




'''simple docstring'''






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


A_ 			=		logging.get_logger(__name__)

A_ 			=		{
    "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 					lowercase_			(	lowerCamelCase__       ):
     A_       							= 'blenderbot'
     A_       							= ['past_key_values']
     A_       							= {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





     def __init__(    self    :				Tuple    ,       __lowerCamelCase    :				Any=8008    ,       __lowerCamelCase    :				str=128    ,       __lowerCamelCase    :				Dict=2    ,       __lowerCamelCase    :				List[Any]=10240    ,       __lowerCamelCase    :				Optional[int]=32    ,       __lowerCamelCase    :				List[str]=24    ,       __lowerCamelCase    :				Optional[Any]=10240    ,       __lowerCamelCase    :				List[Any]=32    ,       __lowerCamelCase    :				int=0.0    ,       __lowerCamelCase    :				Tuple=0.0    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				List[str]="gelu"    ,       __lowerCamelCase    :				Any=2560    ,       __lowerCamelCase    :				List[str]=0.1    ,       __lowerCamelCase    :				Union[str, Any]=0.0    ,       __lowerCamelCase    :				Dict=0.0    ,       __lowerCamelCase    :				Any=0.0_2    ,       __lowerCamelCase    :				Optional[int]=1    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				List[Any]=0    ,       __lowerCamelCase    :				Optional[int]=1    ,       __lowerCamelCase    :				Dict=2    ,       __lowerCamelCase    :				str=3    ,       __lowerCamelCase    :				Any=2    ,       **__lowerCamelCase    :				int    ,       ):
       snake_case__					:      Dict  			=						vocab_size
       snake_case__					:      str  			=						max_position_embeddings
       snake_case__					:      str  			=						d_model
       snake_case__					:      Optional[int]  			=						encoder_ffn_dim
       snake_case__					:      str  			=						encoder_layers
       snake_case__					:      Dict  			=						encoder_attention_heads
       snake_case__					:      int  			=						decoder_ffn_dim
       snake_case__					:      Any  			=						decoder_layers
       snake_case__					:      Optional[int]  			=						decoder_attention_heads
       snake_case__					:      Any  			=						dropout
       snake_case__					:      Any  			=						attention_dropout
       snake_case__					:      Tuple  			=						activation_dropout
       snake_case__					:      List[Any]  			=						activation_function
       snake_case__					:      Union[str, Any]  			=						init_std
       snake_case__					:      Optional[Any]  			=						encoder_layerdrop
       snake_case__					:      Optional[int]  			=						decoder_layerdrop
       snake_case__					:      str  			=						use_cache
       snake_case__					:      List[Any]  			=						encoder_layers
       snake_case__					:      Any  			=						scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 					lowercase_			(	lowerCamelCase__       ):





     @property
     def  _lowerCAmelCase			(    self    :				Any ):






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






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





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[str] = -1    ,       __lowerCamelCase    :				List[Any] = -1    ,       __lowerCamelCase    :				int = False    ,       __lowerCamelCase    :				Tuple = None    ,       ):
       snake_case__					:      Optional[int]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )
       # Generate decoder inputs
       snake_case__					:      Union[str, Any]  			=						seq_length if not self.use_past else 1
       snake_case__					:      Optional[Any]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )
       snake_case__					:      int  			=						{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       snake_case__					:      List[str]  			=						dict(**lowercase_    ,       **lowercase_ )

       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__					:      int  			=						common_inputs['input_ids'].shape
         snake_case__					:      str  			=						common_inputs['decoder_input_ids'].shape[1]
         snake_case__			,       snake_case__					:      Dict  			=						self.num_attention_heads
         snake_case__					:      Any  			=						(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case__					:      Any  			=						decoder_seq_length
         snake_case__					:      List[str]  			=						(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         snake_case__					:      List[Any]  			=						torch.cat(
             [common_inputs['decoder_attention_mask'], torch.ones(lowercase_    ,       lowercase_ )]    ,       dim=1 )
         snake_case__					:      int  			=						[]
         snake_case__			,       snake_case__					:      List[str]  			=						self.num_layers

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





     def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Optional[int] = -1    ,       __lowerCamelCase    :				str = -1    ,       __lowerCamelCase    :				Tuple = False    ,       __lowerCamelCase    :				Any = None    ,       ):
       snake_case__					:      Tuple  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )

       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__					:      List[Any]  			=						common_inputs['input_ids'].shape
         snake_case__					:      str  			=						seqlen
         snake_case__			,       snake_case__					:      Dict  			=						self.num_layers
         snake_case__			,       snake_case__					:      Any  			=						self.num_attention_heads
         snake_case__					:      Optional[int]  			=						(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case__					:      List[str]  			=						common_inputs['attention_mask'].dtype
         snake_case__					:      Any  			=						torch.cat(
             [common_inputs['attention_mask'], torch.ones(lowercase_    ,       lowercase_    ,       dtype=lowercase_ )]    ,       dim=1 )
         snake_case__					:      List[Any]  			=						[
             (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
         ]
       return common_inputs





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any] = -1    ,       __lowerCamelCase    :				Tuple = -1    ,       __lowerCamelCase    :				List[str] = False    ,       __lowerCamelCase    :				List[str] = None    ,       ):
       snake_case__					:      List[str]  			=						compute_effective_axis_dimension(
           lowercase_    ,       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__					:      str  			=						tokenizer.num_special_tokens_to_add(lowercase_ )
       snake_case__					:      Optional[Any]  			=						compute_effective_axis_dimension(
           lowercase_    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=lowercase_ )

       # Generate dummy inputs according to compute batch and sequence
       snake_case__					:      int  			=						[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
       snake_case__					:      int  			=						dict(tokenizer(lowercase_    ,       return_tensors=lowercase_ ) )
       return common_inputs





     def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Dict = -1    ,       __lowerCamelCase    :				Tuple = -1    ,       __lowerCamelCase    :				int = False    ,       __lowerCamelCase    :				str = None    ,       ):






       if self.task in ["default", "seq2seq-lm"]:
         snake_case__					:      Dict  			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )

       elif self.task == "causal-lm":
         snake_case__					:      str  			=						self._generate_dummy_inputs_for_causal_lm(
             lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )
       else:
         snake_case__					:      List[str]  			=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_    ,       batch_size=lowercase_    ,       seq_length=lowercase_    ,       is_pair=lowercase_    ,       framework=lowercase_ )

       return common_inputs





     def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Optional[Any] ):






       if self.task in ["default", "seq2seq-lm"]:
         snake_case__					:      Dict  			=						super()._flatten_past_key_values_(lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )
       else:
         snake_case__					:      Dict  			=						super(lowercase_    ,       self )._flatten_past_key_values_(
             lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_ )





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Optional[int] ):






       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__					:      Tuple  			=						self.num_layers

       snake_case__					:      Optional[int]  			=						'past_encoder_sequence'
       snake_case__					:      int  			=						'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

       for i in range(lowercase_ ):
         snake_case__					:      Dict  			=						{0: 'batch', 2: decoder_sequence}
         snake_case__					:      Dict  			=						{0: 'batch', 2: decoder_sequence}
         snake_case__					:      List[str]  			=						{0: 'batch', 2: encoder_sequence}
         snake_case__					:      List[Any]  			=						{0: 'batch', 2: encoder_sequence}






'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A_ 			=		"platform"

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			)						->      Union[str, Any]:
      if attention_mask is None:
            snake_case__					:      Union[str, Any]  			=						np.where(input_ids != config.pad_token_id							,			1							,			0		)
      if decoder_attention_mask is None:
            snake_case__					:      int  			=						np.where(decoder_input_ids != config.pad_token_id							,			1							,			0		)
      if head_mask is None:
            snake_case__					:      Optional[int]  			=						np.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            snake_case__					:      str  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            snake_case__					:      int  			=						np.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": attention_mask,
      }






class 					lowercase_			:





     def __init__(    self    :				Any    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any]=13    ,       __lowerCamelCase    :				str=7    ,       __lowerCamelCase    :				Optional[int]=True    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				str=99    ,       __lowerCamelCase    :				List[str]=16    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Tuple=4    ,       __lowerCamelCase    :				Any=4    ,       __lowerCamelCase    :				Optional[int]="gelu"    ,       __lowerCamelCase    :				Optional[Any]=0.1    ,       __lowerCamelCase    :				List[Any]=0.1    ,       __lowerCamelCase    :				Optional[int]=32    ,       __lowerCamelCase    :				Optional[Any]=2    ,       __lowerCamelCase    :				Union[str, Any]=1    ,       __lowerCamelCase    :				Dict=0    ,       __lowerCamelCase    :				Tuple=0.0_2    ,       ):
           snake_case__					:      Tuple  			=						parent
           snake_case__					:      Any  			=						batch_size
           snake_case__					:      Union[str, Any]  			=						seq_length
           snake_case__					:      Optional[Any]  			=						is_training
           snake_case__					:      Union[str, Any]  			=						use_labels
           snake_case__					:      Union[str, Any]  			=						vocab_size
           snake_case__					:      Any  			=						hidden_size
           snake_case__					:      Tuple  			=						num_hidden_layers
           snake_case__					:      Tuple  			=						num_attention_heads
           snake_case__					:      str  			=						intermediate_size
           snake_case__					:      Union[str, Any]  			=						hidden_act
           snake_case__					:      Optional[Any]  			=						hidden_dropout_prob
           snake_case__					:      str  			=						attention_probs_dropout_prob
           snake_case__					:      Optional[int]  			=						max_position_embeddings
           snake_case__					:      Union[str, Any]  			=						eos_token_id
           snake_case__					:      str  			=						pad_token_id
           snake_case__					:      Tuple  			=						bos_token_id
           snake_case__					:      str  			=						initializer_range





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      List[Any]  			=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size )    ,       3    ,       self.vocab_size )
           snake_case__					:      Tuple  			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,       dtype=np.intaa ))    ,       -1 )

           snake_case__					:      Optional[int]  			=						shift_tokens_right(__lowerCamelCase    ,       1    ,       2 )

           snake_case__					:      Union[str, Any]  			=						MarianConfig(
               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_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       initializer_range=self.initializer_range    ,       use_cache=__lowerCamelCase    ,       )
           snake_case__					:      Union[str, Any]  			=						prepare_marian_inputs_dict(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__			,       snake_case__					:      Optional[int]  			=						self.prepare_config_and_inputs()
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Dict ):
           snake_case__					:      Optional[int]  			=						20
           snake_case__					:      str  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Dict  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      int  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      Dict  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      Optional[int]  			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,       dtype='i4' )

           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )
           snake_case__					:      Dict  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      List[str]  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      Tuple  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Union[str, Any]  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase )

           snake_case__					:      Tuple  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[int] ):
           snake_case__					:      int  			=						20
           snake_case__					:      Tuple  			=						model_class_name(__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						model.encode(inputs_dict['input_ids'] )

           snake_case__			,       snake_case__					:      Dict  			=						(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           snake_case__					:      List[str]  			=						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
               ]    ,       axis=-1    ,       )

           snake_case__					:      Union[str, Any]  			=						model.init_cache(decoder_input_ids.shape[0]    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      List[str]  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )

           snake_case__					:      List[Any]  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )
           snake_case__					:      Tuple  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      int  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       __lowerCamelCase    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_attention_mask=__lowerCamelCase    ,       decoder_position_ids=__lowerCamelCase    ,       )

           snake_case__					:      Dict  			=						model.decode(__lowerCamelCase    ,       __lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
           self.parent.assertTrue(diff < 1E-3    ,       msg=F"Max diff is {diff}" )






@require_flax
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase	,					lowerCAmelCase_       ):
     A_       							= True
     A_       							= (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     A_       							= (FlaxMarianMTModel,) if is_flax_available() else ()





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Any  			=						FlaxMarianModelTester(self )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__			,       snake_case__					:      str  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      Union[str, Any]  			=						self._prepare_for_class(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )

                       @jax.jit
                       def encode_jitted(__lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Optional[Any]=None    ,       **__lowerCamelCase    :				List[str] ):
                             return model.encode(input_ids=__lowerCamelCase    ,       attention_mask=__lowerCamelCase )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[Any]  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      Dict  			=						encode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__			,       snake_case__					:      Dict  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      List[str]  			=						model_class(__lowerCamelCase )
                       snake_case__					:      Union[str, Any]  			=						model.encode(inputs_dict['input_ids']    ,       inputs_dict['attention_mask'] )

                       snake_case__					:      str  			=						{
                           'decoder_input_ids': inputs_dict['decoder_input_ids'],
                           'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                           'encoder_outputs': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(__lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any] ):
                             return model.decode(
                                 decoder_input_ids=__lowerCamelCase    ,       decoder_attention_mask=__lowerCamelCase    ,       encoder_outputs=__lowerCamelCase    ,       )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      List[str]  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      str  			=						decode_jitted(**__lowerCamelCase ).to_tuple()

                       self.assertEqual(len(__lowerCamelCase )    ,       len(__lowerCamelCase ) )
                       for jitted_output, output in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                             self.assertEqual(jitted_output.shape    ,       output.shape )





     @slow
     def  _lowerCAmelCase			(    self    :				List[str] ):
           for model_class_name in self.all_model_classes:
                 snake_case__					:      Any  			=						model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de' )
                 # FlaxMarianForSequenceClassification expects eos token in input_ids
                 snake_case__					:      List[Any]  			=						np.ones((1, 1) ) * model.config.eos_token_id
                 snake_case__					:      Union[str, Any]  			=						model(__lowerCamelCase )
                 self.assertIsNotNone(__lowerCamelCase )






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	unittest.TestCase       ):
     A_       							= None
     A_       							= None





     @classmethod
     def  _lowerCAmelCase			(    cls    :				Union[str, Any] ):
           snake_case__					:      List[Any]  			=						F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
           return cls





     @cached_property
     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           return MarianTokenizer.from_pretrained(self.model_name )





     @property
     def  _lowerCAmelCase			(    self    :				int ):
           return self.tokenizer.eos_token_id





     @cached_property
     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      FlaxMarianMTModel  			=						FlaxMarianMTModel.from_pretrained(self.model_name )
           self.assertEqual(model.config.decoder_start_token_id    ,       model.config.pad_token_id )
           return model





     def  _lowerCAmelCase			(    self    :				Tuple    ,       **__lowerCamelCase    :				List[Any] ):
           snake_case__					:      Dict  			=						self.translate_src_text(**__lowerCamelCase )
           self.assertListEqual(self.expected_text    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str]    ,       **__lowerCamelCase    :				Dict ):
           snake_case__					:      str  			=						self.tokenizer(self.src_text    ,       padding=__lowerCamelCase    ,       return_tensors='np'    ,       **__lowerCamelCase )
           snake_case__					:      Any  			=						self.model.generate(
               model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2    ,       max_length=128    ,       ).sequences
           snake_case__					:      List[str]  			=						self.tokenizer.batch_decode(__lowerCamelCase    ,       skip_special_tokens=__lowerCamelCase )
           return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "fr"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
     ]
     A_       							= [
         "Je suis une petite grenouille.",
         "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "fr"
     A_       							= "en"
     A_       							= [
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     A_       							= [
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           self._assert_generated_batch_equal_expected()






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





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "de"
     A_       							= [
         "I am a small frog.",
         "Now I can forget the 100 words of german that I know.",
         "Tom asked his teacher for advice.",
         "That's how I would do it.",
         "Tom really admired Mary's courage.",
         "Turn around and close your eyes.",
     ]
     A_       							= [
         "Ich bin ein kleiner Frosch.",
         "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
         "Tom bat seinen Lehrer um Rat.",
         "So würde ich das machen.",
         "Tom bewunderte Marias Mut wirklich.",
         "Drehen Sie sich um und schließen Sie die Augen.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				Dict ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "zh"
     A_       							= ["My name is Wolfgang and I live in Berlin"]
     A_       							= ["我叫沃尔夫冈 我住在柏林"]





     @slow
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "ru"
     A_       							= "fr"
     A_       							= ["Он показал мне рукопись своей новой пьесы."]
     A_       							= ["Il m'a montré le manuscrit de sa nouvelle pièce."]





     @slow
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= "en"
     A_       							= "ROMANCE"
     A_       							= [
         ">>fr<< Don't spend so much time watching TV.",
         ">>pt<< Your message has been sent.",
         ">>es<< He's two years older than me.",
     ]
     A_       							= [
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]





     @slow
     def  _lowerCAmelCase			(    self    :				int ):
           self._assert_generated_batch_equal_expected()
