


'''simple docstring'''




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int=0.0    ,      UpperCAmelCase__       :      Optional[int] = None    ,      UpperCAmelCase__       :      str = "geglu"    ,      UpperCAmelCase__       :      Optional[int] = None    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      bool = False    ,      UpperCAmelCase__       :      bool = True    ,      UpperCAmelCase__       :      str = "layer_norm"    ,      UpperCAmelCase__       :      bool = False    ,      )				->  List[Any]:
        super().__init__()
        lowerCAmelCase     =			only_cross_attention

        lowerCAmelCase     =			(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
        lowerCAmelCase     =			(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
           raise ValueError(
               F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
               F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''    )

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
           lowerCAmelCase     =			AdaLayerNorm(UpperCAmelCase__    ,      UpperCAmelCase__    )
        elif self.use_ada_layer_norm_zero:
           lowerCAmelCase     =			AdaLayerNormZero(UpperCAmelCase__    ,      UpperCAmelCase__    )
        else:
           lowerCAmelCase     =			nn.LayerNorm(UpperCAmelCase__    ,      elementwise_affine=UpperCAmelCase__    )
        lowerCAmelCase     =			Attention(
            query_dim=UpperCAmelCase__    ,      heads=UpperCAmelCase__    ,      dim_head=UpperCAmelCase__    ,      dropout=UpperCAmelCase__    ,      bias=UpperCAmelCase__    ,      cross_attention_dim=cross_attention_dim if only_cross_attention else None    ,      upcast_attention=UpperCAmelCase__    ,      )

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
           # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
           # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
           # the second cross attention block.
           lowerCAmelCase     =			(
               AdaLayerNorm(UpperCAmelCase__    ,      UpperCAmelCase__    )
               if self.use_ada_layer_norm
               else nn.LayerNorm(UpperCAmelCase__    ,      elementwise_affine=UpperCAmelCase__    )
           )
           lowerCAmelCase     =			Attention(
               query_dim=UpperCAmelCase__    ,      cross_attention_dim=cross_attention_dim if not double_self_attention else None    ,      heads=UpperCAmelCase__    ,      dim_head=UpperCAmelCase__    ,      dropout=UpperCAmelCase__    ,      bias=UpperCAmelCase__    ,      upcast_attention=UpperCAmelCase__    ,      )  # is self-attn if encoder_hidden_states is none
        else:
           lowerCAmelCase     =			None
           lowerCAmelCase     =			None

        # 3. Feed-forward
        lowerCAmelCase     =			nn.LayerNorm(UpperCAmelCase__    ,      elementwise_affine=UpperCAmelCase__    )
        lowerCAmelCase     =			FeedForward(UpperCAmelCase__    ,      dropout=UpperCAmelCase__    ,      activation_fn=UpperCAmelCase__    ,      final_dropout=UpperCAmelCase__    )

        # let chunk size default to None
        lowerCAmelCase     =			None
        lowerCAmelCase     =			0





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      int    )				->  Optional[int]:
        # Sets chunk feed-forward
        lowerCAmelCase     =			chunk_size
        lowerCAmelCase     =			dim





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      torch.FloatTensor    ,      UpperCAmelCase__       :      Optional[torch.FloatTensor] = None    ,      UpperCAmelCase__       :      Optional[torch.FloatTensor] = None    ,      UpperCAmelCase__       :      Optional[torch.FloatTensor] = None    ,      UpperCAmelCase__       :      Optional[torch.LongTensor] = None    ,      UpperCAmelCase__       :      Dict[str, Any] = None    ,      UpperCAmelCase__       :      Optional[torch.LongTensor] = None    ,      )				->  Any:
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
           lowerCAmelCase     =			self.norma(UpperCAmelCase__    ,      UpperCAmelCase__    )
        elif self.use_ada_layer_norm_zero:
           lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			self.norma(
               UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      hidden_dtype=hidden_states.dtype    )
        else:
           lowerCAmelCase     =			self.norma(UpperCAmelCase__    )

        lowerCAmelCase     =			cross_attention_kwargs if cross_attention_kwargs is not None else {}

        lowerCAmelCase     =			self.attna(
            UpperCAmelCase__    ,      encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None    ,      attention_mask=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        if self.use_ada_layer_norm_zero:
           lowerCAmelCase     =			gate_msa.unsqueeze(1    ) * attn_output
        lowerCAmelCase     =			attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
           lowerCAmelCase     =			(
               self.norma(UpperCAmelCase__    ,      UpperCAmelCase__    ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase__    )
           )

           lowerCAmelCase     =			self.attna(
               UpperCAmelCase__    ,      encoder_hidden_states=UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
           lowerCAmelCase     =			attn_output + hidden_states

        # 3. Feed-forward
        lowerCAmelCase     =			self.norma(UpperCAmelCase__    )

        if self.use_ada_layer_norm_zero:
           lowerCAmelCase     =			norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
           # "feed_forward_chunk_size" can be used to save memory
           if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
              raise ValueError(
                  F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''    )

           lowerCAmelCase     =			norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
           lowerCAmelCase     =			torch.cat(
               [self.ff(UpperCAmelCase__    ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase__    ,      dim=self._chunk_dim    )]    ,      dim=self._chunk_dim    ,      )
        else:
           lowerCAmelCase     =			self.ff(UpperCAmelCase__    )

        if self.use_ada_layer_norm_zero:
           lowerCAmelCase     =			gate_mlp.unsqueeze(1    ) * ff_output

        lowerCAmelCase     =			ff_output + hidden_states

        return hidden_states




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Tuple    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[int] = None    ,      UpperCAmelCase__       :      int = 4    ,      UpperCAmelCase__       :      float = 0.0    ,      UpperCAmelCase__       :      str = "geglu"    ,      UpperCAmelCase__       :      bool = False    ,      )				->  str:
        super().__init__()
        lowerCAmelCase     =			int(dim * mult    )
        lowerCAmelCase     =			dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
           lowerCAmelCase     =			GELU(UpperCAmelCase__    ,      UpperCAmelCase__    )
        if activation_fn == "gelu-approximate":
           lowerCAmelCase     =			GELU(UpperCAmelCase__    ,      UpperCAmelCase__    ,      approximate='tanh'    )
        elif activation_fn == "geglu":
           lowerCAmelCase     =			GEGLU(UpperCAmelCase__    ,      UpperCAmelCase__    )
        elif activation_fn == "geglu-approximate":
           lowerCAmelCase     =			ApproximateGELU(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			nn.ModuleList([]    )
        # project in
        self.net.append(UpperCAmelCase__    )
        # project dropout
        self.net.append(nn.Dropout(UpperCAmelCase__    )    )
        # project out
        self.net.append(nn.Linear(UpperCAmelCase__    ,      UpperCAmelCase__    )    )
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
           self.net.append(nn.Dropout(UpperCAmelCase__    )    )





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      Tuple    )				->  str:
        for module in self.net:
           lowerCAmelCase     =			module(UpperCAmelCase__    )
        return hidden_states




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      str = "none"    )				->  List[Any]:
        super().__init__()
        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			approximate





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]    )				->  Any:
        if gate.device.type != "mps":
           return F.gelu(UpperCAmelCase__    ,      approximate=self.approximate    )
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa    )    ,      approximate=self.approximate    ).to(dtype=gate.dtype    )





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      Tuple    )				->  List[str]:
        lowerCAmelCase     =			self.proj(UpperCAmelCase__    )
        lowerCAmelCase     =			self.gelu(UpperCAmelCase__    )
        return hidden_states




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        super().__init__()
        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      dim_out * 2    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Any:
        if gate.device.type != "mps":
           return F.gelu(UpperCAmelCase__    )
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa    )    ).to(dtype=gate.dtype    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.proj(UpperCAmelCase__    ).chunk(2    ,      dim=-1    )
        return hidden_states * self.gelu(UpperCAmelCase__    )




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  Optional[Any]:
        super().__init__()
        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        lowerCAmelCase     =			self.proj(UpperCAmelCase__    )
        return x * torch.sigmoid(1.702 * x    )




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Dict    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  Optional[int]:
        super().__init__()
        lowerCAmelCase     =			nn.Embedding(UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			nn.SiLU()
        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      embedding_dim * 2    )
        lowerCAmelCase     =			nn.LayerNorm(UpperCAmelCase__    ,      elementwise_affine=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    )				->  Dict:
        lowerCAmelCase     =			self.linear(self.silu(self.emb(UpperCAmelCase__    )    )    )
        lowerCAmelCase     ,  lowerCAmelCase     =			torch.chunk(UpperCAmelCase__    ,      2    )
        lowerCAmelCase     =			self.norm(UpperCAmelCase__    ) * (1 + scale) + shift
        return x




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      int    )				->  Dict:
        super().__init__()

        lowerCAmelCase     =			CombinedTimestepLabelEmbeddings(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			nn.SiLU()
        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      6 * embedding_dim    ,      bias=UpperCAmelCase__    )
        lowerCAmelCase     =			nn.LayerNorm(UpperCAmelCase__    ,      elementwise_affine=UpperCAmelCase__    ,      eps=1E-6    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]=None    )				->  List[Any]:
        lowerCAmelCase     =			self.linear(self.silu(self.emb(UpperCAmelCase__    ,      UpperCAmelCase__    ,      hidden_dtype=UpperCAmelCase__    )    )    )
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			emb.chunk(6    ,      dim=1    )
        lowerCAmelCase     =			self.norm(UpperCAmelCase__    ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp




class 		UpperCAmelCase_      (  nn.Module		):





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[str] = None    ,      UpperCAmelCase__       :      float = 1E-5    )				->  Optional[Any]:
        super().__init__()
        lowerCAmelCase     =			num_groups
        lowerCAmelCase     =			eps

        if act_fn is None:
           lowerCAmelCase     =			None
        else:
           lowerCAmelCase     =			get_activation(UpperCAmelCase__    )

        lowerCAmelCase     =			nn.Linear(UpperCAmelCase__    ,      out_dim * 2    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Any    )				->  Tuple:
        if self.act:
           lowerCAmelCase     =			self.act(UpperCAmelCase__    )
        lowerCAmelCase     =			self.linear(UpperCAmelCase__    )
        lowerCAmelCase     =			emb[:, :, None, None]
        lowerCAmelCase     ,  lowerCAmelCase     =			emb.chunk(2    ,      dim=1    )

        lowerCAmelCase     =			F.group_norm(UpperCAmelCase__    ,      self.num_groups    ,      eps=self.eps    )
        lowerCAmelCase     =			x * (1 + scale) + shift
        return x









import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


_UpperCAmelCase	:     List[str]   =get_tests_dir("""fixtures/test_sentencepiece.model""")


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

_UpperCAmelCase	:     Optional[int]   =25_0004
_UpperCAmelCase	:     Tuple   =25_0020







@require_sentencepiece
@require_tokenizers
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Tuple    =							MBartTokenizer
						SCREAMING_SNAKE_CASE__					:     Dict    =							MBartTokenizerFast
						SCREAMING_SNAKE_CASE__					:     Tuple    =							True
						SCREAMING_SNAKE_CASE__					:     List[str]    =							True




						def   lowercase_       ( self							)    ->			Dict:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowerCAmelCase_       :					str			 = MBartTokenizer(__lowercase      ,  keep_accents=__lowercase							)
										tokenizer.save_pretrained(self.tmpdirname							)




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Optional[int]			 = MBartTokenizer(__lowercase      ,  keep_accents=__lowercase							)

										lowerCAmelCase_       :					Dict			 = tokenizer.tokenize('''This is a test'''							)
										self.assertListEqual(__lowercase      ,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowercase							)      ,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]      ,  )

										lowerCAmelCase_       :					Optional[Any]			 = tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
										self.assertListEqual(
										    __lowercase      ,  [
										        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''',
										        '''.''',
										    ]      ,  )
										lowerCAmelCase_       :					Dict			 = tokenizer.convert_tokens_to_ids(__lowercase							)
										self.assertListEqual(
										    __lowercase      ,  [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]      ,  )

										lowerCAmelCase_       :					Union[str, Any]			 = tokenizer.convert_ids_to_tokens(__lowercase							)
										self.assertListEqual(
										    __lowercase      ,  [
										        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							)    ->			Dict:
										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

										lowerCAmelCase_       :					int			 = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																		lowerCAmelCase_       :					Optional[int]			 = self.rust_tokenizer_class.from_pretrained(__lowercase      ,  **__lowercase							)
																		lowerCAmelCase_       :					int			 = self.tokenizer_class.from_pretrained(__lowercase      ,  **__lowercase							)

																		lowerCAmelCase_       :					Tuple			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					Union[str, Any]			 = tokenizer_r.save_pretrained(__lowercase							)
																		lowerCAmelCase_       :					Dict			 = tokenizer_p.save_pretrained(__lowercase							)

																		# 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							)							)
																		lowerCAmelCase_       :					str			 = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f							)
																		self.assertSequenceEqual(__lowercase      ,  __lowercase							)

																		# Checks everything loads correctly in the same way
																		lowerCAmelCase_       :					Tuple			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					Dict			 = tokenizer_p.from_pretrained(__lowercase							)

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

																		shutil.rmtree(__lowercase							)

																		# Save tokenizer rust, legacy_format=True
																		lowerCAmelCase_       :					Optional[Any]			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					int			 = tokenizer_r.save_pretrained(__lowercase      ,  legacy_format=__lowercase							)
																		lowerCAmelCase_       :					Tuple			 = tokenizer_p.save_pretrained(__lowercase							)

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

																		# Checks everything loads correctly in the same way
																		lowerCAmelCase_       :					Optional[int]			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					List[Any]			 = tokenizer_p.from_pretrained(__lowercase							)

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

																		shutil.rmtree(__lowercase							)

																		# Save tokenizer rust, legacy_format=False
																		lowerCAmelCase_       :					Optional[Any]			 = tempfile.mkdtemp()

																		lowerCAmelCase_       :					List[str]			 = tokenizer_r.save_pretrained(__lowercase      ,  legacy_format=__lowercase							)
																		lowerCAmelCase_       :					Optional[int]			 = tokenizer_p.save_pretrained(__lowercase							)

																		# 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
																		lowerCAmelCase_       :					Dict			 = tokenizer_r.from_pretrained(__lowercase							)
																		lowerCAmelCase_       :					List[Any]			 = tokenizer_p.from_pretrained(__lowercase							)

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

																		shutil.rmtree(__lowercase							)







@require_torch
@require_sentencepiece
@require_tokenizers
class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     int    =							"""facebook/mbart-large-en-ro"""
						SCREAMING_SNAKE_CASE__					:     int    =							[
						    """ 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__					:     Optional[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.""",
						]
						SCREAMING_SNAKE_CASE__					:     str    =							[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




						@classmethod
						def   lowercase_       ( cls							)    ->			Optional[int]:
										lowerCAmelCase_       :					MBartTokenizer			 = MBartTokenizer.from_pretrained(
										    cls.checkpoint_name      ,  src_lang='''en_XX'''      ,  tgt_lang='''ro_RO'''							)
										lowerCAmelCase_       :					Optional[Any]			 = 1
										return cls




						def   lowercase_       ( self							)    ->			Optional[Any]:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']      ,  2_5_0_0_0_1							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']      ,  2_5_0_0_0_4							)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']      ,  2_5_0_0_2_0							)




						def   lowercase_       ( self							)    ->			Tuple:
										lowerCAmelCase_       :					Optional[int]			 = self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
										self.assertListEqual(self.expected_src_tokens      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			Any:
										self.assertIn(__lowercase      ,  self.tokenizer.all_special_ids							)
										lowerCAmelCase_       :					Union[str, Any]			 = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
										lowerCAmelCase_       :					Tuple			 = self.tokenizer.decode(__lowercase      ,  skip_special_tokens=__lowercase							)
										lowerCAmelCase_       :					List[Any]			 = self.tokenizer.decode(generated_ids[1:]      ,  skip_special_tokens=__lowercase							)
										self.assertEqual(__lowercase      ,  __lowercase							)
										self.assertNotIn(self.tokenizer.eos_token      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			Any:
										lowerCAmelCase_       :					Union[str, Any]			 = ['''this is gunna be a long sentence ''' * 2_0]
										assert isinstance(src_text[0]      ,  __lowercase							)
										lowerCAmelCase_       :					str			 = 1_0
										lowerCAmelCase_       :					Tuple			 = self.tokenizer(__lowercase      ,  max_length=__lowercase      ,  truncation=__lowercase							).input_ids[0]
										self.assertEqual(ids[-2]      ,  2							)
										self.assertEqual(ids[-1]      ,  __lowercase							)
										self.assertEqual(len(__lowercase							)      ,  __lowercase							)




						def   lowercase_       ( self							)    ->			int:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']							)      ,  [2_5_0_0_2_6, 2_5_0_0_0_1]							)




						def   lowercase_       ( self							)    ->			Dict:
										lowerCAmelCase_       :					Any			 = tempfile.mkdtemp()
										lowerCAmelCase_       :					int			 = self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__lowercase							)
										lowerCAmelCase_       :					Optional[Any]			 = MBartTokenizer.from_pretrained(__lowercase							)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,  __lowercase							)




						@require_torch
						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Tuple			 = self.tokenizer(self.src_text      ,  text_target=self.tgt_text      ,  padding=__lowercase      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					Tuple			 = shift_tokens_right(batch['''labels''']      ,  self.tokenizer.pad_token_id							)

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




						@require_torch
						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					str			 = self.tokenizer(
										    self.src_text      ,  text_target=self.tgt_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=len(self.expected_src_tokens							)      ,  return_tensors='''pt'''      ,  )

										lowerCAmelCase_       :					int			 = shift_tokens_right(batch['''labels''']      ,  self.tokenizer.pad_token_id							)

										self.assertIsInstance(__lowercase      ,  __lowercase							)

										self.assertEqual((2, 1_4)      ,  batch.input_ids.shape							)
										self.assertEqual((2, 1_4)      ,  batch.attention_mask.shape							)
										lowerCAmelCase_       :					str			 = batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens      ,  __lowercase							)
										self.assertEqual(2      ,  batch.decoder_input_ids[0, -1]							)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens      ,  []							)
										self.assertEqual(self.tokenizer.suffix_tokens      ,  [self.tokenizer.eos_token_id, EN_CODE]							)




						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Optional[Any]			 = self.tokenizer(self.src_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=3      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					Any			 = self.tokenizer(
										    text_target=self.tgt_text      ,  padding=__lowercase      ,  truncation=__lowercase      ,  max_length=1_0      ,  return_tensors='''pt'''							)
										lowerCAmelCase_       :					int			 = targets['''input_ids''']
										lowerCAmelCase_       :					Optional[Any]			 = shift_tokens_right(__lowercase      ,  self.tokenizer.pad_token_id							)

										self.assertEqual(batch.input_ids.shape[1]      ,  3							)
										self.assertEqual(batch.decoder_input_ids.shape[1]      ,  1_0							)




						@require_torch
						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Any			 = self.tokenizer._build_translation_inputs(
										    '''A test'''      ,  return_tensors='''pt'''      ,  src_lang='''en_XX'''      ,  tgt_lang='''ar_AR'''							)

										self.assertEqual(
										    nested_simplify(__lowercase							)      ,  {
										        # A, test, EOS, en_XX
										        '''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 2_5_0_0_0_1,
										    }      ,  )