check_no_duplicates_on_constructed_node(snake_case__    )
     return mapping


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							Tuple[Optional[str], str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			list(readme_content.splitlines()	)
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  SCREAMING_SNAKE_CASE								=			full_content[1:].index('---'	) + 1
  SCREAMING_SNAKE_CASE								=			'\n'.join(full_content[1:sep_idx]	)
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

 return None, "\n".join(_UpperCamelCase	)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    # class attributes
    __UpperCamelCase         ={"train_eval_index"}  # train-eval-index in the YAML metadata

    @classmethod
    def UpperCamelCase			(			cls :      Union[str, Any]							,    snake_case__ :      Path    ):

     """simple docstring"""






     with open(snake_case__							,    encoding='utf-8'    ) as readme_file:
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			_split_yaml_from_readme(readme_file.read()    )
     if yaml_string is not None:
      return cls.from_yaml_string(snake_case__    )
     else:
      return cls()

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Path    ):

     """simple docstring"""






     if path.exists():
      with open(snake_case__							,    encoding='utf-8'    ) as readme_file:
       SCREAMING_SNAKE_CASE								=			readme_file.read()
     else:
      SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			self._to_readme(snake_case__    )
     with open(snake_case__							,    'w'							,    encoding='utf-8'    ) as readme_file:
      readme_file.write(snake_case__    )

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     if readme_content is not None:
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			_split_yaml_from_readme(snake_case__    )
      SCREAMING_SNAKE_CASE								=			'---\n' + self.to_yaml_string() + '---\n' + content
     else:
      SCREAMING_SNAKE_CASE								=			'---\n' + self.to_yaml_string() + '---\n'
     return full_content

    @classmethod
    def UpperCamelCase			(			cls :      Dict							,    snake_case__ :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			yaml.load(snake_case__							,    Loader=_NoDuplicateSafeLoader    ) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE								=			{
         (key.replace('-'							,    '_'    ) if key.replace('-'							,    '_'    ) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**snake_case__    )




    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return yaml.safe_dump(
         {
             (key.replace('_'							,    '-'    ) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }							,    sort_keys=snake_case__							,    allow_unicode=snake_case__							,    encoding='utf-8'							,    ).decode('utf-8'    )


a_    :						int    		=      {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      a_    :						int    		=      ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      a_    :						Optional[int]    		=      ap.parse_args()

      a_    :						List[str]    		=      Path(args.readme_filepath)
      a_    :						str    		=      DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)




import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel




class      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      str=1_3							,    snake_case__ :      Tuple=7							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      List[str]=False							,    snake_case__ :      Any=True							,    snake_case__ :      Union[str, Any]=9_9							,    snake_case__ :      Dict=3_2							,    snake_case__ :      Optional[Any]=5							,    snake_case__ :      Optional[Any]=4							,    snake_case__ :      Union[str, Any]=3_7							,    snake_case__ :      Tuple="gelu"							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Any=0.1							,    snake_case__ :      int=5_1_2							,    snake_case__ :      Dict=1_6							,    snake_case__ :      str=2							,    snake_case__ :      Any=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      int=4							,    snake_case__ :      List[str]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     if self.use_labels:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.num_choices    )

     SCREAMING_SNAKE_CASE								=			self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     return OpenLlamaConfig(
         vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    use_stable_embedding=snake_case__							,    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      str							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      str							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[int]							,    snake_case__ :      Tuple							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			True
     SCREAMING_SNAKE_CASE								=			OpenLlamaForCausalLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    use_cache=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor((self.batch_size, 3)							,    vocab_size=2    )

     # append to next input_ids and
     SCREAMING_SNAKE_CASE								=			torch.cat([input_ids, next_tokens]							,    dim=-1    )
     SCREAMING_SNAKE_CASE								=			torch.cat([input_mask, next_mask]							,    dim=-1    )

     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    encoder_hidden_states=snake_case__							,    encoder_attention_mask=snake_case__							,    past_key_values=snake_case__							,    output_hidden_states=snake_case__							,    )['hidden_states'][0]

     # select random slice
     SCREAMING_SNAKE_CASE								=			ids_tensor((1,)							,    output_from_past.shape[-1]    ).item()
     SCREAMING_SNAKE_CASE								=			output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE								=			output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-3    )    )




    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    __UpperCamelCase         =(OpenLlamaForCausalLM,) if is_torch_available() else ()
    __UpperCamelCase         =(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			OpenLlamaModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'single_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([self.model_tester.batch_size]							,    self.model_tester.type_sequence_label_size    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			3
     SCREAMING_SNAKE_CASE								=			'multi_label_classification'
     SCREAMING_SNAKE_CASE								=			input_dict['input_ids']
     SCREAMING_SNAKE_CASE								=			input_ids.ne(1    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			ids_tensor(
         [self.model_tester.batch_size, config.num_labels]							,    self.model_tester.type_sequence_label_size    ).to(torch.float    )
     SCREAMING_SNAKE_CASE								=			OpenLlamaForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    labels=snake_case__    )
     self.assertEqual(result.logits.shape							,    (self.model_tester.batch_size, self.model_tester.num_labels)    )

    @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'    )
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     pass




    @parameterized.expand([('linear',), ('dynamic',)]    )
    def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, 1_0]							,    config.vocab_size    )
     SCREAMING_SNAKE_CASE								=			ids_tensor([1, int(config.max_position_embeddings * 1.5    )]							,    config.vocab_size    )

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     original_model.to(snake_case__    )
     original_model.eval()
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			original_model(snake_case__    ).last_hidden_state

     set_seed(4_2    )  # Fixed seed at init time so the two models get the same random weights
     SCREAMING_SNAKE_CASE								=			{'type': scaling_type, 'factor': 10.0}
     SCREAMING_SNAKE_CASE								=			OpenLlamaModel(snake_case__    )
     scaled_model.to(snake_case__    )
     scaled_model.eval()
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state
     SCREAMING_SNAKE_CASE								=			scaled_model(snake_case__    ).last_hidden_state

     # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
     # maximum sequence length, so the outputs for the short input should match.
     if scaling_type == "dynamic":
      self.assertTrue(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )
     else:
      self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

     # The output should be different for long inputs
     self.assertFalse(torch.allclose(snake_case__							,    snake_case__							,    atol=1E-5    )    )

