




from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class 			A			:
     """simple docstring"""

     _snake_case:	Union[str, Any]     =MarianConfig
     _snake_case:	Dict     ={}
     _snake_case:	Union[str, Any]     ="gelu"




     def __init__(    self  :   Optional[int]	,  A__  :   Optional[int]	,  A__  :   str=1_3	,  A__  :   Any=7	,  A__  :   Dict=True	,  A__  :   Tuple=False	,  A__  :   List[Any]=9_9	,  A__  :   Dict=3_2	,  A__  :   Optional[int]=2	,  A__  :   str=4	,  A__  :   int=3_7	,  A__  :   Tuple=0.1	,  A__  :   List[str]=0.1	,  A__  :   Tuple=2_0	,  A__  :   List[str]=2	,  A__  :   int=1	,  A__  :   Dict=0	,  )				->       int:
           lowerCamelCase__        =				parent
           lowerCamelCase__        =				batch_size
           lowerCamelCase__        =				seq_length
           lowerCamelCase__        =				is_training
           lowerCamelCase__        =				use_labels
           lowerCamelCase__        =				vocab_size
           lowerCamelCase__        =				hidden_size
           lowerCamelCase__        =				num_hidden_layers
           lowerCamelCase__        =				num_attention_heads
           lowerCamelCase__        =				intermediate_size

           lowerCamelCase__        =				hidden_dropout_prob
           lowerCamelCase__        =				attention_probs_dropout_prob
           lowerCamelCase__        =				max_position_embeddings
           lowerCamelCase__        =				eos_token_id
           lowerCamelCase__        =				pad_token_id
           lowerCamelCase__        =				bos_token_id




     def  __snake_case   (    self  :   Optional[int]			)				->       Union[str, Any]:
           lowerCamelCase__        =				ids_tensor([self.batch_size, self.seq_length - 1]	,  self.vocab_size			)
           lowerCamelCase__        =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)	,  1			)
           lowerCamelCase__        =				tf.concat([input_ids, eos_tensor]	,  axis=1			)

           lowerCamelCase__        =				ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size			)

           lowerCamelCase__        =				self.config_cls(
               vocab_size=self.vocab_size	,  d_model=self.hidden_size	,  encoder_layers=self.num_hidden_layers	,  decoder_layers=self.num_hidden_layers	,  encoder_attention_heads=self.num_attention_heads	,  decoder_attention_heads=self.num_attention_heads	,  encoder_ffn_dim=self.intermediate_size	,  decoder_ffn_dim=self.intermediate_size	,  dropout=self.hidden_dropout_prob	,  attention_dropout=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  eos_token_ids=[2]	,  bos_token_id=self.bos_token_id	,  pad_token_id=self.pad_token_id	,  decoder_start_token_id=self.pad_token_id	,  **self.config_updates	,  )
           lowerCamelCase__        =				prepare_marian_inputs_dict(A__	,  A__	,  A__			)
           return config, inputs_dict




     def  __snake_case   (    self  :   List[Any]	,  A__  :   Dict	,  A__  :   Union[str, Any]			)				->       List[Any]:
           lowerCamelCase__        =				TFMarianModel(config=A__			).get_decoder()
           lowerCamelCase__        =				inputs_dict['input_ids']

           lowerCamelCase__        =				input_ids[:1, :]
           lowerCamelCase__        =				inputs_dict['attention_mask'][:1, :]
           lowerCamelCase__        =				inputs_dict['head_mask']
           lowerCamelCase__        =				1

           # first forward pass
           lowerCamelCase__        =				model(A__	,  attention_mask=A__	,  head_mask=A__	,  use_cache=A__			)

           lowerCamelCase__  ,		lowerCamelCase__        =				outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           lowerCamelCase__        =				ids_tensor((self.batch_size, 3)	,  config.vocab_size			)
           lowerCamelCase__        =				tf.cast(ids_tensor((self.batch_size, 3)	,  2			)	,  tf.inta			)

           # append to next input_ids and
           lowerCamelCase__        =				tf.concat([input_ids, next_tokens]	,  axis=-1			)
           lowerCamelCase__        =				tf.concat([attention_mask, next_attn_mask]	,  axis=-1			)

           lowerCamelCase__        =				model(A__	,  attention_mask=A__			)[0]
           lowerCamelCase__        =				model(A__	,  attention_mask=A__	,  past_key_values=A__			)[0]

           self.parent.assertEqual(next_tokens.shape[1]	,  output_from_past.shape[1]			)

           # select random slice
           lowerCamelCase__        =				int(ids_tensor((1,)	,  output_from_past.shape[-1]			)			)
           lowerCamelCase__        =				output_from_no_past[:, -3:, random_slice_idx]
           lowerCamelCase__        =				output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(A__	,  A__	,  rtol=1E-3			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       int     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       Optional[Any]=None     ,				lowerCamelCase:       int=None     ,				lowerCamelCase:       Dict=None     ,				lowerCamelCase:       Optional[Any]=None     ,				lowerCamelCase:       Union[str, Any]=None     ,				)					->			List[Any]:


      '''simple docstring'''




      if attention_mask is None:
            lowerCamelCase__        =				tf.cast(tf.math.not_equal(lowerCamelCase     ,				config.pad_token_id						)     ,				tf.inta						)
      if decoder_attention_mask is None:
            lowerCamelCase__        =				tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape     ,				dtype=tf.inta						),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,				config.pad_token_id						)     ,				tf.inta						),
                ]     ,				axis=-1     ,				)
      if head_mask is None:
            lowerCamelCase__        =				tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            lowerCamelCase__        =				tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            lowerCamelCase__        =				tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }






@require_tf
class 			A			(		_lowerCAmelCase			,						_lowerCAmelCase			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	List[str]     =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _snake_case:	List[str]     =(TFMarianMTModel,) if is_tf_available() else ()
     _snake_case:	Tuple     =(
         {
             "conversational": TFMarianMTModel,
             "feature-extraction": TFMarianModel,
             "summarization": TFMarianMTModel,
             "text2text-generation": TFMarianMTModel,
             "translation": TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _snake_case:	Optional[Any]     =True
     _snake_case:	Any     =False
     _snake_case:	Optional[int]     =False




     def  __snake_case   (    self  :   int			)				->       Tuple:
           lowerCamelCase__        =				TFMarianModelTester(self			)
           lowerCamelCase__        =				ConfigTester(self	,  config_class=A__			)




     def  __snake_case   (    self  :   Tuple			)				->       str:
           self.config_tester.run_common_tests()




     def  __snake_case   (    self  :   str			)				->       List[Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*A__			)






@require_tf
class 			A			(		unittest.TestCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =1_000  # show more chars for failing integration tests




     @classmethod
     def  __snake_case   (    cls  :   int			)				->       None:
           lowerCamelCase__        =				F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
           return cls




     @cached_property
     def  __snake_case   (    self  :   List[str]			)				->       MarianTokenizer:
           return AutoTokenizer.from_pretrained(self.model_name			)




     @property
     def  __snake_case   (    self  :   List[Any]			)				->       int:
           return self.tokenizer.eos_token_id




     @cached_property
     def  __snake_case   (    self  :   Tuple			)				->       Tuple:
           warnings.simplefilter('error'			)
           lowerCamelCase__        =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
           assert isinstance(A__	,  A__			)
           lowerCamelCase__        =				model.config
           self.assertListEqual(c.bad_words_ids	,  [[c.pad_token_id]]			)
           self.assertEqual(c.max_length	,  5_1_2			)
           self.assertEqual(c.decoder_start_token_id	,  c.pad_token_id			)
           return model




     def  __snake_case   (    self  :   Dict	,  **A__  :   List[Any]			)				->       str:
           lowerCamelCase__        =				self.translate_src_text(**A__			)
           self.assertListEqual(self.expected_text	,  A__			)




     def  __snake_case   (    self  :   Tuple	,  **A__  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				self.tokenizer(self.src_text	,  **A__	,  padding=A__	,  return_tensors='tf'			)
           lowerCamelCase__        =				self.model.generate(
               model_inputs.input_ids	,  attention_mask=model_inputs.attention_mask	,  num_beams=2	,  max_length=1_2_8			)
           lowerCamelCase__        =				self.tokenizer.batch_decode(generated_ids.numpy()	,  skip_special_tokens=A__			)
           return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Tuple     ="mt"
     _snake_case:	List[str]     ="en"
     _snake_case:	Tuple     =["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
     _snake_case:	Optional[Any]     =["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]




     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def  __snake_case   (    self  :   List[Any]			)				->       int:
           self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     ="en"
     _snake_case:	Union[str, Any]     ="zh"
     _snake_case:	Union[str, Any]     =["My name is Wolfgang and I live in Berlin"]
     _snake_case:	Optional[int]     =["我叫沃尔夫冈 我住在柏林"]




     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def  __snake_case   (    self  :   str			)				->       Any:
           self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	str     ="en"
     _snake_case:	Optional[Any]     ="ROMANCE"
     _snake_case:	Tuple     =[
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     _snake_case:	Tuple     =[
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]




     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def  __snake_case   (    self  :   List[str]			)				->       Union[str, Any]:
           self._assert_generated_batch_equal_expected()




     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def  __snake_case   (    self  :   List[Any]			)				->       Tuple:
           lowerCamelCase__        =				TranslationPipeline(self.model	,  self.tokenizer	,  framework='tf'			)
           lowerCamelCase__        =				pipeline(self.src_text			)
           self.assertEqual(self.expected_text	,  [x['translation_text'] for x in output]			)





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__SCREAMING_SNAKE_CASE    :   Dict       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   str       ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE    :   int       ={
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =VOCAB_FILES_NAMES
     _snake_case:	List[str]     =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:	str     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:	str     =["input_ids", "attention_mask"]
     _snake_case:	Union[str, Any]     =LongformerTokenizer




     def __init__(    self  :   str	,  A__  :   Optional[Any]=None	,  A__  :   Dict=None	,  A__  :   Any=None	,  A__  :   Optional[Any]="replace"	,  A__  :   Tuple="<s>"	,  A__  :   Any="</s>"	,  A__  :   Dict="</s>"	,  A__  :   Any="<s>"	,  A__  :   Optional[Any]="<unk>"	,  A__  :   Tuple="<pad>"	,  A__  :   Union[str, Any]="<mask>"	,  A__  :   List[str]=False	,  A__  :   List[str]=True	,  **A__  :   Optional[int]	,  )				->       List[str]:
           super().__init__(
               A__	,  A__	,  tokenizer_file=A__	,  errors=A__	,  bos_token=A__	,  eos_token=A__	,  sep_token=A__	,  cls_token=A__	,  unk_token=A__	,  pad_token=A__	,  mask_token=A__	,  add_prefix_space=A__	,  trim_offsets=A__	,  **A__	,  )

           lowerCamelCase__        =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                 lowerCamelCase__        =				getattr(A__	,  pre_tok_state.pop('type'			)			)
                 lowerCamelCase__        =				add_prefix_space
                 lowerCamelCase__        =				pre_tok_class(**A__			)

           lowerCamelCase__        =				add_prefix_space

           lowerCamelCase__        =				'post_processor'
           lowerCamelCase__        =				getattr(self.backend_tokenizer	,  A__	,  A__			)
           if tokenizer_component_instance:
                 lowerCamelCase__        =				json.loads(tokenizer_component_instance.__getstate__()			)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       lowerCamelCase__        =				tuple(state['sep']			)
                 if "cls" in state:
                       lowerCamelCase__        =				tuple(state['cls']			)

                 lowerCamelCase__        =				False

                 if state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                       lowerCamelCase__        =				add_prefix_space
                       lowerCamelCase__        =				True

                 if state.get('trim_offsets'	,  A__			) != trim_offsets:
                       lowerCamelCase__        =				trim_offsets
                       lowerCamelCase__        =				True

                 if changes_to_apply:
                       lowerCamelCase__        =				getattr(A__	,  state.pop('type'			)			)
                       lowerCamelCase__        =				component_class(**A__			)
                       setattr(self.backend_tokenizer	,  A__	,  A__			)




     @property
     def  __snake_case   (    self  :   Tuple			)				->       str:
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'			)
                 return None
           return str(self._mask_token			)




     @mask_token.setter
     def  __snake_case   (    self  :   Dict	,  A__  :   Tuple			)				->       int:
           lowerCamelCase__        =				AddedToken(A__	,  lstrip=A__	,  rstrip=A__			) if isinstance(A__	,  A__			) else value
           lowerCamelCase__        =				value




     def  __snake_case   (    self  :   Any	,  *A__  :   Any	,  **A__  :   Any			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)
           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Tuple	,  *A__  :   str	,  **A__  :   int			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)

           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Optional[str] = None			)				->       Tuple[str]:
           lowerCamelCase__        =				self._tokenizer.model.save(A__	,  name=A__			)
           return tuple(A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[str]=None			)				->       Dict:
           lowerCamelCase__        =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   List[int]	,  A__  :   Optional[List[int]] = None			)				->       List[int]:
           lowerCamelCase__        =				[self.sep_token_id]
           lowerCamelCase__        =				[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep			) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]