



"""simple docstring"""







from typing import Mapping

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


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)

lowercase__		:				List[Any]          =	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """t5"""
      _SCREAMING_SNAKE_CASE  				=     ["""past_key_values"""]
      _SCREAMING_SNAKE_CASE  				=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



      def __init__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      int=3_2_1_2_8    ,			SCREAMING_SNAKE_CASE_				:      List[str]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Tuple=6_4    ,			SCREAMING_SNAKE_CASE_				:      Dict=2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=6    ,			SCREAMING_SNAKE_CASE_				:      Any=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=8    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=3_2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_2_8    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      str=1E-6    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=1.0    ,			SCREAMING_SNAKE_CASE_				:      Tuple="relu"    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      Dict=0    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=1    ,			**SCREAMING_SNAKE_CASE_				:      Tuple    ,			):
        lowerCAmelCase_   :				Any								= vocab_size
        lowerCAmelCase_   :				Union[str, Any]								= d_model
        lowerCAmelCase_   :				List[Any]								= d_kv
        lowerCAmelCase_   :				str								= d_ff
        lowerCAmelCase_   :				Union[str, Any]								= num_layers
        lowerCAmelCase_   :				Optional[int]								= (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowerCAmelCase_   :				Optional[int]								= num_heads
        lowerCAmelCase_   :				Tuple								= relative_attention_num_buckets
        lowerCAmelCase_   :				Optional[Any]								= relative_attention_max_distance
        lowerCAmelCase_   :				Union[str, Any]								= dropout_rate
        lowerCAmelCase_   :				Any								= layer_norm_epsilon
        lowerCAmelCase_   :				List[Any]								= initializer_factor
        lowerCAmelCase_   :				int								= feed_forward_proj
        lowerCAmelCase_   :				Optional[Any]								= use_cache

        lowerCAmelCase_   :				Tuple								= self.feed_forward_proj.split('-'       )
        lowerCAmelCase_   :				Union[str, Any]								= act_info[-1]
        lowerCAmelCase_   :				List[Any]								= act_info[0] == 'gated'

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

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







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Any								= {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
          lowerCAmelCase_   :				List[str]								= 'past_encoder_sequence + sequence'
          lowerCAmelCase_   :				List[str]								= {0: 'batch'}
          lowerCAmelCase_   :				Tuple								= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
          lowerCAmelCase_   :				int								= {0: 'batch', 1: 'decoder_sequence'}
          lowerCAmelCase_   :				Optional[Any]								= {0: 'batch', 1: 'decoder_sequence'}

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

        return common_inputs





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return 1_3










"""simple docstring"""







import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.eager import context
     from tensorflow.python.framework import ops

     from transformers import GradientAccumulator, create_optimizer







@require_tf
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			len(SCREAMING_SNAKE_CASE_       )       )
        for a, b in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
          self.assertAlmostEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			delta=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				int								= GradientAccumulator()
        accumulator([tf.constant([1.0, 2.0]       )]       )
        accumulator([tf.constant([-2.0, 1.0]       )]       )
        accumulator([tf.constant([-1.0, 2.0]       )]       )
        with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
          accumulator([tf.constant([1.0, 1.0]       ), tf.constant([2.0, 2.0]       )]       )
        self.assertEqual(accumulator.step    ,			3       )
        self.assertEqual(len(accumulator.gradients       )    ,			1       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[-2.0, 5.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()    ,			[0.0, 0.0]    ,			tol=1E-2       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= None
        ops.enable_eager_execution_internal()
        lowerCAmelCase_   :				str								= tf.config.list_physical_devices('CPU'       )
        if len(SCREAMING_SNAKE_CASE_       ) == 1:
          tf.config.set_logical_device_configuration(
              physical_devices[0]    ,			[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]       )
        lowerCAmelCase_   :				Dict								= tf.config.list_logical_devices(device_type='CPU'       )
        lowerCAmelCase_   :				Optional[int]								= tf.distribute.MirroredStrategy(devices=devices[:2]       )

        with strategy.scope():
          lowerCAmelCase_   :				Union[str, Any]								= GradientAccumulator()
          lowerCAmelCase_   :				int								= tf.Variable([4.0, 3.0]       )
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= create_optimizer(5E-5    ,			1_0    ,			5       )
          lowerCAmelCase_   :				Union[str, Any]								= tf.Variable([0.0, 0.0]    ,			trainable=SCREAMING_SNAKE_CASE_       )

        def accumulate_on_replica(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          accumulator([gradient]       )

        def apply_on_replica():
          optimizer.apply_gradients(list(zip(accumulator.gradients    ,			[variable]       )       )       )

        @tf.function
        def accumulate(SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
          with strategy.scope():
            lowerCAmelCase_   :				Tuple								= strategy.experimental_local_results(SCREAMING_SNAKE_CASE_       )
            local_variables[0].assign(SCREAMING_SNAKE_CASE_       )
            local_variables[1].assign(SCREAMING_SNAKE_CASE_       )
            strategy.run(SCREAMING_SNAKE_CASE_    ,			args=(gradient_placeholder,)       )

        @tf.function
        def apply_grad():
          with strategy.scope():
            strategy.run(SCREAMING_SNAKE_CASE_       )

        def _check_local_values(SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				List[Any]								= strategy.experimental_local_results(accumulator._gradients[0]       )
          self.assertListAlmostEqual(values[0].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )
          self.assertListAlmostEqual(values[1].value()    ,			SCREAMING_SNAKE_CASE_    ,			tol=1E-2       )

        accumulate([1.0, 2.0]    ,			[-1.0, 1.0]       )
        accumulate([3.0, -1.0]    ,			[-1.0, -1.0]       )
        accumulate([-2.0, 2.0]    ,			[3.0, -2.0]       )
        self.assertEqual(accumulator.step    ,			3       )
        _check_local_values([2.0, 3.0]    ,			[1.0, -2.0]       )
        apply_grad()
        self.assertListAlmostEqual(variable.value()    ,			[4.0, 3.0]    ,			tol=1E-2       )
        accumulator.reset()
        self.assertEqual(accumulator.step    ,			0       )
        _check_local_values([0.0, 0.0]    ,			[0.0, 0.0]       )





