
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		Optional[Any]						     =     LongformerTokenizer
   A__					:		str						     =     True
   A__					:		Any						     =     LongformerTokenizerFast
   A__					:		List[str]						     =     True



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	str:


          '''simple docstring'''


          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case       :   Union[str, Any]							=      [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          snake_case       :   List[str]							=      dict(zip(snake_case__    ,						range(len(snake_case__ ) ) ) )
          snake_case       :   List[Any]							=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          snake_case       :   Any							=      {"unk_token": "<unk>"}

          snake_case       :   Optional[int]							=      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"] )
          snake_case       :   Any							=      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 fp.write(json.dumps(snake_case__ ) + "\n" )
          with open(self.merges_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 fp.write("\n".join(snake_case__ ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						**snake_case__		:					Optional[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						**snake_case__		:					Union[str, Any] )		->	str:


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      "lower newer"
          snake_case       :   str							=      "lower newer"
          return input_text, output_text



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.tokenizer_class(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map )
          snake_case       :   Tuple							=      "lower newer"
          snake_case       :   List[str]							=      ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          snake_case       :   Optional[Any]							=      tokenizer.tokenize(snake_case__ )  # , add_prefix_space=True)
          self.assertListEqual(snake_case__    ,						snake_case__ )

          snake_case       :   int							=      tokens + [tokenizer.unk_token]
          snake_case       :   int							=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      self.get_tokenizer()

          self.assertListEqual(tokenizer.encode("Hello world!"    ,						add_special_tokens=snake_case__ )    ,						[0, 3_14_14, 2_32, 3_28, 2] )
          self.assertListEqual(
              tokenizer.encode("Hello world! cécé herlolip 418"    ,						add_special_tokens=snake_case__ )    ,						[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]    ,						)



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )

          snake_case       :   Any							=      tokenizer.encode("sequence builders"    ,						add_special_tokens=snake_case__ )
          snake_case       :   Optional[int]							=      tokenizer.encode("multi-sequence build"    ,						add_special_tokens=snake_case__ )

          snake_case       :   int							=      tokenizer.encode(
              "sequence builders"    ,						add_special_tokens=snake_case__    ,						add_prefix_space=snake_case__ )
          snake_case       :   int							=      tokenizer.encode(
              "sequence builders"    ,						"multi-sequence build"    ,						add_special_tokens=snake_case__    ,						add_prefix_space=snake_case__ )

          snake_case       :   int							=      tokenizer.build_inputs_with_special_tokens(snake_case__ )
          snake_case       :   int							=      tokenizer.build_inputs_with_special_tokens(snake_case__    ,						snake_case__ )

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_tokenizer()

          snake_case       :   str							=      "Encode this sequence."
          snake_case       :   Optional[Any]							=      tokenizer.byte_encoder[" ".encode("utf-8" )[0]]

          # Testing encoder arguments
          snake_case       :   Optional[Any]							=      tokenizer.encode(snake_case__    ,						add_special_tokens=snake_case__    ,						add_prefix_space=snake_case__ )
          snake_case       :   int							=      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
          self.assertNotEqual(snake_case__    ,						snake_case__ )

          snake_case       :   int							=      tokenizer.encode(snake_case__    ,						add_special_tokens=snake_case__    ,						add_prefix_space=snake_case__ )
          snake_case       :   List[Any]							=      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
          self.assertEqual(snake_case__    ,						snake_case__ )

          tokenizer.add_special_tokens({"bos_token": "<s>"} )
          snake_case       :   int							=      tokenizer.encode(snake_case__    ,						add_special_tokens=snake_case__ )
          snake_case       :   Dict							=      tokenizer.convert_ids_to_tokens(encoded[1] )[0]
          self.assertNotEqual(snake_case__    ,						snake_case__ )

          # Testing spaces after special tokens
          snake_case       :   Union[str, Any]							=      "<mask>"
          tokenizer.add_special_tokens(
              {"mask_token": AddedToken(snake_case__    ,						lstrip=snake_case__    ,						rstrip=snake_case__ )} )  # mask token has a left space
          snake_case       :   List[str]							=      tokenizer.convert_tokens_to_ids(snake_case__ )

          snake_case       :   Tuple							=      "Encode <mask> sequence"
          snake_case       :   Optional[Any]							=      "Encode <mask>sequence"

          snake_case       :   Dict							=      tokenizer.encode(snake_case__ )
          snake_case       :   Optional[Any]							=      encoded.index(snake_case__ )
          snake_case       :   int							=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
          self.assertEqual(snake_case__    ,						snake_case__ )

          snake_case       :   Dict							=      tokenizer.encode(snake_case__ )
          snake_case       :   Union[str, Any]							=      encoded.index(snake_case__ )
          snake_case       :   Union[str, Any]							=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
          self.assertNotEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[Any]:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                        snake_case       :   Optional[int]							=      self.rust_tokenizer_class.from_pretrained(snake_case__    ,						**snake_case__ )
                        snake_case       :   List[Any]							=      self.tokenizer_class.from_pretrained(snake_case__    ,						**snake_case__ )
                        snake_case       :   Union[str, Any]							=      "A, <mask> AllenNLP sentence."
                        snake_case       :   Tuple							=      tokenizer_r.encode_plus(snake_case__    ,						add_special_tokens=snake_case__    ,						return_token_type_ids=snake_case__ )
                        snake_case       :   Optional[int]							=      tokenizer_p.encode_plus(snake_case__    ,						add_special_tokens=snake_case__    ,						return_token_type_ids=snake_case__ )

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["token_type_ids"] )    ,						sum(tokens_p["token_type_ids"] ) )

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] )    ,						sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] )    ,						)

                        snake_case       :   List[Any]							=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
                        snake_case       :   int							=      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["input_ids"]    ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
                        self.assertSequenceEqual(tokens_r["input_ids"]    ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

                        self.assertSequenceEqual(
                            snake_case__    ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
                        self.assertSequenceEqual(
                            snake_case__    ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	str:


          '''simple docstring'''


          for trim_offsets, add_prefix_space in itertools.product([True, False]    ,						repeat=2 ):
                 snake_case       :   Any							=      self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )

                 snake_case       :   Dict							=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
                 snake_case       :   str							=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

                 self.assertEqual(pre_tokenizer_state["add_prefix_space"]    ,						snake_case__ )

                 self.assertEqual(post_processor_state["add_prefix_space"]    ,						snake_case__ )
                 self.assertEqual(post_processor_state["trim_offsets"]    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                        snake_case       :   Union[str, Any]							=      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                        snake_case       :   List[Any]							=      f"""{text_of_1_token} {text_of_1_token}"""

                        snake_case       :   int							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   str							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   Any							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   int							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(len(snake_case__ ) + 1, len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   int							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   Union[str, Any]							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   Union[str, Any]							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   List[Any]							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(len(snake_case__ ), len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   Any							=      f""" {text}"""

                        # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                        # )
                        # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                        # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                        # self.assertEqual(
                        #     encoding.offset_mapping[1],
                        #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                        # )

                        snake_case       :   List[str]							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   Optional[int]							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(1, 1 + len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(1 + len(snake_case__ ) + 1, 1 + len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   Optional[Any]							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   List[Any]							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, 1 + len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)

                        snake_case       :   Optional[int]							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						use_fast=snake_case__    ,						add_prefix_space=snake_case__    ,						trim_offsets=snake_case__ )
                        snake_case       :   Optional[Any]							=      tokenizer_r(snake_case__    ,						return_offsets_mapping=snake_case__    ,						add_special_tokens=snake_case__ )
                        self.assertEqual(encoding.offset_mapping[0]    ,						(0, 1 + len(snake_case__ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]    ,						(1 + len(snake_case__ ), 1 + len(snake_case__ ) + 1 + len(snake_case__ ))    ,						)







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	List[str]:


          '''simple docstring'''


          return f"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case__ ) for s in shape] )}.npy"""



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          super().tearDown()
          gc.collect()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					Any=(4, 4, 64, 64)    ,						snake_case__		:					List[Any]=False )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Optional[int]							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[Any]="CompVis/stable-diffusion-v1-4" )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   str							=      "bf16" if fpaa else None

          snake_case				,					snake_case       :   Optional[int]							=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case__    ,						subfolder="unet"    ,						dtype=snake_case__    ,						revision=snake_case__ )
          return model, params



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Union[str, Any]=(4, 77, 7_68)    ,						snake_case__		:					Dict=False )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Any							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return hidden_states



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_latents(snake_case__    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_encoder_hidden_states(snake_case__    ,						fpaa=snake_case__ )

          snake_case       :   Dict							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   Optional[Any]							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Optional[int]							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_latents(snake_case__    ,						shape=(4, 4, 96, 96)    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_encoder_hidden_states(snake_case__    ,						shape=(4, 77, 10_24)    ,						fpaa=snake_case__ )

          snake_case       :   Optional[int]							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   int							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Dict							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )





