'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

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

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


if is_flax_available():
      import os

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

      import jax
      import jax.numpy as jnp

      from transformers import AutoTokenizer
      from transformers.models.mbart.modeling_flax_mbart import (
          FlaxMBartForConditionalGeneration,
          FlaxMBartForQuestionAnswering,
          FlaxMBartForSequenceClassification,
          FlaxMBartModel,
          shift_tokens_right,
      )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Union[str, Any]				,	snake_case       :				Any				,	snake_case       :				str=None				,	snake_case       :				Tuple=None				,	snake_case       :				Union[str, Any]=None				,	snake_case       :				int=None				,	snake_case       :				Optional[int]=None				,	snake_case       :				List[str]=None				,	)   ->				List[str]:







     """simple docstring"""
     if attention_mask is None:
          a					:							List[Any]							= np.where(input_ids != config.pad_token_id				,	1				,	0  )
     if decoder_attention_mask is None:
          a					:							Tuple							= np.where(decoder_input_ids != config.pad_token_id				,	1				,	0  )
     if head_mask is None:
          a					:							str							= np.ones((config.encoder_layers, config.encoder_attention_heads)  )
     if decoder_head_mask is None:
          a					:							int							= np.ones((config.decoder_layers, config.decoder_attention_heads)  )
     if cross_attn_head_mask is None:
          a					:							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": decoder_attention_mask,
     }



class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    def __init__(					self     :						List[Any]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Optional[Any]=1_3							,							UpperCAmelCase_     :						int=7							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						str=False							,							UpperCAmelCase_     :						Optional[int]=9_9							,							UpperCAmelCase_     :						Tuple=1_6							,							UpperCAmelCase_     :						List[str]=2							,							UpperCAmelCase_     :						Optional[Any]=4							,							UpperCAmelCase_     :						List[str]=4							,							UpperCAmelCase_     :						List[Any]="gelu"							,							UpperCAmelCase_     :						Union[str, Any]=0.1							,							UpperCAmelCase_     :						Dict=0.1							,							UpperCAmelCase_     :						int=3_2							,							UpperCAmelCase_     :						List[Any]=2							,							UpperCAmelCase_     :						str=1							,							UpperCAmelCase_     :						Union[str, Any]=0							,							UpperCAmelCase_     :						Union[str, Any]=2							,							UpperCAmelCase_     :						int=0.02							,							):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







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

         a					:							Dict							= shift_tokens_right(UpperCAmelCase_							,							1)

         a					:							Any							= MBartConfig(
             vocab_size=self.vocab_size							,							d_model=self.hidden_size							,							encoder_layers=self.num_hidden_layers							,							decoder_layers=self.num_hidden_layers							,							encoder_attention_heads=self.num_attention_heads							,							decoder_attention_heads=self.num_attention_heads							,							encoder_ffn_dim=self.intermediate_size							,							decoder_ffn_dim=self.intermediate_size							,							dropout=self.hidden_dropout_prob							,							attention_dropout=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							eos_token_id=self.eos_token_id							,							bos_token_id=self.bos_token_id							,							pad_token_id=self.pad_token_id							,							decoder_start_token_id=self.decoder_start_token_id							,							initializer_range=self.initializer_range							,							use_cache=UpperCAmelCase_							,							)
         a					:							List[str]							= prepare_mbart_inputs_dict(UpperCAmelCase_							,							UpperCAmelCase_							,							UpperCAmelCase_)
         return config, inputs_dict





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a							,			a					:							Optional[Any]							= self.prepare_config_and_inputs()
         return config, inputs_dict





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[int]):


         """simple docstring"""







         a					:							Tuple							= 2_0
         a					:							Optional[Any]							= model_class_name(UpperCAmelCase_)

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

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

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

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

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

         a					:							str							= model.decode(UpperCAmelCase_							,							UpperCAmelCase_)

         a					:							Union[str, 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    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						Dict):


         """simple docstring"""







         a					:							int							= 2_0
         a					:							int							= model_class_name(UpperCAmelCase_)

         a					:							List[Any]							= model.encode(inputs_dict['input_ids'])

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

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

         a					:							Dict							= model.init_cache(decoder_input_ids.shape[0]							,							UpperCAmelCase_							,							UpperCAmelCase_)
         a					:							List[Any]							= jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]							,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,							)

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

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

         a					:							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   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			99





    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Dict							= np.array(
             [
                 [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                 [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                 [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                 [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                 [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                 [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                 [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                 [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                 [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                 [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                 [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                 [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                 [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
             ]							,							dtype=np.intaa							,							)

         a					:							Dict							= input_ids.shape[0]
         a					:							Optional[int]							= MBartConfig(
             vocab_size=self.vocab_size							,							d_model=2_4							,							encoder_layers=2							,							decoder_layers=2							,							encoder_attention_heads=2							,							decoder_attention_heads=2							,							encoder_ffn_dim=3_2							,							decoder_ffn_dim=3_2							,							max_position_embeddings=4_8							,							eos_token_id=2							,							pad_token_id=1							,							bos_token_id=0							,							)
         return config, input_ids, batch_size





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         a							,			a							,			a					:							Tuple							= self._get_config_and_data()
         a					:							Union[str, Any]							= FlaxMBartForSequenceClassification(UpperCAmelCase_)
         a					:							Tuple							= model(input_ids=UpperCAmelCase_							,							decoder_input_ids=UpperCAmelCase_)
         a					:							Tuple							= (batch_size, config.num_labels)
         self.assertEqual(outputs['logits'].shape							,							UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a							,			a							,			a					:							Union[str, Any]							= self._get_config_and_data()
         a					:							List[Any]							= FlaxMBartForQuestionAnswering(UpperCAmelCase_)
         a					:							Optional[Any]							= model(input_ids=UpperCAmelCase_)

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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a							,			a							,			a					:							List[Any]							= self._get_config_and_data()
         a					:							Optional[Any]							= FlaxMBartForConditionalGeneration(UpperCAmelCase_)
         a					:							Dict							= lm_model(input_ids=UpperCAmelCase_)
         a					:							List[str]							= (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape							,							UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a					:							List[str]							= MBartConfig(
             vocab_size=self.vocab_size							,							d_model=1_4							,							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=4_8							,							)
         a					:							Tuple							= FlaxMBartForConditionalGeneration(UpperCAmelCase_)
         a					:							Optional[Any]							= np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,							dtype=np.intaa)
         a					:							Tuple							= np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,							dtype=np.intaa)
         a					:							Union[str, Any]							= lm_model(input_ids=UpperCAmelCase_							,							decoder_input_ids=UpperCAmelCase_)
         a					:							Optional[Any]							= (*summary.shape, config.vocab_size)
         self.assertEqual(outputs['logits'].shape							,							UpperCAmelCase_)







    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							Any							= np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,							dtype=np.intaa)
         a					:							Optional[Any]							= shift_tokens_right(UpperCAmelCase_							,							1)
         a					:							List[str]							= np.equal(UpperCAmelCase_							,							1).astype(np.floataa).sum()
         a					:							Dict							= np.equal(UpperCAmelCase_							,							1).astype(np.floataa).sum()
         self.assertEqual(shifted.shape							,							input_ids.shape)
         self.assertEqual(UpperCAmelCase_							,							n_pad_before - 1)
         self.assertTrue(np.equal(shifted[:, 0]							,							2).all())






@require_flax
class   UpperCamelCase					(       a_    ,     unittest.TestCase    ,     a_       ):







    """simple docstring"""





    A     :    str				=			True
    A     :    Optional[int]				=			(
        (
            FlaxMBartModel,
            FlaxMBartForConditionalGeneration,
            FlaxMBartForSequenceClassification,
            FlaxMBartForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    A     :    List[Any]				=			(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							Dict							= FlaxMBartModelTester(self)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a							,			a					:							Optional[Any]							= 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    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a							,			a					:							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    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a							,			a					:							Any							= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                   with self.subTest('JIT Disabled'):
                        with jax.disable_jit():
                             a					:							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    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a							,			a					:							Union[str, Any]							= self.model_tester.prepare_config_and_inputs_for_common()

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

                   a					:							Optional[int]							= {
                       'decoder_input_ids': inputs_dict['decoder_input_ids'],
                       'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                       'encoder_outputs': encoder_outputs,
                   }

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

                   with self.subTest('JIT Enabled'):
                        a					:							Any							= decode_jitted(**UpperCAmelCase_).to_tuple()

                   with self.subTest('JIT Disabled'):
                        with jax.disable_jit():
                             a					:							List[Any]							= 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    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







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






@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    A     :    Optional[int]				=			[
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    A     :    Union[str, Any]				=			[
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    A     :    Any				=			"facebook/mbart-large-en-ro"





    @cached_property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.model_name)





    @cached_property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         a					:							Dict							= FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,							from_pt=UpperCAmelCase_)
         return model





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]							,							**UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Union[str, Any]							= self.translate_src_text(**UpperCAmelCase_)
         self.assertListEqual(self.expected_text							,							UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							**UpperCAmelCase_     :						Tuple):


         """simple docstring"""







         a					:							Union[str, Any]							= self.tokenizer(self.src_text							,							**UpperCAmelCase_							,							return_tensors='np')
         a					:							Tuple							= self.model.generate(
             model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']							,							early_stopping=UpperCAmelCase_							,							num_beams=2							,							).sequences
         a					:							Any							= self.tokenizer.batch_decode(UpperCAmelCase_							,							skip_special_tokens=UpperCAmelCase_)
         return generated_words







    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         self._assert_generated_batch_equal_expected()


'''simple docstring'''





from typing import Mapping

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


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Union[str, Any]				=			"longt5"
    A     :    List[str]				=			["past_key_values"]
    A     :    Dict				=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





    def __init__(					self     :						int							,							UpperCAmelCase_     :						Union[str, Any]=3_2_1_2_8							,							UpperCAmelCase_     :						List[Any]=5_1_2							,							UpperCAmelCase_     :						List[Any]=6_4							,							UpperCAmelCase_     :						List[str]=2_0_4_8							,							UpperCAmelCase_     :						List[str]=6							,							UpperCAmelCase_     :						Tuple=None							,							UpperCAmelCase_     :						Tuple=8							,							UpperCAmelCase_     :						str=1_2_7							,							UpperCAmelCase_     :						Dict=1_6							,							UpperCAmelCase_     :						Optional[int]=3_2							,							UpperCAmelCase_     :						Union[str, Any]=1_2_8							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						Optional[int]=1e-6							,							UpperCAmelCase_     :						Tuple=1.0							,							UpperCAmelCase_     :						str="relu"							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Dict="local"							,							UpperCAmelCase_     :						Optional[int]=True							,							UpperCAmelCase_     :						Dict=0							,							UpperCAmelCase_     :						Union[str, Any]=1							,							**UpperCAmelCase_     :						Any							,							):


         """simple docstring"""







         a					:							Optional[int]							= vocab_size
         a					:							Tuple							= d_model
         a					:							str							= d_kv
         a					:							str							= d_ff
         a					:							Union[str, Any]							= num_layers
         # default = symmetry
         a					:							Optional[Any]							= num_decoder_layers if num_decoder_layers is not None else self.num_layers
         a					:							List[Any]							= num_heads
         a					:							List[str]							= local_radius
         a					:							Optional[Any]							= global_block_size
         a					:							Optional[int]							= relative_attention_num_buckets
         a					:							List[Any]							= relative_attention_max_distance
         a					:							int							= dropout_rate
         a					:							List[str]							= layer_norm_epsilon
         a					:							Optional[Any]							= initializer_factor
         a					:							List[Any]							= feed_forward_proj
         a					:							Dict							= encoder_attention_type
         a					:							int							= use_cache

         a					:							Optional[Any]							= self.feed_forward_proj.split('-')
         a					:							List[Any]							= act_info[-1]
         a					:							str							= act_info[0] == 'gated'

         if len(UpperCAmelCase_) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_) > 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":
              a					:							Any							= 'gelu_new'

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






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







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

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

         return common_inputs







    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         return 1_3

