




'''simple docstring'''





import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
           RwkvForCausalLM,
           RwkvModel,
       )
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
       lowercase__								=						False






class     lowercase  :






      def __init__(self			,	a__			,	a__=1_4			,	a__=7			,	a__=True			,	a__=False			,	a__=True			,	a__=True			,	a__=True			,	a__=9_9			,	a__=3_2			,	a__=5			,	a__=3_7			,	a__="gelu"			,	a__=0.1			,	a__=0.1			,	a__=5_1_2			,	a__=1_6			,	a__=2			,	a__=3			,	a__=4			,	a__=None			,	)							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			parent
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			batch_size
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			seq_length
         SCREAMING_SNAKE_CASE__      :					int							    =			is_training
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			use_token_type_ids
         SCREAMING_SNAKE_CASE__      :					Dict							    =			use_input_mask
         SCREAMING_SNAKE_CASE__      :					Dict							    =			use_labels
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			use_mc_token_ids
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			vocab_size
         SCREAMING_SNAKE_CASE__      :					str							    =			hidden_size
         SCREAMING_SNAKE_CASE__      :					int							    =			num_hidden_layers
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			intermediate_size
         SCREAMING_SNAKE_CASE__      :					str							    =			hidden_act
         SCREAMING_SNAKE_CASE__      :					int							    =			hidden_dropout_prob
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__      :					int							    =			max_position_embeddings
         SCREAMING_SNAKE_CASE__      :					Any							    =			type_vocab_size
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			type_sequence_label_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			num_labels
         SCREAMING_SNAKE_CASE__      :					int							    =			num_choices
         SCREAMING_SNAKE_CASE__      :					Any							    =			scope
         SCREAMING_SNAKE_CASE__      :					Dict							    =			vocab_size - 1
         SCREAMING_SNAKE_CASE__      :					str							    =			vocab_size - 1
         SCREAMING_SNAKE_CASE__      :					int							    =			vocab_size - 1






      def  A__(self       )							-> Tuple:
         return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''       )






      def  A__(self			,	a__=False			,	a__=False			,	a__=False       )							-> int:
         SCREAMING_SNAKE_CASE__      :					str							    =			ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			None
         if self.use_input_mask:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			random_attention_mask([self.batch_size, self.seq_length]       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			None
         if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			ids_tensor([self.batch_size, self.seq_length]			,	self.type_vocab_size       )

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if self.use_mc_token_ids:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			ids_tensor([self.batch_size, self.num_choices]			,	self.seq_length       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			None
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			None
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			None
         if self.use_labels:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			ids_tensor([self.batch_size]			,	self.type_sequence_label_size       )
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			ids_tensor([self.batch_size, self.seq_length]			,	self.num_labels       )
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			ids_tensor([self.batch_size]			,	self.num_choices       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_config(
             gradient_checkpointing=a__			,	scale_attn_by_inverse_layer_idx=a__			,	reorder_and_upcast_attn=a__			,	)

         return (
             config,
             input_ids,
             input_mask,
             None,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )






      def  A__(self			,	a__=False			,	a__=False			,	a__=False       )							-> Tuple:
         return RwkvConfig(
             vocab_size=self.vocab_size			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	intermediate_size=self.intermediate_size			,	activation_function=self.hidden_act			,	resid_pdrop=self.hidden_dropout_prob			,	attn_pdrop=self.attention_probs_dropout_prob			,	n_positions=self.max_position_embeddings			,	type_vocab_size=self.type_vocab_size			,	use_cache=a__			,	bos_token_id=self.bos_token_id			,	eos_token_id=self.eos_token_id			,	pad_token_id=self.pad_token_id			,	gradient_checkpointing=a__			,	scale_attn_by_inverse_layer_idx=a__			,	reorder_and_upcast_attn=a__			,	)






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__      :					str							    =			self.get_config()
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			3_0_0
         return config






      def  A__(self       )							-> int:
         (
             (
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					
         )      :					List[Any]							    =			self.prepare_config_and_inputs()

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         SCREAMING_SNAKE_CASE__      :					int							    =			ids_tensor([self.batch_size, self.seq_length]			,	vocab_size=2       )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )






      def  A__(self			,	a__			,	a__			,	a__			,	a__			,	a__			,	*a__       )							-> str:
         SCREAMING_SNAKE_CASE__      :					Any							    =			True
         SCREAMING_SNAKE_CASE__      :					str							    =			RwkvModel(config=a__       )
         model.to(a__       )
         model.eval()

         SCREAMING_SNAKE_CASE__      :					int							    =			model(a__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(len(result.hidden_states       )			,	config.num_hidden_layers + 1       )






      def  A__(self			,	a__			,	a__			,	a__			,	a__			,	a__			,	*a__       )							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					str							    =			RwkvForCausalLM(a__       )
         model.to(a__       )
         model.eval()

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(a__			,	labels=a__       )
         self.parent.assertEqual(result.loss.shape			,	()       )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size)       )






      def  A__(self			,	a__			,	a__			,	a__			,	a__			,	a__			,	*a__       )							-> int:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			RwkvModel(config=a__       )
         model.to(a__       )
         model.eval()

         SCREAMING_SNAKE_CASE__      :					int							    =			model(a__       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			outputs.last_hidden_state

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(input_ids[:, :2]       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			outputs.last_hidden_state

         # Using the state computed on the first inputs, we will get the same output
         SCREAMING_SNAKE_CASE__      :					int							    =			model(input_ids[:, 2:]			,	state=outputs.state       )
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			outputs.last_hidden_state

         self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]			,	dim=1       )			,	a__			,	atol=1E-5       )       )






      def  A__(self			,	a__			,	a__			,	a__			,	a__			,	a__			,	*a__			,	a__=False       )							-> str:
         SCREAMING_SNAKE_CASE__      :					int							    =			RwkvForCausalLM(a__       )
         model.to(a__       )
         if gradient_checkpointing:
            model.gradient_checkpointing_enable()

         SCREAMING_SNAKE_CASE__      :					Any							    =			model(a__			,	labels=a__       )
         self.parent.assertEqual(result.loss.shape			,	()       )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size)       )
         result.loss.backward()






      def  A__(self       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.prepare_config_and_inputs()

         (
             (
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					(
             SCREAMING_SNAKE_CASE__
         )    ,					
         )      :					List[str]							    =			config_and_inputs

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{'''input_ids''': input_ids}

         return config, inputs_dict






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0					,      reason='''See https://github.com/huggingface/transformers/pull/24204''')
@require_torch
class     lowercase  ( UpperCAmelCase_					,      UpperCAmelCase_					,      UpperCAmelCase_					,      unittest.TestCase):
      __lowercase					=	(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
      __lowercase					=	(
          {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
      )
      # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
      __lowercase					=	False
      __lowercase					=	False
      __lowercase					=	False
      __lowercase					=	False
      __lowercase					=	False  # Rwkv does not support head masking






      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			RwkvModelTester(self       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			ConfigTester(
             self			,	config_class=a__			,	n_embd=3_7			,	common_properties=['''hidden_size''', '''num_hidden_layers''']       )






      def  A__(self			,	a__			,	a__			,	a__=None       )							-> Tuple:
         if isinstance(a__			,	torch.Tensor       ):
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					int							    =			member.max().item(), member.min().item()
         elif isinstance(a__			,	a__       ) or isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			max(a__       ), min(a__       )

         if not isinstance(a__			,	a__       ):
            raise TypeError('''container should be a list or tuple'''       )
         elif len(a__       ) != 2:
            raise ValueError('''container should have 2 elements'''       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			container

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(min_value >= expected_min) and (max_value <= expected_max)

         if not is_inside_interval:
            SCREAMING_SNAKE_CASE__      :					str							    =			'''%s not found in %s''' % (safe_repr(a__       ), safe_repr(a__       ))
            self.fail(self._formatMessage(a__			,	a__       )       )






      def  A__(self       )							-> Optional[Any]:
         self.config_tester.run_common_tests()






      def  A__(self       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_rwkv_model(*a__       )






      def  A__(self       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					str							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_causl_lm(*a__       )






      def  A__(self       )							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_state_equivalency(*a__       )






      def  A__(self       )							-> int:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__      :					int							    =			model_class(config=a__       )
            for name, param in model.named_parameters():
               if "time_decay" in name:
                  if param.requires_grad:
                     self.assertTrue(param.data.max().item() == 3.0       )
                     self.assertTrue(param.data.min().item() == -5.0       )
               elif "time_first" in name:
                  if param.requires_grad:
                     # check if it's a ones like
                     self.assertTrue(torch.allclose(param.data			,	torch.ones_like(param.data       )			,	atol=1E-5			,	rtol=1E-5       )       )
               elif any(x in name for x in ['''time_mix_key''', '''time_mix_receptance''']       ):
                  if param.requires_grad:
                     self.assertInterval(
                         param.data			,	[0.0, 1.0]			,	msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,	)
               elif "time_mix_value" in name:
                  if param.requires_grad:
                     self.assertInterval(
                         param.data			,	[0.0, 1.3]			,	msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,	)






      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			True

         SCREAMING_SNAKE_CASE__      :					str							    =			getattr(self.model_tester			,	'''seq_length'''			,	a__       )

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			True
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			False
            SCREAMING_SNAKE_CASE__      :					Any							    =			True
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			model_class(a__       )
            model.to(a__       )
            model.eval()

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self._prepare_for_class(a__			,	a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			inputs['''input_ids'''].shape[0]
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			model(**a__       )
            SCREAMING_SNAKE_CASE__      :					Dict							    =			outputs.attentions
            self.assertEqual(len(a__       )			,	self.model_tester.num_hidden_layers       )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			True
            SCREAMING_SNAKE_CASE__      :					int							    =			model_class(a__       )
            model.to(a__       )
            model.eval()

            SCREAMING_SNAKE_CASE__      :					int							    =			self._prepare_for_class(a__			,	a__       )
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			inputs['''input_ids'''].shape[0]
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			model(**a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			outputs.attentions
            self.assertEqual(len(a__       )			,	self.model_tester.num_hidden_layers       )

            self.assertListEqual(
                list(attentions[0].shape[-3:]       )			,	[batch_size, seq_len, config.hidden_size]			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			len(a__       )

            # Check attention is always last and order is fine
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			True
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			True
            SCREAMING_SNAKE_CASE__      :					Dict							    =			model_class(a__       )
            model.to(a__       )
            model.eval()

            SCREAMING_SNAKE_CASE__      :					Any							    =			self._prepare_for_class(a__			,	a__       )
            SCREAMING_SNAKE_CASE__      :					str							    =			inputs['''input_ids'''].shape[0]
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					List[Any]							    =			model(**a__       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			1
            self.assertEqual(out_len + added_hidden_states			,	len(a__       )       )

            SCREAMING_SNAKE_CASE__      :					int							    =			outputs.attentions

            self.assertEqual(len(a__       )			,	self.model_tester.num_hidden_layers       )
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]       )			,	[batch_size, seq_len, config.hidden_size]			,	)






      @slow
      def  A__(self       )							-> Dict:
         for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			RwkvModel.from_pretrained(a__       )
            self.assertIsNotNone(a__       )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0					,      reason='''See https://github.com/huggingface/transformers/pull/24204''')
@slow
class     lowercase  ( unittest.TestCase):






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			'''RWKV/rwkv-4-169m-pile'''
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			AutoTokenizer.from_pretrained(self.model_id       )






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			'''Hello my name is Jasmine and I am a newbie to the'''
         SCREAMING_SNAKE_CASE__      :					Dict							    =			RwkvForCausalLM.from_pretrained(self.model_id       ).to(a__       )

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer('''Hello my name is'''			,	return_tensors='''pt'''       ).input_ids.to(a__       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model.generate(a__			,	max_new_tokens=1_0       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.tokenizer.decode(output[0].tolist()       )

         self.assertEqual(a__			,	a__       )






      def  A__(self       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					str							    =			'''Hello my name is Jasmine and I am a newbie to the'''

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.tokenizer('''Hello my name is'''			,	return_tensors='''pt'''       ).input_ids.to(a__       )
         SCREAMING_SNAKE_CASE__      :					int							    =			RwkvForCausalLM.from_pretrained(self.model_id			,	torch_dtype=torch.bfloataa       ).to(a__       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			model.generate(a__			,	max_new_tokens=1_0       )
         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer.decode(output[0].tolist()       )

         self.assertEqual(a__			,	a__       )







'''simple docstring'''





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase__								=						logging.get_logger(__name__)






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''mt5'''
      __lowercase					=	['''past_key_values''']






      def __init__(self			,	a__=2_5_0_1_1_2			,	a__=5_1_2			,	a__=6_4			,	a__=1_0_2_4			,	a__=8			,	a__=None			,	a__=6			,	a__=3_2			,	a__=1_2_8			,	a__=0.1			,	a__=1E-6			,	a__=1.0			,	a__="gated-gelu"			,	a__=True			,	a__=True			,	a__="T5Tokenizer"			,	a__=False			,	a__=0			,	a__=1			,	a__=0			,	**a__			,	)							-> List[Any]:
         super().__init__(
             is_encoder_decoder=a__			,	tokenizer_class=a__			,	tie_word_embeddings=a__			,	pad_token_id=a__			,	eos_token_id=a__			,	decoder_start_token_id=a__			,	**a__			,	)
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			vocab_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			d_model
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			d_kv
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			d_ff
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			num_layers
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         SCREAMING_SNAKE_CASE__      :					Any							    =			num_heads
         SCREAMING_SNAKE_CASE__      :					Any							    =			relative_attention_num_buckets
         SCREAMING_SNAKE_CASE__      :					Any							    =			relative_attention_max_distance
         SCREAMING_SNAKE_CASE__      :					Any							    =			dropout_rate
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			layer_norm_epsilon
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			initializer_factor
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			feed_forward_proj
         SCREAMING_SNAKE_CASE__      :					Any							    =			use_cache

         SCREAMING_SNAKE_CASE__      :					int							    =			self.feed_forward_proj.split('''-'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			act_info[-1]
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			act_info[0] == '''gated'''

         if len(a__       ) > 1 and act_info[0] != "gated" or len(a__       ) > 2:
            raise ValueError(
                f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                '''\'gated-gelu\' or \'relu\''''       )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
            SCREAMING_SNAKE_CASE__      :					int							    =			'''gelu_new'''






      @property
      def  A__(self       )							-> Dict:
         return self.d_model






      @property
      def  A__(self       )							-> Tuple:
         return self.num_heads






      @property
      def  A__(self       )							-> List[str]:
         return self.num_layers






class     lowercase  ( UpperCAmelCase_):






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			'''past_encoder_sequence + sequence'''
            SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch'''}
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            SCREAMING_SNAKE_CASE__      :					int							    =			{0: '''batch''', 1: '''decoder_sequence'''}
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
            self.fill_with_past_key_values_(a__			,	direction='''inputs'''       )

         return common_inputs






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def  A__(self       )							-> int:
         return 1_3






      @property
      def  A__(self       )							-> float:
         return 5E-4

