'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:					Optional[int]  =get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE_:					Optional[Any]  =25_60_47
SCREAMING_SNAKE_CASE_:					List[Any]  =25_61_45




@require_sentencepiece
@require_tokenizers
class 	__A				(			lowerCamelCase__		,     unittest.TestCase     ):
    a__	:						Optional[Any]	       =  NllbTokenizer
    a__	:						int	       =  NllbTokenizerFast
    a__	:						str	       =  True
    a__	:						List[str]	       =  True
    a__	:						Optional[Any]	       =  {}



    def  _lowercase						(self   :   Optional[Any]			):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_	   =       NllbTokenizer(__lowerCamelCase       ,      keep_accents=__lowerCamelCase			)
         tokenizer.save_pretrained(self.tmpdirname			)



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       NllbTokenizer(__lowerCamelCase       ,      keep_accents=__lowerCamelCase			)

         UpperCAmelCase_	   =       tokenizer.tokenize("This is a test"			)
         self.assertListEqual(__lowerCamelCase       ,      ["▁This", "▁is", "▁a", "▁t", "est"]			)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__lowerCamelCase			)       ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,      )

         UpperCAmelCase_	   =       tokenizer.tokenize("I was born in 92000, and this is falsé."			)
         self.assertListEqual(
             __lowerCamelCase       ,      [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]       ,      )
         UpperCAmelCase_	   =       tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
         self.assertListEqual(
             __lowerCamelCase       ,      [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]       ,      )

         UpperCAmelCase_	   =       tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
         self.assertListEqual(
             __lowerCamelCase       ,      [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]       ,      )



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                   UpperCAmelCase_	   =       self.rust_tokenizer_class.from_pretrained(__lowerCamelCase       ,      **__lowerCamelCase			)
                   UpperCAmelCase_	   =       self.tokenizer_class.from_pretrained(__lowerCamelCase       ,      **__lowerCamelCase			)

                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__lowerCamelCase			)

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)
                   UpperCAmelCase_	   =       tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f			)
                   self.assertSequenceEqual(__lowerCamelCase       ,      __lowerCamelCase			)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__lowerCamelCase			)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__lowerCamelCase       ,      __lowerCamelCase			)			)

                   shutil.rmtree(__lowerCamelCase			)

                   # Save tokenizer rust, legacy_format=True
                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__lowerCamelCase       ,      legacy_format=__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__lowerCamelCase			)

                   # Checks it save with the same files
                   self.assertSequenceEqual(__lowerCamelCase       ,      __lowerCamelCase			)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__lowerCamelCase			)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__lowerCamelCase       ,      __lowerCamelCase			)			)

                   shutil.rmtree(__lowerCamelCase			)

                   # Save tokenizer rust, legacy_format=False
                   UpperCAmelCase_	   =       tempfile.mkdtemp()

                   UpperCAmelCase_	   =       tokenizer_r.save_pretrained(__lowerCamelCase       ,      legacy_format=__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.save_pretrained(__lowerCamelCase			)

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)

                   # Checks everything loads correctly in the same way
                   UpperCAmelCase_	   =       tokenizer_r.from_pretrained(__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_p.from_pretrained(__lowerCamelCase			)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__lowerCamelCase       ,      __lowerCamelCase			)			)

                   shutil.rmtree(__lowerCamelCase			)



    @require_torch
    def  _lowercase						(self   :   int			):






         if not self.test_seqaseq:
              return

         UpperCAmelCase_	   =       self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f"""{tokenizer.__class__.__name__}"""			):
                   # Longer text that will definitely require truncation.
                   UpperCAmelCase_	   =       [
                       ''' UN Chief Says There Is No Military Solution in Syria''',
                       ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
                       ''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
                       ''' will only worsen the violence and misery for millions of people.''',
                   ]
                   UpperCAmelCase_	   =       [
                       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
                       '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
                       ''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
                       ''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
                   ]
                   try:
                        UpperCAmelCase_	   =       tokenizer.prepare_seqaseq_batch(
                            src_texts=__lowerCamelCase       ,      tgt_texts=__lowerCamelCase       ,      max_length=3       ,      max_target_length=10       ,      return_tensors="pt"       ,      src_lang="eng_Latn"       ,      tgt_lang="ron_Latn"       ,      )
                   except NotImplementedError:
                        return
                   self.assertEqual(batch.input_ids.shape[1]       ,      3			)
                   self.assertEqual(batch.labels.shape[1]       ,      10			)
                   # max_target_length will default to max_length if not specified
                   UpperCAmelCase_	   =       tokenizer.prepare_seqaseq_batch(
                       __lowerCamelCase       ,      tgt_texts=__lowerCamelCase       ,      max_length=3       ,      return_tensors="pt"			)
                   self.assertEqual(batch.input_ids.shape[1]       ,      3			)
                   self.assertEqual(batch.labels.shape[1]       ,      3			)

                   UpperCAmelCase_	   =       tokenizer.prepare_seqaseq_batch(
                       src_texts=__lowerCamelCase       ,      max_length=3       ,      max_target_length=10       ,      return_tensors="pt"			)
                   self.assertEqual(batch_encoder_only.input_ids.shape[1]       ,      3			)
                   self.assertEqual(batch_encoder_only.attention_mask.shape[1]       ,      3			)
                   self.assertNotIn("decoder_input_ids"       ,      __lowerCamelCase			)



    @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."			)
    def  _lowercase						(self   :   str			):
         pass






    def  _lowercase						(self   :   int			):






         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
                   UpperCAmelCase_	   =       [AddedToken("<special>"       ,      lstrip=__lowerCamelCase			)]

                   UpperCAmelCase_	   =       self.rust_tokenizer_class.from_pretrained(
                       __lowerCamelCase       ,      additional_special_tokens=__lowerCamelCase       ,      **__lowerCamelCase			)
                   UpperCAmelCase_	   =       tokenizer_r.encode("Hey this is a <special> token"			)

                   UpperCAmelCase_	   =       tokenizer_r.encode("<special>"       ,      add_special_tokens=__lowerCamelCase			)[0]

                   self.assertTrue(special_token_id in r_output			)

                   if self.test_slow_tokenizer:
                        UpperCAmelCase_	   =       self.rust_tokenizer_class.from_pretrained(
                            __lowerCamelCase       ,      additional_special_tokens=__lowerCamelCase       ,      **__lowerCamelCase       ,      )
                        UpperCAmelCase_	   =       self.tokenizer_class.from_pretrained(
                            __lowerCamelCase       ,      additional_special_tokens=__lowerCamelCase       ,      **__lowerCamelCase			)

                        UpperCAmelCase_	   =       tokenizer_p.encode("Hey this is a <special> token"			)

                        UpperCAmelCase_	   =       tokenizer_cr.encode("Hey this is a <special> token"			)

                        self.assertEqual(__lowerCamelCase       ,      __lowerCamelCase			)
                        self.assertEqual(__lowerCamelCase       ,      __lowerCamelCase			)
                        self.assertTrue(special_token_id in p_output			)
                        self.assertTrue(special_token_id in cr_output			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 	__A				(			unittest.TestCase     ):
    a__	:						List[Any]	       =  """facebook/nllb-200-distilled-600M"""
    a__	:						Tuple	       =  [
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    a__	:						int	       =  [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    a__	:						List[str]	       =  [
        256_047,
        16_297,
        134_408,
        8_165,
        248_066,
        14_734,
        950,
        1_135,
        105_721,
        3_573,
        83,
        27_352,
        108,
        49_486,
        2,
    ]



    @classmethod
    def  _lowercase						(cls   :   List[str]			):
         UpperCAmelCase_	   =       NllbTokenizer.from_pretrained(
             cls.checkpoint_name       ,      src_lang="eng_Latn"       ,      tgt_lang="ron_Latn"			)
         UpperCAmelCase_	   =       1
         return cls



    def  _lowercase						(self   :   List[Any]			):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]       ,      256001			)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]       ,      256002			)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]       ,      256057			)



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
         self.assertListEqual(self.expected_src_tokens       ,      __lowerCamelCase			)



    def  _lowercase						(self   :   List[Any]			):
         self.assertIn(__lowerCamelCase       ,      self.tokenizer.all_special_ids			)
         # fmt: off
         UpperCAmelCase_	   =       [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
         # fmt: on

         UpperCAmelCase_	   =       self.tokenizer.decode(__lowerCamelCase       ,      skip_special_tokens=__lowerCamelCase			)
         UpperCAmelCase_	   =       self.tokenizer.decode(generated_ids[1:]       ,      skip_special_tokens=__lowerCamelCase			)
         self.assertEqual(__lowerCamelCase       ,      __lowerCamelCase			)
         self.assertNotIn(self.tokenizer.eos_token       ,      __lowerCamelCase			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       ['''this is gunna be a long sentence ''' * 20]
         assert isinstance(src_text[0]       ,      __lowerCamelCase			)
         UpperCAmelCase_	   =       10
         UpperCAmelCase_	   =       self.tokenizer(__lowerCamelCase       ,      max_length=__lowerCamelCase       ,      truncation=__lowerCamelCase			).input_ids[0]
         self.assertEqual(ids[-1]       ,      2			)
         self.assertEqual(ids[0]       ,      __lowerCamelCase			)
         self.assertEqual(len(__lowerCamelCase			)       ,      __lowerCamelCase			)



    def  _lowercase						(self   :   Tuple			):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]			)       ,      [256203, 3]			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       tempfile.mkdtemp()
         UpperCAmelCase_	   =       self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__lowerCamelCase			)
         UpperCAmelCase_	   =       NllbTokenizer.from_pretrained(__lowerCamelCase			)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,      __lowerCamelCase			)



    @require_torch
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       self.tokenizer(
             self.src_text       ,      text_target=self.tgt_text       ,      padding=__lowerCamelCase       ,      truncation=__lowerCamelCase       ,      max_length=len(self.expected_src_tokens			)       ,      return_tensors="pt"       ,      )
         UpperCAmelCase_	   =       shift_tokens_right(
             batch["labels"]       ,      self.tokenizer.pad_token_id       ,      self.tokenizer.lang_code_to_id["ron_Latn"]			)

         self.assertIsInstance(__lowerCamelCase       ,      __lowerCamelCase			)

         self.assertEqual((2, 15)       ,      batch.input_ids.shape			)
         self.assertEqual((2, 15)       ,      batch.attention_mask.shape			)
         UpperCAmelCase_	   =       batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens       ,      __lowerCamelCase			)
         self.assertEqual(__lowerCamelCase       ,      batch.decoder_input_ids[0, 0]			)  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens       ,      [EN_CODE]			)
         self.assertEqual(self.tokenizer.suffix_tokens       ,      [self.tokenizer.eos_token_id]			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       self.tokenizer(self.src_text       ,      padding=__lowerCamelCase       ,      truncation=__lowerCamelCase       ,      max_length=3       ,      return_tensors="pt"			)
         UpperCAmelCase_	   =       self.tokenizer(
             text_target=self.tgt_text       ,      padding=__lowerCamelCase       ,      truncation=__lowerCamelCase       ,      max_length=10       ,      return_tensors="pt"			)
         UpperCAmelCase_	   =       targets['''input_ids''']
         UpperCAmelCase_	   =       shift_tokens_right(
             __lowerCamelCase       ,      self.tokenizer.pad_token_id       ,      decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]       ,      )

         self.assertEqual(batch.input_ids.shape[1]       ,      3			)
         self.assertEqual(batch.decoder_input_ids.shape[1]       ,      10			)



    @require_torch
    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       self.tokenizer._build_translation_inputs(
             "A test"       ,      return_tensors="pt"       ,      src_lang="eng_Latn"       ,      tgt_lang="fra_Latn"			)

         self.assertEqual(
             nested_simplify(__lowerCamelCase			)       ,      {
                 # A, test, EOS, en_XX
                 "input_ids": [[256047, 70, 7356, 2]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # ar_AR
                 "forced_bos_token_id": 256057,
             }       ,      )






    @require_torch
    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       self.tokenizer(
             "UN Chief says there is no military solution in Syria"       ,      src_lang="eng_Latn"       ,      tgt_lang="fra_Latn"			)
         self.assertEqual(
             inputs.input_ids       ,      [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]			)

         UpperCAmelCase_	   =       False
         UpperCAmelCase_	   =       self.tokenizer(
             "UN Chief says there is no military solution in Syria"       ,      src_lang="eng_Latn"       ,      tgt_lang="fra_Latn"			)
         self.assertEqual(
             inputs.input_ids       ,      [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]			)





'''simple docstring'''





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	__A				:



    @property
    def  _lowercase						(self   :   int			):
         return self.get_dummy_input()



    @property
    def  _lowercase						(self   :   Any			):
         if self.block_type == "down":
              return (4, 32, 16, 16)
         elif self.block_type == "mid":
              return (4, 32, 32, 32)
         elif self.block_type == "up":
              return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[str]=True       ,      __a   :   Any=False       ,      __a   :   List[Any]=False       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       4
         UpperCAmelCase_	   =       32
         UpperCAmelCase_	   =       (32, 32)

         UpperCAmelCase_	   =       torch.manual_seed(0			)
         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       (batch_size, num_channels) + sizes
         UpperCAmelCase_	   =       randn_tensor(__a       ,      generator=__a       ,      device=__a			)
         UpperCAmelCase_	   =       {"hidden_states": hidden_states}

         if include_temb:
              UpperCAmelCase_	   =       128
              UpperCAmelCase_	   =       randn_tensor((batch_size, temb_channels)       ,      generator=__a       ,      device=__a			)

         if include_res_hidden_states_tuple:
              UpperCAmelCase_	   =       torch.manual_seed(1			)
              UpperCAmelCase_	   =       (randn_tensor(__a       ,      generator=__a       ,      device=__a			),)

         if include_encoder_hidden_states:
              UpperCAmelCase_	   =       floats_tensor((batch_size, 32, 32)			).to(__a			)

         if include_skip_sample:
              UpperCAmelCase_	   =       randn_tensor(((batch_size, 3) + sizes)       ,      generator=__a       ,      device=__a			)

         return dummy_input



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       {
             "in_channels": 32,
             "out_channels": 32,
             "temb_channels": 128,
         }
         if self.block_type == "up":
              UpperCAmelCase_	   =       32

         if self.block_type == "mid":
              init_dict.pop("out_channels"			)

         UpperCAmelCase_	   =       self.dummy_input
         return init_dict, inputs_dict



    def  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         unet_block.to(__a			)
         unet_block.eval()

         with torch.no_grad():
              UpperCAmelCase_	   =       unet_block(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         self.assertEqual(output.shape       ,      self.output_shape			)

         UpperCAmelCase_	   =       output[0, -1, -3:, -3:]
         UpperCAmelCase_	   =       torch.tensor(__a			).to(__a			)
         assert torch_all_close(output_slice.flatten()       ,      __a       ,      atol=5E-3			)






    @unittest.skipIf(torch_device == "mps"       ,      "Training is not supported in mps"			)
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         model.to(__a			)
         model.train()
         UpperCAmelCase_	   =       model(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       randn_tensor(output.shape       ,      device=__a			)
         UpperCAmelCase_	   =       torch.nn.functional.mse_loss(__a       ,      __a			)
         loss.backward()




