






"""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_			: int				=			logging.get_logger(__name__)

A_			: List[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 		a_      (	__UpperCAmelCase ):







      '''simple docstring'''

      lowerCamelCase__       :				Optional[int] =	'blenderbot'
      lowerCamelCase__       :				str =	['past_key_values']
      lowerCamelCase__       :				Optional[Any] =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




      def __init__(self,     lowerCamelCase_=8_0_0_8,     lowerCamelCase_=1_2_8,     lowerCamelCase_=2,     lowerCamelCase_=1_0_2_4_0,     lowerCamelCase_=3_2,     lowerCamelCase_=2_4,     lowerCamelCase_=1_0_2_4_0,     lowerCamelCase_=3_2,     lowerCamelCase_=0.0,     lowerCamelCase_=0.0,     lowerCamelCase_=True,     lowerCamelCase_=True,     lowerCamelCase_="gelu",     lowerCamelCase_=2_5_6_0,     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_,     ):

       '''simple docstring'''



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

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



class 		a_      (	__UpperCAmelCase ):







      '''simple docstring'''

      @property
      def 			a__							(self					):

       '''simple docstring'''



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

       '''simple docstring'''



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




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_ = -1,     lowerCamelCase_ = -1,     lowerCamelCase_ = False,     lowerCamelCase_ = None,     ):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_					)
       # Generate decoder inputs
       lowerCamelCase__     :			Optional[Any]     				=						seq_length if not self.use_past else 1
       lowerCamelCase__     :			List[Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_					)
       lowerCamelCase__     :			Any     				=						{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       lowerCamelCase__     :			int     				=						dict(**UpperCAmelCase_,     **UpperCAmelCase_					)

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

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




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_ = -1,     lowerCamelCase_ = -1,     lowerCamelCase_ = False,     lowerCamelCase_ = None,     ):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[int]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_					)

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




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_ = -1,     lowerCamelCase_ = -1,     lowerCamelCase_ = False,     lowerCamelCase_ = None,     ):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						compute_effective_axis_dimension(
           UpperCAmelCase_,     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
       lowerCamelCase__     :			Any     				=						tokenizer.num_special_tokens_to_add(UpperCAmelCase_					)
       lowerCamelCase__     :			Any     				=						compute_effective_axis_dimension(
           UpperCAmelCase_,     fixed_dimension=OnnxConfig.default_fixed_sequence,     num_token_to_add=UpperCAmelCase_					)

       # Generate dummy inputs according to compute batch and sequence
       lowerCamelCase__     :			Union[str, Any]     				=						[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
       lowerCamelCase__     :			Any     				=						dict(tokenizer(UpperCAmelCase_,     return_tensors=UpperCAmelCase_					)					)
       return common_inputs




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_ = -1,     lowerCamelCase_ = -1,     lowerCamelCase_ = False,     lowerCamelCase_ = None,     ):

       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase__     :			Any     				=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            UpperCAmelCase_,     batch_size=UpperCAmelCase_,     seq_length=UpperCAmelCase_,     is_pair=UpperCAmelCase_,     framework=UpperCAmelCase_					)

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

       return common_inputs




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase__     :			Optional[int]     				=						super()._flatten_past_key_values_(UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_					)
       else:
        lowerCamelCase__     :			List[str]     				=						super(UpperCAmelCase_,     self					)._flatten_past_key_values_(
            UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_					)





      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Dict     				=						'past_key_values' if direction == 'inputs' else 'present'
       lowerCamelCase__     :			Any     				=						self.num_layers

       lowerCamelCase__     :			Any     				=						'past_encoder_sequence'
       lowerCamelCase__     :			Any     				=						'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

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



"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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 AutoTokenizer
					from transformers.models.mbart.modeling_flax_mbart import (
					    FlaxMBartForConditionalGeneration,
					    FlaxMBartForQuestionAnswering,
					    FlaxMBartForSequenceClassification,
					    FlaxMBartModel,
					    shift_tokens_right,
					)
def 		UpperCAmelCase					(		a_,						a_,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						a_=None,						):





		'''simple docstring'''


		if attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(input_ids != config.pad_token_id,						1,						0			)
		if decoder_attention_mask is None:
				lowerCamelCase     :			Dict		=    np.where(decoder_input_ids != config.pad_token_id,						1,						0			)
		if head_mask is None:
				lowerCamelCase     :			List[Any]		=    np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				lowerCamelCase     :			Optional[Any]		=    np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				lowerCamelCase     :			Any		=    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": decoder_attention_mask,
		}






class        _lowercase   (				unittest.TestCase		):




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=13		, UpperCAmelCase_=7		, UpperCAmelCase_=True		, UpperCAmelCase_=False		, UpperCAmelCase_=99		, UpperCAmelCase_=16		, UpperCAmelCase_=2		, UpperCAmelCase_=4		, UpperCAmelCase_=4		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=32		, UpperCAmelCase_=2		, UpperCAmelCase_=1		, UpperCAmelCase_=0		, UpperCAmelCase_=2		, UpperCAmelCase_=0.02		, )		->							Union[str, Any]:
			lowerCamelCase     :			Any		=    parent
			lowerCamelCase     :			Dict		=    batch_size
			lowerCamelCase     :			str		=    seq_length
			lowerCamelCase     :			Dict		=    is_training
			lowerCamelCase     :			List[Any]		=    use_labels
			lowerCamelCase     :			Dict		=    vocab_size
			lowerCamelCase     :			List[str]		=    hidden_size
			lowerCamelCase     :			Tuple		=    num_hidden_layers
			lowerCamelCase     :			Union[str, Any]		=    num_attention_heads
			lowerCamelCase     :			int		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			str		=    hidden_dropout_prob
			lowerCamelCase     :			str		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[str]		=    max_position_embeddings
			lowerCamelCase     :			int		=    eos_token_id
			lowerCamelCase     :			Optional[int]		=    pad_token_id
			lowerCamelCase     :			Dict		=    bos_token_id
			lowerCamelCase     :			List[Any]		=    decoder_start_token_id
			lowerCamelCase     :			Dict		=    initializer_range




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			List[Any]		=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size					)		, 3		, self.vocab_size					)
			lowerCamelCase     :			List[Any]		=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		, dtype=np.intaa					))		, -1					)

			lowerCamelCase     :			int		=    shift_tokens_right(UpperCAmelCase_		, 1					)

			lowerCamelCase     :			Dict		=    MBartConfig(
			    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		, decoder_start_token_id=self.decoder_start_token_id		, initializer_range=self.initializer_range		, use_cache=UpperCAmelCase_		, )
			lowerCamelCase     :			List[str]		=    prepare_mbart_inputs_dict(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			return config, inputs_dict




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.prepare_config_and_inputs()
			return config, inputs_dict




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Any:
			lowerCamelCase     :			List[str]		=    20
			lowerCamelCase     :			Optional[int]		=    model_class_name(UpperCAmelCase_					)

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

			lowerCamelCase	,    lowerCamelCase     :			List[str]		=    (
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			lowerCamelCase     :			str		=    model.init_cache(decoder_input_ids.shape[0]		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		, dtype='i4'					)

			lowerCamelCase     :			Tuple		=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )
			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, :-1]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			Optional[int]		=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			Union[str, Any]		=    model.decode(
			    decoder_input_ids[:, -1:]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			Optional[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase     :			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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			int		=    20
			lowerCamelCase     :			Tuple		=    model_class_name(UpperCAmelCase_					)

			lowerCamelCase     :			Optional[Any]		=    model.encode(inputs_dict['input_ids']					)

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

			lowerCamelCase     :			Optional[Any]		=    jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
			    ]		, axis=-1		, )

			lowerCamelCase     :			Optional[Any]		=    model.init_cache(decoder_input_ids.shape[0]		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]		, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		, )

			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, :-1]		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, past_key_values=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )
			lowerCamelCase     :			int		=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			int		=    model.decode(
			    decoder_input_ids[:, -1:]		, UpperCAmelCase_		, past_key_values=outputs_cache.past_key_values		, decoder_attention_mask=UpperCAmelCase_		, decoder_position_ids=UpperCAmelCase_		, )

			lowerCamelCase     :			List[Any]		=    model.decode(UpperCAmelCase_		, UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_					)

			lowerCamelCase     :			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}"""					)






@require_flax
class        _lowercase   (				unittest.TestCase		):
	lowercase_        =	9_9




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]		, dtype=np.intaa		, )

			lowerCamelCase     :			List[Any]		=    input_ids.shape[0]
			lowerCamelCase     :			Optional[Any]		=    MBartConfig(
			    vocab_size=self.vocab_size		, d_model=24		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=32		, decoder_ffn_dim=32		, max_position_embeddings=48		, eos_token_id=2		, pad_token_id=1		, bos_token_id=0		, )
			return config, input_ids, batch_size




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			str		=    FlaxMBartForSequenceClassification(UpperCAmelCase_					)
			lowerCamelCase     :			Tuple		=    model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    (batch_size, config.num_labels)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			str		=    self._get_config_and_data()
			lowerCamelCase     :			Any		=    FlaxMBartForQuestionAnswering(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    model(input_ids=UpperCAmelCase_					)

			self.assertEqual(outputs['start_logits'].shape		, input_ids.shape					)
			self.assertEqual(outputs['end_logits'].shape		, input_ids.shape					)




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self._get_config_and_data()
			lowerCamelCase     :			List[str]		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    lm_model(input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    (batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Optional[Any]		=    MBartConfig(
			    vocab_size=self.vocab_size		, d_model=14		, encoder_layers=2		, decoder_layers=2		, encoder_attention_heads=2		, decoder_attention_heads=2		, encoder_ffn_dim=8		, decoder_ffn_dim=8		, max_position_embeddings=48		, )
			lowerCamelCase     :			int		=    FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			Any		=    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    lm_model(input_ids=UpperCAmelCase_		, decoder_input_ids=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    (*summary.shape, config.vocab_size)
			self.assertEqual(outputs['logits'].shape		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			Optional[int]		=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		, dtype=np.intaa					)
			lowerCamelCase     :			List[Any]		=    shift_tokens_right(UpperCAmelCase_		, 1					)
			lowerCamelCase     :			str		=    np.equal(UpperCAmelCase_		, 1					).astype(np.floataa					).sum()
			lowerCamelCase     :			Any		=    np.equal(UpperCAmelCase_		, 1					).astype(np.floataa					).sum()
			self.assertEqual(shifted.shape		, input_ids.shape					)
			self.assertEqual(UpperCAmelCase_		, n_pad_before - 1					)
			self.assertTrue(np.equal(shifted[:, 0]		, 2					).all()					)






@require_flax
class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase				,       __UpperCAmelCase		):
	lowercase_        =	True
	lowercase_        =	(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase_        =	(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Any		=    FlaxMBartModelTester(self					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase	,    lowerCamelCase     :			int		=    self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			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(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							lowerCamelCase     :			Union[str, Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_					)
							lowerCamelCase     :			Dict		=    model_class(UpperCAmelCase_					)

							@jax.jit
							def encode_jitted(UpperCAmelCase_		, UpperCAmelCase_=None		, **UpperCAmelCase_					):
									return model.encode(input_ids=UpperCAmelCase_		, attention_mask=UpperCAmelCase_					)

							with self.subTest('JIT Enabled'					):
									lowerCamelCase     :			Dict		=    encode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			List[Any]		=    encode_jitted(**UpperCAmelCase_					).to_tuple()

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




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase	,    lowerCamelCase     :			Union[str, Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							lowerCamelCase     :			int		=    model_class(UpperCAmelCase_					)
							lowerCamelCase     :			Any		=    model.encode(inputs_dict['input_ids']		, inputs_dict['attention_mask']					)

							lowerCamelCase     :			Optional[int]		=    {
							    '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(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					):
									return model.decode(
									    decoder_input_ids=UpperCAmelCase_		, decoder_attention_mask=UpperCAmelCase_		, encoder_outputs=UpperCAmelCase_		, )

							with self.subTest('JIT Enabled'					):
									lowerCamelCase     :			int		=    decode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest('JIT Disabled'					):
									with jax.disable_jit():
											lowerCamelCase     :			Dict		=    decode_jitted(**UpperCAmelCase_					).to_tuple()

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




	@slow
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			List[Any]		=    model_class_name.from_pretrained('facebook/mbart-large-cc25'		, from_pt=UpperCAmelCase_					)
					# FlaxMBartForSequenceClassification expects eos token in input_ids
					lowerCamelCase     :			str		=    np.ones((1, 1)					) * model.config.eos_token_id
					lowerCamelCase     :			int		=    model(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)






@require_flax
@require_sentencepiece
@require_tokenizers
class        _lowercase   (				unittest.TestCase		):
	lowercase_        =	[
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	lowercase_        =	[
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	lowercase_        =	'facebook/mbart-large-en-ro'




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return AutoTokenizer.from_pretrained(self.model_name					)




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		, from_pt=UpperCAmelCase_					)
			return model




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			List[str]		=    self.translate_src_text(**UpperCAmelCase_					)
			self.assertListEqual(self.expected_text		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.tokenizer(self.src_text		, **UpperCAmelCase_		, return_tensors='np'					)
			lowerCamelCase     :			List[Any]		=    self.model.generate(
			    model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']		, early_stopping=UpperCAmelCase_		, num_beams=2		, ).sequences
			lowerCamelCase     :			str		=    self.tokenizer.batch_decode(UpperCAmelCase_		, skip_special_tokens=UpperCAmelCase_					)
			return generated_words




	@slow
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			self._assert_generated_batch_equal_expected()






