from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowerCAmelCase    :  Optional[Any]										= logging.get_logger(__name__)

__lowerCAmelCase    :  Optional[Any]										= {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'blenderbot'
      __lowerCamelCase							=					['past_key_values']
      __lowerCamelCase							=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






      def __init__(      self  ,		__lowerCamelCase=80_08  ,		__lowerCamelCase=1_28  ,		__lowerCamelCase=2  ,		__lowerCamelCase=1_02_40  ,		__lowerCamelCase=32  ,		__lowerCamelCase=24  ,		__lowerCamelCase=1_02_40  ,		__lowerCamelCase=32  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=True  ,		__lowerCamelCase=True  ,		__lowerCamelCase="gelu"  ,		__lowerCamelCase=25_60  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.02  ,		__lowerCamelCase=1  ,		__lowerCamelCase=False  ,		__lowerCamelCase=0  ,		__lowerCamelCase=1  ,		__lowerCamelCase=2  ,		__lowerCamelCase=3  ,		__lowerCamelCase=2  ,		**__lowerCamelCase  ,		)     ->				Any:


          '''simple docstring'''







          snake_case:						List[Any]         =   vocab_size
          snake_case:						int         =   max_position_embeddings
          snake_case:						Any         =   d_model
          snake_case:						int         =   encoder_ffn_dim
          snake_case:						Dict         =   encoder_layers
          snake_case:						Any         =   encoder_attention_heads
          snake_case:						Dict         =   decoder_ffn_dim
          snake_case:						str         =   decoder_layers
          snake_case:						Union[str, Any]         =   decoder_attention_heads
          snake_case:						List[Any]         =   dropout
          snake_case:						Any         =   attention_dropout
          snake_case:						Tuple         =   activation_dropout
          snake_case:						Any         =   activation_function
          snake_case:						Dict         =   init_std
          snake_case:						Tuple         =   encoder_layerdrop
          snake_case:						Any         =   decoder_layerdrop
          snake_case:						Optional[int]         =   use_cache
          snake_case:						str         =   encoder_layers
          snake_case:						Any         =   scale_embedding  # scale factor will be sqrt(d_model) if True

          super().__init__(
              pad_token_id=__lowerCamelCase  ,		bos_token_id=__lowerCamelCase  ,		eos_token_id=__lowerCamelCase  ,		is_encoder_decoder=__lowerCamelCase  ,		decoder_start_token_id=__lowerCamelCase  ,		encoder_no_repeat_ngram_size=__lowerCamelCase  ,		forced_eos_token_id=__lowerCamelCase  ,		**__lowerCamelCase  ,		)





class 				lowerCamelCase			(							__snake_case					):






      @property
      def 			lowerCAmelCase_   (      self						)     ->				Mapping[str, Mapping[int, str]]:


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						List[Any]         =   OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]						)
              if self.use_past:
                  snake_case:						Optional[int]         =   {0: """batch"""}
                  snake_case:						Tuple         =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                  snake_case:						Union[str, Any]         =   {0: """batch""", 1: """decoder_sequence"""}
                  snake_case:						Union[str, Any]         =   {0: """batch""", 1: """decoder_sequence"""}
              if self.use_past:
                  self.fill_with_past_key_values_(__lowerCamelCase  ,		direction="""inputs"""						)
          elif self.task == "causal-lm":
              snake_case:						Optional[Any]         =   OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]						)
              if self.use_past:
                  snake_case    , snake_case:						List[Any]         =   self.num_layers
                  for i in range(__lowerCamelCase						):
                      snake_case:						Dict         =   {0: """batch""", 2: """past_sequence + sequence"""}
                      snake_case:						int         =   {0: """batch""", 2: """past_sequence + sequence"""}
          else:
              snake_case:						int         =   OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                      ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                  ]						)

          return common_inputs






      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def 			lowerCAmelCase_   (      self						)     ->				Mapping[str, Mapping[int, str]]:


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						Dict         =   super().outputs
          else:
              snake_case:						int         =   super(__lowerCamelCase  ,		self						).outputs
              if self.use_past:
                  snake_case    , snake_case:						Any         =   self.num_layers
                  for i in range(__lowerCamelCase						):
                      snake_case:						Union[str, Any]         =   {0: """batch""", 2: """past_sequence + sequence"""}
                      snake_case:						Any         =   {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				Mapping[str, Any]:


          '''simple docstring'''







          snake_case:						Tuple         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          # Generate decoder inputs
          snake_case:						Optional[int]         =   seq_length if not self.use_past else 1
          snake_case:						Any         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          snake_case:						str         =   {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          snake_case:						int         =   dict(**__lowerCamelCase  ,		**__lowerCamelCase						)

          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
              else:
                  import torch
              snake_case    , snake_case:						Dict         =   common_inputs["""input_ids"""].shape
              snake_case:						Dict         =   common_inputs["""decoder_input_ids"""].shape[1]
              snake_case    , snake_case:						List[str]         =   self.num_attention_heads
              snake_case:						str         =   (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              snake_case:						Any         =   decoder_seq_length
              snake_case:						Tuple         =   (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )
              snake_case:						Optional[Any]         =   torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(__lowerCamelCase  ,		__lowerCamelCase						)]  ,		dim=1						)
              snake_case:						Union[str, Any]         =   []
              snake_case    , snake_case:						Tuple         =   self.num_layers

              for _ in range(__lowerCamelCase						):
                  common_inputs["past_key_values"].append(
                      (
                          torch.zeros(__lowerCamelCase						),
                          torch.zeros(__lowerCamelCase						),
                          torch.zeros(__lowerCamelCase						),
                          torch.zeros(__lowerCamelCase						),
                      )						)
          return common_inputs






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				Mapping[str, Any]:


          '''simple docstring'''







          snake_case:						Dict         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)

          if self.use_past:
              if not is_torch_available():
                  raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
              else:
                  import torch
              snake_case    , snake_case:						Optional[Any]         =   common_inputs["""input_ids"""].shape
              snake_case:						Tuple         =   seqlen
              snake_case    , snake_case:						Union[str, Any]         =   self.num_layers
              snake_case    , snake_case:						int         =   self.num_attention_heads
              snake_case:						Tuple         =   (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              snake_case:						Optional[int]         =   common_inputs["""attention_mask"""].dtype
              snake_case:						Optional[int]         =   torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(__lowerCamelCase  ,		__lowerCamelCase  ,		dtype=__lowerCamelCase						)]  ,		dim=1						)
              snake_case:						Optional[Any]         =   [
                  (torch.zeros(__lowerCamelCase						), torch.zeros(__lowerCamelCase						)) for _ in range(__lowerCamelCase						)
              ]
          return common_inputs






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				Mapping[str, Any]:


          '''simple docstring'''







          snake_case:						Optional[int]         =   compute_effective_axis_dimension(
              __lowerCamelCase  ,		fixed_dimension=OnnxConfig.default_fixed_batch  ,		num_token_to_add=0						)

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case:						Tuple         =   tokenizer.num_special_tokens_to_add(__lowerCamelCase						)
          snake_case:						str         =   compute_effective_axis_dimension(
              __lowerCamelCase  ,		fixed_dimension=OnnxConfig.default_fixed_sequence  ,		num_token_to_add=__lowerCamelCase						)

          # Generate dummy inputs according to compute batch and sequence
          snake_case:						Dict         =   [""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
          snake_case:						Optional[int]         =   dict(tokenizer(__lowerCamelCase  ,		return_tensors=__lowerCamelCase						)						)
          return common_inputs






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = -1  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				Mapping[str, Any]:


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						Optional[int]         =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  __lowerCamelCase  ,		batch_size=__lowerCamelCase  ,		seq_length=__lowerCamelCase  ,		is_pair=__lowerCamelCase  ,		framework=__lowerCamelCase						)

          elif self.task == "causal-lm":
              snake_case:						Optional[int]         =   self._generate_dummy_inputs_for_causal_lm(
                  __lowerCamelCase  ,		batch_size=__lowerCamelCase  ,		seq_length=__lowerCamelCase  ,		is_pair=__lowerCamelCase  ,		framework=__lowerCamelCase						)
          else:
              snake_case:						Tuple         =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __lowerCamelCase  ,		batch_size=__lowerCamelCase  ,		seq_length=__lowerCamelCase  ,		is_pair=__lowerCamelCase  ,		framework=__lowerCamelCase						)

          return common_inputs






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				int:


          '''simple docstring'''







          if self.task in ["default", "seq2seq-lm"]:
              snake_case:						Optional[Any]         =   super()._flatten_past_key_values_(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          else:
              snake_case:						List[str]         =   super(__lowerCamelCase  ,		self						)._flatten_past_key_values_(
                  __lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Optional[Any]:


          '''simple docstring'''







          if direction not in ["inputs", "outputs"]:
              raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"						)

          snake_case:						Dict         =   """past_key_values""" if direction == """inputs""" else """present"""
          snake_case    , snake_case:						int         =   self.num_layers

          snake_case:						Tuple         =   """past_encoder_sequence"""
          snake_case:						Union[str, Any]         =   """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

          for i in range(__lowerCamelCase						):
              snake_case:						Dict         =   {0: """batch""", 2: decoder_sequence}
              snake_case:						Tuple         =   {0: """batch""", 2: decoder_sequence}
              snake_case:						List[Any]         =   {0: """batch""", 2: encoder_sequence}
              snake_case:						List[str]         =   {0: """batch""", 2: encoder_sequence}



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 				lowerCamelCase			:
      __lowerCamelCase							=					MarianConfig
      __lowerCamelCase							=					{}
      __lowerCamelCase							=					'gelu'






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase=13  ,		__lowerCamelCase=7  ,		__lowerCamelCase=True  ,		__lowerCamelCase=False  ,		__lowerCamelCase=99  ,		__lowerCamelCase=32  ,		__lowerCamelCase=2  ,		__lowerCamelCase=4  ,		__lowerCamelCase=37  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase=20  ,		__lowerCamelCase=2  ,		__lowerCamelCase=1  ,		__lowerCamelCase=0  ,		)     ->				Any:


          '''simple docstring'''







          snake_case:						Any         =   parent
          snake_case:						List[Any]         =   batch_size
          snake_case:						int         =   seq_length
          snake_case:						Tuple         =   is_training
          snake_case:						Union[str, Any]         =   use_labels
          snake_case:						Union[str, Any]         =   vocab_size
          snake_case:						Dict         =   hidden_size
          snake_case:						Any         =   num_hidden_layers
          snake_case:						List[Any]         =   num_attention_heads
          snake_case:						str         =   intermediate_size

          snake_case:						Union[str, Any]         =   hidden_dropout_prob
          snake_case:						List[Any]         =   attention_probs_dropout_prob
          snake_case:						Tuple         =   max_position_embeddings
          snake_case:						Optional[int]         =   eos_token_id
          snake_case:						Optional[Any]         =   pad_token_id
          snake_case:						Optional[Any]         =   bos_token_id






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


          '''simple docstring'''







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

          snake_case:						Optional[int]         =   ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

          snake_case:						List[str]         =   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  ,		)
          snake_case:						Tuple         =   prepare_marian_inputs_dict(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)
          return config, inputs_dict






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				List[str]:


          '''simple docstring'''







          snake_case:						Any         =   TFMarianModel(config=__lowerCamelCase						).get_decoder()
          snake_case:						str         =   inputs_dict["""input_ids"""]

          snake_case:						Union[str, Any]         =   input_ids[:1, :]
          snake_case:						Union[str, Any]         =   inputs_dict["""attention_mask"""][:1, :]
          snake_case:						str         =   inputs_dict["""head_mask"""]
          snake_case:						Optional[Any]         =   1

          # first forward pass
          snake_case:						int         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		head_mask=__lowerCamelCase  ,		use_cache=__lowerCamelCase						)

          snake_case    , snake_case:						List[str]         =   outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          snake_case:						Optional[int]         =   ids_tensor((self.batch_size, 3)  ,		config.vocab_size						)
          snake_case:						List[Any]         =   tf.cast(ids_tensor((self.batch_size, 3)  ,		2						)  ,		tf.inta						)

          # append to next input_ids and
          snake_case:						Optional[int]         =   tf.concat([input_ids, next_tokens]  ,		axis=-1						)
          snake_case:						Dict         =   tf.concat([attention_mask, next_attn_mask]  ,		axis=-1						)

          snake_case:						Optional[Any]         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase						)[0]
          snake_case:						Any         =   model(__lowerCamelCase  ,		attention_mask=__lowerCamelCase  ,		past_key_values=__lowerCamelCase						)[0]

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

          # select random slice
          snake_case:						str         =   int(ids_tensor((1,)  ,		output_from_past.shape[-1]						)						)
          snake_case:						List[Any]         =   output_from_no_past[:, -3:, random_slice_idx]
          snake_case:						Optional[int]         =   output_from_past[:, :, random_slice_idx]

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





def   a_					(_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Tuple=None			,	_lowerCAmelCase  :   Optional[int]=None			,	_lowerCAmelCase  :   Optional[int]=None			,	_lowerCAmelCase  :   Optional[Any]=None			,	_lowerCAmelCase  :   List[str]=None			,	)->				Any:
    if attention_mask is None:
        snake_case:						str         =   tf.cast(tf.math.not_equal(_lowerCAmelCase			,	config.pad_token_id       )			,	tf.inta       )
    if decoder_attention_mask is None:
        snake_case:						Dict         =   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:
        snake_case:						Union[str, Any]         =   tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        snake_case:						Optional[Any]         =   tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        snake_case:						Optional[int]         =   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 				lowerCamelCase			(							__snake_case				,     __snake_case				,     unittest.TestCase					):
      __lowerCamelCase							=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      __lowerCamelCase							=					(TFMarianMTModel,) if is_tf_available() else ()
      __lowerCamelCase							=					(
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      __lowerCamelCase							=					True
      __lowerCamelCase							=					False
      __lowerCamelCase							=					False






      def 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						int         =   TFMarianModelTester(self						)
          snake_case:						Tuple         =   ConfigTester(self  ,		config_class=__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          self.config_tester.run_common_tests()






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case:						int         =   self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase						)





@require_tf
class 				lowerCamelCase			(							unittest.TestCase					):
      __lowerCamelCase							=					1_000  # show more chars for failing integration tests






      @classmethod
      def 			lowerCAmelCase_   (      cls						)     ->				None:


          '''simple docstring'''







          snake_case:						Any         =   F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls






      @cached_property
      def 			lowerCAmelCase_   (      self						)     ->				MarianTokenizer:


          '''simple docstring'''







          return AutoTokenizer.from_pretrained(self.model_name						)






      @property
      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          return self.tokenizer.eos_token_id






      @cached_property
      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          warnings.simplefilter("""error"""						)
          snake_case:						TFMarianMTModel         =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
          assert isinstance(__lowerCamelCase  ,		__lowerCamelCase						)
          snake_case:						Optional[Any]         =   model.config
          self.assertListEqual(c.bad_words_ids  ,		[[c.pad_token_id]]						)
          self.assertEqual(c.max_length  ,		5_12						)
          self.assertEqual(c.decoder_start_token_id  ,		c.pad_token_id						)
          return model






      def 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   self.translate_src_text(**__lowerCamelCase						)
          self.assertListEqual(self.expected_text  ,		__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Any         =   self.tokenizer(self.src_text  ,		**__lowerCamelCase  ,		padding=__lowerCamelCase  ,		return_tensors="""tf"""						)
          snake_case:						int         =   self.model.generate(
              model_inputs.input_ids  ,		attention_mask=model_inputs.attention_mask  ,		num_beams=2  ,		max_length=1_28						)
          snake_case:						Any         =   self.tokenizer.batch_decode(generated_ids.numpy()  ,		skip_special_tokens=__lowerCamelCase						)
          return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'mt'
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      __lowerCamelCase							=					['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					'zh'
      __lowerCamelCase							=					['My name is Wolfgang and I live in Berlin']
      __lowerCamelCase							=					['我叫沃尔夫冈 我住在柏林']






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					'en'
      __lowerCamelCase							=					'ROMANCE'
      __lowerCamelCase							=					[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      __lowerCamelCase							=					[
          '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 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          self._assert_generated_batch_equal_expected()






      @unittest.skip("""Skipping until #12647 is resolved."""						)
      @slow
      def 			lowerCAmelCase_   (      self						)     ->				int:


          '''simple docstring'''







          snake_case:						List[str]         =   TranslationPipeline(self.model  ,		self.tokenizer  ,		framework="""tf"""						)
          snake_case:						List[Any]         =   pipeline(self.src_text						)
          self.assertEqual(self.expected_text  ,		[x["""translation_text"""] for x in output]						)


