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
				lowerCAmelCase__	:  Optional[Any]						=  '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__		(							A__     ,					A__     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					)  ->   int:
				if attention_mask is None:
								snake_case__   :      Tuple   				= np.where(input_ids != config.pad_token_id     ,					1     ,					0       )
				if decoder_attention_mask is None:
								snake_case__   :      List[Any]   				= np.where(decoder_input_ids != config.pad_token_id     ,					1     ,					0       )
				if head_mask is None:
								snake_case__   :      List[str]   				= 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__   :      Dict   				= 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 					__snake_case    :




					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=0.0_2       ,			)	->    List[Any]:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







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

									snake_case__   :      List[str]   				= shift_tokens_right(UpperCAmelCase_       ,			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=UpperCAmelCase_       ,			)
									snake_case__   :      Any   				= prepare_marian_inputs_dict(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_						)
									return config, inputs_dict




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      str   				= self.prepare_config_and_inputs()
									return config, inputs_dict




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 20
									snake_case__   :      Any   				= model_class_name(UpperCAmelCase_						)

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

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

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

									snake_case__   :      Optional[int]   				= 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__   :      int   				= model.decode(
									    decoder_input_ids[:, :-1]       ,			UpperCAmelCase_       ,			decoder_attention_mask=UpperCAmelCase_       ,			past_key_values=UpperCAmelCase_       ,			decoder_position_ids=UpperCAmelCase_       ,			)

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

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

									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}"""						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= 20
									snake_case__   :      Optional[int]   				= model_class_name(UpperCAmelCase_						)

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

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

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

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

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

									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}"""						)





@require_flax
class 					__snake_case    (     snake_case__       ,unittest.TestCase       ,snake_case__      ):
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					__lowerCamelCase			   =  (FlaxMarianMTModel,) if is_flax_available() else ()




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Dict   				= FlaxMarianModelTester(self						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									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(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      List[Any]   				= 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      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= 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__   :      Tuple   				= self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_						)
																	snake_case__   :      Optional[int]   				= 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'						):
																					snake_case__   :      Optional[Any]   				= encode_jitted(**UpperCAmelCase_						).to_tuple()

																	with self.subTest('JIT Disabled'						):
																					with jax.disable_jit():
																									snake_case__   :      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      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									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__   :      str   				= model_class(UpperCAmelCase_						)
																	snake_case__   :      List[str]   				= model.encode(inputs_dict['input_ids']       ,			inputs_dict['attention_mask']						)

																	snake_case__   :      Tuple   				= {
																	    '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'						):
																					snake_case__   :      List[Any]   				= decode_jitted(**UpperCAmelCase_						).to_tuple()

																	with self.subTest('JIT Disabled'						):
																					with jax.disable_jit():
																									snake_case__   :      str   				= 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      __a     (  self						)	->    Dict:



									'''simple docstring'''







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





@require_flax
@require_sentencepiece
@require_tokenizers
class 					__snake_case    (     unittest.TestCase      ):
					__lowerCamelCase			   =  None
					__lowerCamelCase			   =  None




					@classmethod
					def      __a     (  cls						)	->    None:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls




					@cached_property
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									return MarianTokenizer.from_pretrained(self.model_name						)




					@property
					def      __a     (  self						)	->    int:



									'''simple docstring'''







									return self.tokenizer.eos_token_id




					@cached_property
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= FlaxMarianMTModel.from_pretrained(self.model_name						)
									self.assertEqual(model.config.decoder_start_token_id       ,			model.config.pad_token_id						)
									return model




					def      __a     (  self       ,			**__UpperCamelCase						)	->    str:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.translate_src_text(**UpperCAmelCase_						)
									self.assertListEqual(self.expected_text       ,			UpperCAmelCase_						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      int   				= self.tokenizer(self.src_text       ,			padding=UpperCAmelCase_       ,			return_tensors='np'       ,			**UpperCAmelCase_						)
									snake_case__   :      Optional[Any]   				= self.model.generate(
									    model_inputs.input_ids       ,			attention_mask=model_inputs.attention_mask       ,			num_beams=2       ,			max_length=128       ,			).sequences
									snake_case__   :      int   				= self.tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_						)
									return generated_words





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




					@slow
					def      __a     (  self						)	->    str:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





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




					@slow
					def      __a     (  self						)	->    int:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





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




					@slow
					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 					__snake_case    (     snake_case__      ):
					__lowerCamelCase			   =  """en"""
					__lowerCamelCase			   =  """de"""
					__lowerCamelCase			   =  [
					    """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.""",
					]
					__lowerCamelCase			   =  [
					    """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      __a     (  self						)	->    int:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





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




					@slow
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





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




					@slow
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()





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




					@slow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()






"""simple docstring"""
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


__SCREAMING_SNAKE_CASE				:     int		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     str		=     {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		str				   =     """bigbird_pegasus"""
							__lowercase:		Any				   =     ["""past_key_values"""]
							__lowercase:		Optional[Any]				   =     {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							}



							def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[Any]=96_103		,							UpperCAmelCase_				:   List[str]=4_096		,							UpperCAmelCase_				:   Optional[Any]=16		,							UpperCAmelCase_				:   str=4_096		,							UpperCAmelCase_				:   Optional[int]=16		,							UpperCAmelCase_				:   List[Any]=16		,							UpperCAmelCase_				:   Optional[int]=4_096		,							UpperCAmelCase_				:   List[str]=16		,							UpperCAmelCase_				:   Tuple=0.0		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Dict=True		,							UpperCAmelCase_				:   List[str]="gelu_new"		,							UpperCAmelCase_				:   Tuple=1_024		,							UpperCAmelCase_				:   int=0.1		,							UpperCAmelCase_				:   Union[str, Any]=0.0		,							UpperCAmelCase_				:   str=0.0		,							UpperCAmelCase_				:   str=0.02		,							UpperCAmelCase_				:   Tuple=2		,							UpperCAmelCase_				:   int=0.0		,							UpperCAmelCase_				:   str=True		,							UpperCAmelCase_				:   Any=0		,							UpperCAmelCase_				:   Dict=2		,							UpperCAmelCase_				:   List[str]=1		,							UpperCAmelCase_				:   Optional[int]="block_sparse"		,							UpperCAmelCase_				:   Tuple=64		,							UpperCAmelCase_				:   List[str]=3		,							UpperCAmelCase_				:   Optional[int]=False		,							**UpperCAmelCase_				:   str		,							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		vocab_size
														snake_case_     							=		max_position_embeddings
														snake_case_     							=		d_model
														snake_case_     							=		encoder_ffn_dim
														snake_case_     							=		encoder_layers
														snake_case_     							=		encoder_attention_heads
														snake_case_     							=		decoder_ffn_dim
														snake_case_     							=		decoder_layers
														snake_case_     							=		decoder_attention_heads
														snake_case_     							=		dropout
														snake_case_     							=		attention_dropout
														snake_case_     							=		activation_dropout
														snake_case_     							=		activation_function
														snake_case_     							=		init_std
														snake_case_     							=		encoder_layerdrop
														snake_case_     							=		decoder_layerdrop
														snake_case_     							=		classifier_dropout
														snake_case_     							=		use_cache
														snake_case_     							=		encoder_layers
														snake_case_     							=		scale_embedding  # scale factor will be sqrt(d_model) if True

														# extra config
														snake_case_     							=		attention_type
														snake_case_     							=		block_size
														snake_case_     							=		num_random_blocks
														snake_case_     							=		use_bias

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





class   __A  (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



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

																					if self.use_past:
																												snake_case_     							=		{0: """batch"""}
																												snake_case_     							=		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
																					else:
																												snake_case_     							=		{0: """batch""", 1: """decoder_sequence"""}
																												snake_case_     							=		{0: """batch""", 1: """decoder_sequence"""}

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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)

														# Generate decoder inputs
														snake_case_     							=		seq_length if not self.use_past else 1
														snake_case_     							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														snake_case_     							=		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
																					snake_case_							,    snake_case_     							=		common_inputs["""input_ids"""].shape
																					snake_case_     							=		common_inputs["""decoder_input_ids"""].shape[1]
																					snake_case_							,    snake_case_     							=		self.num_attention_heads
																					snake_case_     							=		(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					snake_case_     							=		decoder_seq_length + 3
																					snake_case_     							=		(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					snake_case_     							=		torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_							)]		,							dim=1							)

																					snake_case_     							=		[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					snake_case_							,    snake_case_     							=		self.num_layers
																					snake_case_     							=		min(UpperCAmelCase_		,							UpperCAmelCase_							)
																					snake_case_     							=		max(UpperCAmelCase_		,							UpperCAmelCase_							) - min_num_layers
																					snake_case_     							=		"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		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
																					snake_case_							,    snake_case_     							=		common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					snake_case_     							=		seqlen + 2
																					snake_case_							,    snake_case_     							=		self.num_layers
																					snake_case_							,    snake_case_     							=		self.num_attention_heads
																					snake_case_     							=		(
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					snake_case_     							=		common_inputs["""attention_mask"""].dtype
																					snake_case_     							=		torch.cat(
																					    [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_		,							UpperCAmelCase_		,							dtype=UpperCAmelCase_							)]		,							dim=1							)
																					snake_case_     							=		[
																					    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(UpperCAmelCase_							)
																					]
														return common_inputs



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														snake_case_     							=		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
														snake_case_     							=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_							)
														snake_case_     							=		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
														snake_case_     							=		[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
														snake_case_     							=		dict(tokenizer(UpperCAmelCase_		,							return_tensors=UpperCAmelCase_							)							)
														return common_inputs



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   PreTrainedTokenizer		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   int = -1		,							UpperCAmelCase_				:   bool = False		,							UpperCAmelCase_				:   Optional[TensorType] = None		,							)       ->Mapping[str, Any]:



														"""simple docstring"""



														if self.task in ["default", "seq2seq-lm"]:
																					snake_case_     							=		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":
																					snake_case_     							=		self._generate_dummy_inputs_for_causal_lm(
																					    UpperCAmelCase_		,							batch_size=UpperCAmelCase_		,							seq_length=UpperCAmelCase_		,							is_pair=UpperCAmelCase_		,							framework=UpperCAmelCase_							)
														else:
																					snake_case_     							=		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 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Any							)       ->Tuple:



														"""simple docstring"""



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



