






from typing import Mapping

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


lowerCamelCase      :       Any    =						logging.get_logger(__name__)

lowerCamelCase      :       Optional[int]    =						{
    '''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      (a_      ):
      snake_case_	=				"""t5"""
      snake_case_	=				["""past_key_values"""]
      snake_case_	=				{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


      def __init__(						self       ,    __UpperCamelCase=3_2_1_2_8       ,    __UpperCamelCase=5_1_2       ,    __UpperCamelCase=6_4       ,    __UpperCamelCase=2_0_4_8       ,    __UpperCamelCase=6       ,    __UpperCamelCase=None       ,    __UpperCamelCase=8       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=1_2_8       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=1e-6       ,    __UpperCamelCase=1.0       ,    __UpperCamelCase="relu"       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase=0       ,    __UpperCamelCase=1       ,    **__UpperCamelCase       ,    )->						Any:
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       d_model
           __lowerCAmelCase             =       d_kv
           __lowerCAmelCase             =       d_ff
           __lowerCAmelCase             =       num_layers
           __lowerCAmelCase             =       (
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __lowerCAmelCase             =       num_heads
           __lowerCAmelCase             =       relative_attention_num_buckets
           __lowerCAmelCase             =       relative_attention_max_distance
           __lowerCAmelCase             =       dropout_rate
           __lowerCAmelCase             =       layer_norm_epsilon
           __lowerCAmelCase             =       initializer_factor
           __lowerCAmelCase             =       feed_forward_proj
           __lowerCAmelCase             =       use_cache

           __lowerCAmelCase             =       self.feed_forward_proj.split("-"						)
           __lowerCAmelCase             =       act_info[-1]
           __lowerCAmelCase             =       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":
                __lowerCAmelCase             =       "gelu_new"

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



class 				_UpperCamelCase      (a_      ):


      @property
      def 			__UpperCAmelCase		(						self						)->						Mapping[str, Mapping[int, str]]:
           __lowerCAmelCase             =       {
               "input_ids": {0: "batch", 1: "encoder_sequence"},
               "attention_mask": {0: "batch", 1: "encoder_sequence"},
           }
           if self.use_past:
                __lowerCAmelCase             =       "past_encoder_sequence + sequence"
                __lowerCAmelCase             =       {0: "batch"}
                __lowerCAmelCase             =       {0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
                __lowerCAmelCase             =       {0: "batch", 1: "decoder_sequence"}
                __lowerCAmelCase             =       {0: "batch", 1: "decoder_sequence"}

           if self.use_past:
                self.fill_with_past_key_values_(__UpperCamelCase       ,    direction="inputs"						)

           return common_inputs


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return 1_3














import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       r"\w+[.]\d+"
     __lowerCAmelCase             =       re.findall(__snake_case       ,			__snake_case							)
     for pat in pats:
          __lowerCAmelCase             =       key.replace(__snake_case       ,			"_".join(pat.split("."							)							)							)
     return key





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
     if (
         any("norm" in str_ for str_ in pt_tuple_key							)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("scale",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          __lowerCAmelCase             =       pt_tensor.transpose(2       ,			3       ,			1       ,			0							)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight":
          __lowerCAmelCase             =       pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("bias",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     __lowerCAmelCase             =       flax_model.init_weights(PRNGKey(__snake_case							)							)

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          __lowerCAmelCase             =       rename_key(__snake_case							)
          __lowerCAmelCase             =       tuple(renamed_pt_key.split("."							)							)

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)

        # also add unexpected weight so that warning is thrown
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






