



"""simple docstring"""






import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def      _lowercase							(    __snake_case      )		->  Dict:
			__lowerCAmelCase :    Optional[int]				=			torch.exp(__snake_case      )
			__lowerCAmelCase :    int				=			torch.sum(__snake_case   ,dim=1      )  # sum of exp(x_i)
			__lowerCAmelCase :    Optional[Any]				=			torch.sum(x * exp_x   ,dim=1      )  # sum of x_i * exp(x_i)
			return torch.log(__snake_case      ) - B / A



class      A__  (    nn.Module				):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Any				=			config.output_attentions
				__lowerCAmelCase :    Optional[Any]				=			config.output_hidden_states
				__lowerCAmelCase :    Tuple				=			nn.ModuleList([BertLayer(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])
				__lowerCAmelCase :    int				=			nn.ModuleList([BertHighway(_SCREAMING_SNAKE_CASE) for _ in range(config.num_hidden_layers)])

				__lowerCAmelCase :    List[str]				=			[-1 for _ in range(config.num_hidden_layers)]

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

				"""simple docstring"""

				if (type(_SCREAMING_SNAKE_CASE) is float) or (type(_SCREAMING_SNAKE_CASE) is int):
							for i in range(len(self.early_exit_entropy)):
										__lowerCAmelCase :    List[Any]				=			x
				else:
							__lowerCAmelCase :    str				=			x

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			pooler.state_dict()
				for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
										param.copy_(loaded_model[name])







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			()
				__lowerCAmelCase :    Tuple				=			()
				__lowerCAmelCase :    Optional[Any]				=			()
				for i, layer_module in enumerate(self.layer):
							if self.output_hidden_states:
										__lowerCAmelCase :    Tuple				=			all_hidden_states + (hidden_states,)

							__lowerCAmelCase :    Any				=			layer_module(
							    _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     head_mask[i]			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    str				=			layer_outputs[0]

							if self.output_attentions:
										__lowerCAmelCase :    List[Any]				=			all_attentions + (layer_outputs[1],)

							__lowerCAmelCase :    Optional[int]				=			(hidden_states,)
							if self.output_hidden_states:
										__lowerCAmelCase :    Tuple				=			current_outputs + (all_hidden_states,)
							if self.output_attentions:
										__lowerCAmelCase :    List[str]				=			current_outputs + (all_attentions,)

							__lowerCAmelCase :    List[str]				=			self.highway[i](_SCREAMING_SNAKE_CASE)
							# logits, pooled_output

							if not self.training:
										__lowerCAmelCase :    Union[str, Any]				=			highway_exit[0]
										__lowerCAmelCase :    Dict				=			entropy(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    Dict				=			highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
										__lowerCAmelCase :    Dict				=			all_highway_exits + (highway_exit,)

										if highway_entropy < self.early_exit_entropy[i]:
													__lowerCAmelCase :    Any				=			(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
													raise HighwayException(_SCREAMING_SNAKE_CASE			,     i + 1)
							else:
										__lowerCAmelCase :    Optional[Any]				=			all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
							__lowerCAmelCase :    Union[str, Any]				=			all_hidden_states + (hidden_states,)

				__lowerCAmelCase :    str				=			(hidden_states,)
				if self.output_hidden_states:
							__lowerCAmelCase :    Union[str, Any]				=			outputs + (all_hidden_states,)
				if self.output_attentions:
							__lowerCAmelCase :    Optional[int]				=			outputs + (all_attentions,)

				__lowerCAmelCase :    List[str]				=			outputs + (all_highway_exits,)
				return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '    ,						__SCREAMING_SNAKE_CASE    ,						)
class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			config

				__lowerCAmelCase :    str				=			BertEmbeddings(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			DeeBertEncoder(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			BertPooler(_SCREAMING_SNAKE_CASE)

				self.init_weights()

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

				"""simple docstring"""

				self.encoder.init_highway_pooler(self.pooler)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			str:

				"""simple docstring"""

				return self.embeddings.word_embeddings

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

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			value

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(_SCREAMING_SNAKE_CASE)







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=None			,     _SCREAMING_SNAKE_CASE:						int=None			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     )							->			Dict:

				"""simple docstring"""

				if input_ids is not None and inputs_embeds is not None:
							raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
				elif input_ids is not None:
							__lowerCAmelCase :    Tuple				=			input_ids.size()
				elif inputs_embeds is not None:
							__lowerCAmelCase :    List[str]				=			inputs_embeds.size()[:-1]
				else:
							raise ValueError("You have to specify either input_ids or inputs_embeds")

				__lowerCAmelCase :    Any				=			input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if encoder_attention_mask is None:
							__lowerCAmelCase :    Tuple				=			torch.ones(_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE)
				if token_type_ids is None:
							__lowerCAmelCase :    Union[str, Any]				=			torch.zeros(_SCREAMING_SNAKE_CASE			,     dtype=torch.long			,     device=_SCREAMING_SNAKE_CASE)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				__lowerCAmelCase :    torch.Tensor				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if encoder_attention_mask.dim() == 3:
							__lowerCAmelCase :    Union[str, Any]				=			encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
							__lowerCAmelCase :    Optional[Any]				=			encoder_attention_mask[:, None, None, :]

				__lowerCAmelCase :    List[str]				=			encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()).dtype)  # fp16 compatibility
				__lowerCAmelCase :    Union[str, Any]				=			(1.0 - encoder_extended_attention_mask) * -1_0000.0

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				__lowerCAmelCase :    Union[str, Any]				=			self.get_head_mask(_SCREAMING_SNAKE_CASE			,     self.config.num_hidden_layers)

				__lowerCAmelCase :    Union[str, Any]				=			self.embeddings(
				    input_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			self.encoder(
				    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     encoder_attention_mask=_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    List[str]				=			encoder_outputs[0]
				__lowerCAmelCase :    Union[str, Any]				=			self.pooler(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			(
				    sequence_output,
				    pooled_output,
				) + encoder_outputs[
				    1:
				]  # add hidden_states and attentions if they are here
				return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			message
				__lowerCAmelCase :    Union[str, Any]				=			exit_layer  # start from 1!






class      A__  (    nn.Module				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase :    Optional[int]				=			BertPooler(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Optional[Any]				=			nn.Linear(config.hidden_size			,     config.num_labels)







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

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			encoder_outputs[0]
				__lowerCAmelCase :    Union[str, Any]				=			self.pooler(_SCREAMING_SNAKE_CASE)
				# "return" pooler_output

				# BertModel
				__lowerCAmelCase :    str				=			(pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				__lowerCAmelCase :    Tuple				=			bmodel_output[1]

				__lowerCAmelCase :    int				=			self.dropout(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			self.classifier(_SCREAMING_SNAKE_CASE)

				return logits, pooled_output






@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '    ,						__SCREAMING_SNAKE_CASE    ,						)
class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			config.num_labels
				__lowerCAmelCase :    Tuple				=			config.num_hidden_layers

				__lowerCAmelCase :    Optional[Any]				=			DeeBertModel(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			nn.Dropout(config.hidden_dropout_prob)
				__lowerCAmelCase :    Dict				=			nn.Linear(config.hidden_size			,     self.config.num_labels)

				self.init_weights()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE)
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						Any=None			,     _SCREAMING_SNAKE_CASE:						Dict=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Dict=None			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=-1			,     _SCREAMING_SNAKE_CASE:						str=False			,     )							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.num_layers
				try:
							__lowerCAmelCase :    Optional[int]				=			self.bert(
							    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     position_ids=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     inputs_embeds=_SCREAMING_SNAKE_CASE			,     )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							__lowerCAmelCase :    Tuple				=			outputs[1]

							__lowerCAmelCase :    Union[str, Any]				=			self.dropout(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    List[str]				=			self.classifier(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Union[str, Any]				=			(logits,) + outputs[2:]  # add hidden states and attention if they are here
				except HighwayException as e:
							__lowerCAmelCase :    str				=			e.message
							__lowerCAmelCase :    Optional[Any]				=			e.exit_layer
							__lowerCAmelCase :    Optional[Any]				=			outputs[0]

				if not self.training:
							__lowerCAmelCase :    int				=			entropy(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    str				=			[]
							__lowerCAmelCase :    Union[str, Any]				=			[]
				if labels is not None:
							if self.num_labels == 1:
										#  We are doing regression
										__lowerCAmelCase :    Optional[int]				=			MSELoss()
										__lowerCAmelCase :    List[Any]				=			loss_fct(logits.view(-1)			,     labels.view(-1))
							else:
										__lowerCAmelCase :    Optional[int]				=			CrossEntropyLoss()
										__lowerCAmelCase :    Tuple				=			loss_fct(logits.view(-1			,     self.num_labels)			,     labels.view(-1))

							# work with highway exits
							__lowerCAmelCase :    List[str]				=			[]
							for highway_exit in outputs[-1]:
										__lowerCAmelCase :    List[Any]				=			highway_exit[0]
										if not self.training:
													highway_logits_all.append(_SCREAMING_SNAKE_CASE)
													highway_entropy.append(highway_exit[2])
										if self.num_labels == 1:
													#  We are doing regression
													__lowerCAmelCase :    Optional[int]				=			MSELoss()
													__lowerCAmelCase :    str				=			loss_fct(highway_logits.view(-1)			,     labels.view(-1))
										else:
													__lowerCAmelCase :    int				=			CrossEntropyLoss()
													__lowerCAmelCase :    Optional[Any]				=			loss_fct(highway_logits.view(-1			,     self.num_labels)			,     labels.view(-1))
										highway_losses.append(_SCREAMING_SNAKE_CASE)

							if train_highway:
										__lowerCAmelCase :    List[Any]				=			(sum(highway_losses[:-1]),) + outputs
										# exclude the final highway, of course
							else:
										__lowerCAmelCase :    int				=			(loss,) + outputs
				if not self.training:
							__lowerCAmelCase :    Any				=			outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
										__lowerCAmelCase :    Any				=			(
										    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
										)  # use the highway of the last layer

				return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case				:  List[Any] =       get_tests_dir('fixtures/test_sentencepiece.model')

__snake_case				:  Union[str, Any] =       {'target_lang': 'fi', 'source_lang': 'en'}
__snake_case				:  Union[str, Any] =       '>>zh<<'
__snake_case				:  List[str] =       'Helsinki-NLP/'

if is_torch_available():
	__snake_case				:  Optional[int] =       'pt'
elif is_tf_available():
	__snake_case				:  List[Any] =       'tf'
else:
	__snake_case				:  Union[str, Any] =       'jax'



@require_sentencepiece
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MarianTokenizer
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

				super().setUp()
				__lowerCAmelCase :    Dict				=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
				__lowerCAmelCase :    Union[str, Any]				=			dict(zip(_SCREAMING_SNAKE_CASE			,     range(len(_SCREAMING_SNAKE_CASE))))
				__lowerCAmelCase :    int				=			Path(self.tmpdirname)
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["vocab"])
				save_json(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
				if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["source_spm"])
							copyfile(_SCREAMING_SNAKE_CASE			,     save_dir / VOCAB_FILES_NAMES["target_spm"])

				__lowerCAmelCase :    Tuple				=			MarianTokenizer.from_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			MarianTokenizer:

				"""simple docstring"""

				return MarianTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				return (
				    "This is a test",
				    "This is a test",
				)

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    int				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0]			,     "</s>")
				self.assertEqual(vocab_keys[1]			,     "<unk>")
				self.assertEqual(vocab_keys[-1]			,     "<pad>")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size			,     9)

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
				__lowerCAmelCase :    int				=			en_de_tokenizer(["I am a small frog"]			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			[38, 121, 14, 697, 3_8848, 0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     batch.input_ids[0])

				__lowerCAmelCase :    Tuple				=			tempfile.mkdtemp()
				en_de_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			[x.name for x in Path(_SCREAMING_SNAKE_CASE).glob("*")]
				self.assertIn("source.spm"			,     _SCREAMING_SNAKE_CASE)
				MarianTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_tokenizer()

				__lowerCAmelCase :    int				=			tok(
				    ["I am a small frog" * 1000, "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch.input_ids.shape			,     (2, 512))

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

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_tokenizer()
				__lowerCAmelCase :    str				=			tok(["I am a tiny frog", "I am a small frog"]			,     padding=_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(batch_smaller.input_ids.shape			,     (2, 10))

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "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, 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], [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]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_SCREAMING_SNAKE_CASE			,     model_name="Helsinki-NLP/opus-mt-en-de"			,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,     decode_kwargs={"use_source_tokenizer": True}			,     )







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

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

				__lowerCAmelCase :    List[str]				=			"Tämä on testi"
				__lowerCAmelCase :    int				=			"This is a test"

				__lowerCAmelCase :    Union[str, Any]				=			[76, 7, 2047, 2]
				__lowerCAmelCase :    Dict				=			[69, 12, 11, 940, 2]

				__lowerCAmelCase :    List[str]				=			tokenizer(_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			tokenizer(text_target=_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			tokenizer.decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)