

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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
  UpperCAmelCase_  							=				'platform'

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )




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

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=13,	__magic_name__=7,	__magic_name__=True,	__magic_name__=False,	__magic_name__=99,	__magic_name__=16,	__magic_name__=2,	__magic_name__=4,	__magic_name__=4,	__magic_name__="gelu",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=32,	__magic_name__=2,	__magic_name__=1,	__magic_name__=0,	__magic_name__=0.02,	)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   parent
       UpperCamelCase__							:       Optional[int]      =   batch_size
       UpperCamelCase__							:       int      =   seq_length
       UpperCamelCase__							:       int      =   is_training
       UpperCamelCase__							:       Optional[int]      =   use_labels
       UpperCamelCase__							:       List[Any]      =   vocab_size
       UpperCamelCase__							:       Union[str, Any]      =   hidden_size
       UpperCamelCase__							:       int      =   num_hidden_layers
       UpperCamelCase__							:       Dict      =   num_attention_heads
       UpperCamelCase__							:       Union[str, Any]      =   intermediate_size
       UpperCamelCase__							:       Optional[Any]      =   hidden_act
       UpperCamelCase__							:       Any      =   hidden_dropout_prob
       UpperCamelCase__							:       List[str]      =   attention_probs_dropout_prob
       UpperCamelCase__							:       Optional[Any]      =   max_position_embeddings
       UpperCamelCase__							:       Union[str, Any]      =   eos_token_id
       UpperCamelCase__							:       Optional[Any]      =   pad_token_id
       UpperCamelCase__							:       Tuple      =   bos_token_id
       UpperCamelCase__							:       Optional[int]      =   initializer_range




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1],	self.vocab_size							),	3,	self.vocab_size							)
       UpperCamelCase__							:       Optional[int]      =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),	dtype=np.intaa							)),	-1							)

       UpperCamelCase__							:       Any      =   shift_tokens_right(_lowerCamelCase,	1,	2							)

       UpperCamelCase__							:       Union[str, Any]      =   BlenderbotConfig(
           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,	)
       UpperCamelCase__							:       List[Any]      =   prepare_blenderbot_inputs_dict(_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase							)
       return config, inputs_dict




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.prepare_config_and_inputs()
       return config, inputs_dict




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   20
       UpperCamelCase__							:       Union[str, Any]      =   model_class_name(_lowerCamelCase							)

       UpperCamelCase__							:       List[str]      =   model.encode(inputs_dict['''input_ids''']							)

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

       UpperCamelCase__							:       List[str]      =   model.init_cache(decoder_input_ids.shape[0],	_lowerCamelCase,	_lowerCamelCase							)
       UpperCamelCase__							:       str      =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length),	dtype='''i4'''							)

       UpperCamelCase__							:       str      =   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :],	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),	)
       UpperCamelCase__							:       Any      =   model.decode(
           decoder_input_ids[:, :-1],	_lowerCamelCase,	decoder_attention_mask=_lowerCamelCase,	past_key_values=_lowerCamelCase,	decoder_position_ids=_lowerCamelCase,	)

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

       UpperCamelCase__							:       Dict      =   model.decode(_lowerCamelCase,	_lowerCamelCase							)

       UpperCamelCase__							:       List[Any]      =   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,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   20
       UpperCamelCase__							:       List[Any]      =   model_class_name(_lowerCamelCase							)

       UpperCamelCase__							:       Union[str, Any]      =   model.encode(inputs_dict['''input_ids''']							)

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

       UpperCamelCase__							:       int      =   jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
           ],	axis=-1,	)

       UpperCamelCase__							:       List[Any]      =   model.init_cache(decoder_input_ids.shape[0],	_lowerCamelCase,	_lowerCamelCase							)
       UpperCamelCase__							:       Optional[Any]      =   jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :],	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),	)

       UpperCamelCase__							:       Optional[int]      =   model.decode(
           decoder_input_ids[:, :-1],	_lowerCamelCase,	decoder_attention_mask=_lowerCamelCase,	past_key_values=_lowerCamelCase,	decoder_position_ids=_lowerCamelCase,	)
       UpperCamelCase__							:       Dict      =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],	dtype='''i4'''							)
       UpperCamelCase__							:       Tuple      =   model.decode(
           decoder_input_ids[:, -1:],	_lowerCamelCase,	past_key_values=outputs_cache.past_key_values,	decoder_attention_mask=_lowerCamelCase,	decoder_position_ids=_lowerCamelCase,	)

       UpperCamelCase__							:       List[Any]      =   model.decode(_lowerCamelCase,	_lowerCamelCase,	decoder_attention_mask=_lowerCamelCase							)

       UpperCamelCase__							:       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__ (			unittest.TestCase       ):

   '''simple docstring'''


   a		:							str     	=	99




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   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,	)

       UpperCamelCase__							:       str      =   input_ids.shape[0]
       UpperCamelCase__							:       int      =   BlenderbotConfig(
           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							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       str      =   self._get_config_and_data()
       UpperCamelCase__							:       int      =   FlaxBlenderbotForConditionalGeneration(_lowerCamelCase							)
       UpperCamelCase__							:       Union[str, Any]      =   lm_model(input_ids=_lowerCamelCase							)
       UpperCamelCase__							:       Union[str, Any]      =   (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,	_lowerCamelCase							)




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   BlenderbotConfig(
           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,	)
       UpperCamelCase__							:       Union[str, Any]      =   FlaxBlenderbotForConditionalGeneration(_lowerCamelCase							)
       UpperCamelCase__							:       Any      =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],	dtype=np.intaa							)
       UpperCamelCase__							:       Optional[int]      =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],	dtype=np.intaa							)
       UpperCamelCase__							:       Optional[int]      =   lm_model(input_ids=_lowerCamelCase,	decoder_input_ids=_lowerCamelCase							)
       UpperCamelCase__							:       int      =   (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,	_lowerCamelCase							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       int      =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],	dtype=np.intaa							)
       UpperCamelCase__							:       List[str]      =   shift_tokens_right(_lowerCamelCase,	1,	2							)
       UpperCamelCase__							:       Any      =   np.equal(_lowerCamelCase,	1							).astype(np.floataa							).sum()
       UpperCamelCase__							:       Union[str, Any]      =   np.equal(_lowerCamelCase,	1							).astype(np.floataa							).sum()
       self.assertEqual(shifted.shape,	input_ids.shape							)
       self.assertEqual(_lowerCamelCase,	n_pad_before - 1							)
       self.assertTrue(np.equal(shifted[:, 0],	2							).all()							)






@require_flax
class 					lowercase__ (			__UpperCAmelCase    ,       unittest.TestCase    ,       __UpperCAmelCase       ):

   '''simple docstring'''


   a		:							Tuple     	=	True
   a		:							Optional[int]     	=	(
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   a		:							str     	=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   FlaxBlenderbotModelTester(self							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   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    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       List[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    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:       int      =   self._prepare_for_class(_lowerCamelCase,	_lowerCamelCase							)
               UpperCamelCase__							:       Any      =   model_class(_lowerCamelCase							)

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

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:       Optional[int]      =   encode_jitted(**_lowerCamelCase							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:       str      =   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    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:       Dict      =   model_class(_lowerCamelCase							)
               UpperCamelCase__							:       Optional[Any]      =   model.encode(inputs_dict['''input_ids'''],	inputs_dict['''attention_mask''']							)

               UpperCamelCase__							:       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(__magic_name__,	__magic_name__,	__magic_name__							):
                   return model.decode(
                       decoder_input_ids=_lowerCamelCase,	decoder_attention_mask=_lowerCamelCase,	encoder_outputs=_lowerCamelCase,	)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:       str      =   decode_jitted(**_lowerCamelCase							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:       int      =   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    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:       Any      =   model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''							)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           UpperCamelCase__							:       Tuple      =   np.ones((1, 1)							) * model.config.eos_token_id
           UpperCamelCase__							:       Union[str, Any]      =   model(_lowerCamelCase							)
           self.assertIsNotNone(_lowerCamelCase							)




   @unittest.skipUnless(jax_device != '''cpu''',	'''3B test too slow on CPU.'''							)
   @slow
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
       UpperCamelCase__							:       str      =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

       UpperCamelCase__							:       Dict      =   FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',	from_pt=_lowerCamelCase							)
       UpperCamelCase__							:       Union[str, Any]      =   BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''							)

       UpperCamelCase__							:       List[str]      =   ['''Sam''']
       UpperCamelCase__							:       Optional[Any]      =   tokenizer(_lowerCamelCase,	return_tensors='''jax'''							)

       UpperCamelCase__							:       str      =   model.generate(**_lowerCamelCase,	**_lowerCamelCase							)
       UpperCamelCase__							:       Dict      =   '''Sam is a great name. It means "sun" in Gaelic.'''

       UpperCamelCase__							:       str      =   tokenizer.batch_decode(_lowerCamelCase,	**_lowerCamelCase							)
       assert generated_txt[0].strip() == tgt_text








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

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


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[int]     	=	"marian"
   a		:							Optional[Any]     	=	["past_key_values"]
   a		:							Union[str, Any]     	=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self,	__magic_name__=58101,	__magic_name__=None,	__magic_name__=1024,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=True,	__magic_name__=True,	__magic_name__="gelu",	__magic_name__=1024,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.02,	__magic_name__=58100,	__magic_name__=False,	__magic_name__=58100,	__magic_name__=0,	__magic_name__=0,	__magic_name__=True,	**__magic_name__,	)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Dict      =   decoder_vocab_size or vocab_size
       UpperCamelCase__							:       Any      =   max_position_embeddings
       UpperCamelCase__							:       Any      =   d_model
       UpperCamelCase__							:       Union[str, Any]      =   encoder_ffn_dim
       UpperCamelCase__							:       Optional[Any]      =   encoder_layers
       UpperCamelCase__							:       List[str]      =   encoder_attention_heads
       UpperCamelCase__							:       str      =   decoder_ffn_dim
       UpperCamelCase__							:       str      =   decoder_layers
       UpperCamelCase__							:       Optional[int]      =   decoder_attention_heads
       UpperCamelCase__							:       str      =   dropout
       UpperCamelCase__							:       Optional[Any]      =   attention_dropout
       UpperCamelCase__							:       List[Any]      =   activation_dropout
       UpperCamelCase__							:       int      =   activation_function
       UpperCamelCase__							:       Optional[int]      =   init_std
       UpperCamelCase__							:       Any      =   encoder_layerdrop
       UpperCamelCase__							:       Tuple      =   decoder_layerdrop
       UpperCamelCase__							:       Dict      =   use_cache
       UpperCamelCase__							:       str      =   encoder_layers
       UpperCamelCase__							:       str      =   scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__							:       Union[str, Any]      =   share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__magic_name__,	eos_token_id=__magic_name__,	is_encoder_decoder=__magic_name__,	decoder_start_token_id=__magic_name__,	forced_eos_token_id=__magic_name__,	**__magic_name__,	)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


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






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       str      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)

           if self.use_past:
               UpperCamelCase__							:       str      =   {0: '''batch'''}
               UpperCamelCase__							:       List[str]      =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}
               UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__magic_name__,	direction='''inputs'''							)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase__							:       Any      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       Tuple      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
           UpperCamelCase__							:       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    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       Optional[Any]      =   super().outputs
       else:
           UpperCamelCase__							:       Tuple      =   super(__magic_name__,	self							).outputs
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       str      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




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






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       # Generate decoder inputs
       UpperCamelCase__							:       Optional[Any]      =   seq_length if not self.use_past else 1
       UpperCamelCase__							:       List[str]      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       UpperCamelCase__							:       Dict      =   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__							:       Dict      =   dict(**__magic_name__,	**__magic_name__							)

       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
           else:
               import torch
           UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   common_inputs['''input_ids'''].shape
           UpperCamelCase__							:       Optional[int]      =   common_inputs['''decoder_input_ids'''].shape[1]
           UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.num_attention_heads
           UpperCamelCase__							:       Tuple      =   (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase__							:       Optional[int]      =   decoder_seq_length + 3
           UpperCamelCase__							:       Dict      =   (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           UpperCamelCase__							:       List[str]      =   torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__,	__magic_name__							)],	dim=1							)

           UpperCamelCase__							:       Any      =   []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self.num_layers
           UpperCamelCase__							:       Union[str, Any]      =   min(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   max(__magic_name__,	__magic_name__							) - min_num_layers
           UpperCamelCase__							:       str      =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

           for _ in range(__magic_name__							):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                   )							)
           # TODO: test this.
           UpperCamelCase__							:       List[Any]      =   encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
           for _ in range(__magic_name__,	__magic_name__							):
               common_inputs["past_key_values"].append((torch.zeros(__magic_name__							), torch.zeros(__magic_name__							))							)
       return common_inputs




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






       """simple docstring"""
       UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
           else:
               import torch
           UpperCamelCase__     ,UpperCamelCase__							:       Union[str, Any]      =   common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           UpperCamelCase__							:       Dict      =   seqlen + 2
           UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.num_layers
           UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.num_attention_heads
           UpperCamelCase__							:       Tuple      =   (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           UpperCamelCase__							:       int      =   common_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:       Union[str, Any]      =   torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(__magic_name__,	__magic_name__,	dtype=__magic_name__							)],	dim=1							)
           UpperCamelCase__							:       Union[str, Any]      =   [
               (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(__magic_name__							)
           ]
       return common_inputs




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






       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__							:       Any      =   compute_effective_axis_dimension(
           __magic_name__,	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
       UpperCamelCase__							:       Optional[int]      =   tokenizer.num_special_tokens_to_add(__magic_name__							)
       UpperCamelCase__							:       Dict      =   compute_effective_axis_dimension(
           __magic_name__,	fixed_dimension=OnnxConfig.default_fixed_sequence,	num_token_to_add=__magic_name__							)

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__							:       Dict      =   [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
       UpperCamelCase__							:       Optional[Any]      =   dict(tokenizer(__magic_name__,	return_tensors=__magic_name__							)							)
       return common_inputs




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






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       else:
           UpperCamelCase__							:       Optional[Any]      =   self._generate_dummy_inputs_for_causal_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       List[str]      =   super()._flatten_past_key_values_(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       else:
           UpperCamelCase__							:       Dict      =   super(__magic_name__,	self							)._flatten_past_key_values_(
               __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   float:






       """simple docstring"""
       return 1E-4

