import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE :	List[Any]							     =		get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE :	Tuple							     =		25_0004
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		25_0020
@require_sentencepiece
@require_tokenizers
class    A_ (       a_			,       unittest.TestCase ):
		_SCREAMING_SNAKE_CASE    				=			MBartaaTokenizer
		_SCREAMING_SNAKE_CASE    				=			MBartaaTokenizerFast
		_SCREAMING_SNAKE_CASE    				=			True
		_SCREAMING_SNAKE_CASE    				=			True

		def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
			super().setUp()

			# We have a SentencePiece fixture for testing
			__a       =  MBartaaTokenizer(__SCREAMING_SNAKE_CASE     ,							src_lang="en_XX"     ,							tgt_lang="ro_RO"     ,							keep_accents=__SCREAMING_SNAKE_CASE       )
			tokenizer.save_pretrained(self.tmpdirname       )

		def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
			__a       =  '<s>'
			__a       =  0

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      Any       ):
			__a       =  list(self.get_tokenizer().get_vocab().keys()       )

			self.assertEqual(vocab_keys[0]     ,							"<s>"       )
			self.assertEqual(vocab_keys[1]     ,							"<pad>"       )
			self.assertEqual(vocab_keys[-1]     ,							"<mask>"       )
			self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							10_54       )

		def 		_UpperCAmelCase   (		self :      Dict       ):
			self.assertEqual(self.get_tokenizer().vocab_size     ,							10_54       )

		def 		_UpperCAmelCase   (		self :      Any       ):
			__a       =  MBartaaTokenizer(__SCREAMING_SNAKE_CASE     ,							src_lang="en_XX"     ,							tgt_lang="ro_RO"     ,							keep_accents=__SCREAMING_SNAKE_CASE       )

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

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )     ,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,							)

			__a       =  tokenizer.tokenize("I was born in 92000, and this is falsé."       )
			self.assertListEqual(
			    __SCREAMING_SNAKE_CASE     ,							[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", "."]     ,							)
			__a       =  tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )
			self.assertListEqual(
			    __SCREAMING_SNAKE_CASE     ,							[
			        value + tokenizer.fairseq_offset
			        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
			    ]     ,							)

			__a       =  tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       )
			self.assertListEqual(
			    __SCREAMING_SNAKE_CASE     ,							[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>", "."]     ,							)

		@slow
		def 		_UpperCAmelCase   (		self :      Optional[int]       ):
			__a       =  {'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=__SCREAMING_SNAKE_CASE     ,							model_name="facebook/mbart-large-50"     ,							revision="d3913889c59cd5c9e456b269c376325eabad57e2"     ,							)

		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):







			if not self.test_slow_tokenizer:
				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
				return

			__a       =  (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
					__a       =  self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
					__a       =  self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

					__a       =  tempfile.mkdtemp()

					__a       =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE       )
					__a       =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE       )

					# 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       )       )
					__a       =  tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f       )
					self.assertSequenceEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

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

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )
						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

					shutil.rmtree(__SCREAMING_SNAKE_CASE       )

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

					__a       =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE     ,							legacy_format=__SCREAMING_SNAKE_CASE       )
					__a       =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE       )

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

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

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

					shutil.rmtree(__SCREAMING_SNAKE_CASE       )

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

					__a       =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE     ,							legacy_format=__SCREAMING_SNAKE_CASE       )
					__a       =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE       )

					# 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
					__a       =  tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE       )
					__a       =  tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE       )

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

					shutil.rmtree(__SCREAMING_SNAKE_CASE       )
@require_torch
@require_sentencepiece
@require_tokenizers
class    A_ (       unittest.TestCase ):
		_SCREAMING_SNAKE_CASE    				=			"""facebook/mbart-large-50-one-to-many-mmt"""
		_SCREAMING_SNAKE_CASE    				=			[
		    """ 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.""",
		]
		_SCREAMING_SNAKE_CASE    				=			[
		    """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.""",
		]
		_SCREAMING_SNAKE_CASE    				=			[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]

		@classmethod
		def 		_UpperCAmelCase   (		cls :      int       ):
			__a       =  MBartaaTokenizer.from_pretrained(
			    cls.checkpoint_name     ,							src_lang="en_XX"     ,							tgt_lang="ro_RO"       )
			__a       =  1
			return cls

		def 		_UpperCAmelCase   (		self :      str       ):
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]     ,							25_00_01       )
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]     ,							25_00_04       )
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]     ,							25_00_20       )
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]     ,							25_00_38       )

		def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
			__a       =  self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
			self.assertListEqual(self.expected_src_tokens     ,							__SCREAMING_SNAKE_CASE       )

		def 		_UpperCAmelCase   (		self :      int       ):
			self.assertIn(__SCREAMING_SNAKE_CASE     ,							self.tokenizer.all_special_ids       )
			__a       =  [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
			__a       =  self.tokenizer.decode(__SCREAMING_SNAKE_CASE     ,							skip_special_tokens=__SCREAMING_SNAKE_CASE       )
			__a       =  self.tokenizer.decode(generated_ids[1:]     ,							skip_special_tokens=__SCREAMING_SNAKE_CASE       )
			self.assertEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
			self.assertNotIn(self.tokenizer.eos_token     ,							__SCREAMING_SNAKE_CASE       )

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

		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
			self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )     ,							[25_00_53, 25_00_01]       )

		def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
			__a       =  tempfile.mkdtemp()
			__a       =  self.tokenizer.fairseq_tokens_to_ids
			self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE       )
			__a       =  MBartaaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )
			self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,							__SCREAMING_SNAKE_CASE       )

		@require_torch
		def 		_UpperCAmelCase   (		self :      Any       ):
			__a       =  self.tokenizer(self.src_text     ,							text_target=self.tgt_text     ,							padding=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
			__a       =  shift_tokens_right(batch["labels"]     ,							self.tokenizer.pad_token_id       )

			# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
			assert batch.input_ids[1][0] == EN_CODE
			assert batch.input_ids[1][-1] == 2
			assert batch.labels[1][0] == RO_CODE
			assert batch.labels[1][-1] == 2
			assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

		@require_torch
		def 		_UpperCAmelCase   (		self :      int       ):
			__a       =  self.tokenizer(
			    self.src_text     ,							text_target=self.tgt_text     ,							padding=__SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=len(self.expected_src_tokens       )     ,							return_tensors="pt"     ,							)
			__a       =  shift_tokens_right(batch["labels"]     ,							self.tokenizer.pad_token_id       )

			self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

			self.assertEqual((2, 14)     ,							batch.input_ids.shape       )
			self.assertEqual((2, 14)     ,							batch.attention_mask.shape       )
			__a       =  batch.input_ids.tolist()[0]
			self.assertListEqual(self.expected_src_tokens     ,							__SCREAMING_SNAKE_CASE       )
			self.assertEqual(2     ,							batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
			# 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 		_UpperCAmelCase   (		self :      Any       ):
			__a       =  self.tokenizer(self.src_text     ,							padding=__SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=3     ,							return_tensors="pt"       )
			__a       =  self.tokenizer(
			    text_target=self.tgt_text     ,							padding=__SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=10     ,							return_tensors="pt"       )
			__a       =  targets['input_ids']
			__a       =  shift_tokens_right(__SCREAMING_SNAKE_CASE     ,							self.tokenizer.pad_token_id       )

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

		@require_torch
		def 		_UpperCAmelCase   (		self :      List[Any]       ):
			__a       =  self.tokenizer._build_translation_inputs(
			    "A test"     ,							return_tensors="pt"     ,							src_lang="en_XX"     ,							tgt_lang="ar_AR"       )

			self.assertEqual(
			    nested_simplify(__SCREAMING_SNAKE_CASE       )     ,							{
			        # en_XX, A, test, EOS
			        "input_ids": [[25_00_04, 62, 30_34, 2]],
			        "attention_mask": [[1, 1, 1, 1]],
			        # ar_AR
			        "forced_bos_token_id": 25_00_01,
			    }     ,							)



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

   __a       =  encoder_input_tokens.shape[1]
   __a       =  torch.arange(__SCREAMING_SNAKE_CASE     ,							device=encoder_input_tokens.device       )
   x += self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


