


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		int:
       if isinstance(a_					,		np.ndarray  ):
              return list(tensor.shape  )

       UpperCamelCase_          =  tf.shape(a_  )

       if tensor.shape == tf.TensorShape(a_  ):
              return dynamic

       UpperCamelCase_          =  tensor.shape.as_list()

       return [dynamic[i] if s is None else s for i, s in enumerate(a_  )]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None  )     ->		Tuple:
       return tf.nn.softmax(logits=logits + 1e-9					,		axis=a_					,		name=a_  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=1e-5					,		UpperCamelCase_=-1  )     ->		Optional[int]:
       # This is a very simplified functional layernorm, designed to duplicate
       # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
       # models in Transformers.

       if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a_					,		a_  ):
              raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."  )

       # Get mean and variance on the axis to be normalized
       UpperCamelCase_          =  tf.nn.moments(a_					,		axes=[axis]					,		keepdims=a_  )

       if axis != -1:
              # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
              # on every dimension except axis
              UpperCamelCase_          =  [1] * inputs.shape.rank
              UpperCamelCase_          =  shape_list(a_  )[axis]
              UpperCamelCase_          =  tf.reshape(a_					,		a_  )
              UpperCamelCase_          =  tf.reshape(a_					,		a_  )

       # Compute layer normalization using the batch_normalization
       # function.
       UpperCamelCase_          =  tf.nn.batch_normalization(
           a_					,		a_					,		a_					,		offset=a_					,		scale=a_					,		variance_epsilon=a_					,		)
       return outputs




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=0					,		UpperCamelCase_=-1  )     ->		List[str]:
       # Replicates the behavior of torch.flatten in TF

       # If end_dim or start_dim is negative, count them from the end
       if end_dim < 0:
              end_dim += input.shape.rank
       if start_dim < 0:
              start_dim += input.shape.rank

       if start_dim == end_dim:
              return input

       UpperCamelCase_          =  tf.shape(a_  )
       UpperCamelCase_          =  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]  )
       UpperCamelCase_          =  tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]					,		axis=0  )
       return tf.reshape(a_					,		a_  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[str]:
       if not isinstance(a_					,		tf.Tensor  ):
              UpperCamelCase_          =  tf.convert_to_tensor(a_  )  # Catches stray NumPy inputs
       if encoder_attention_mask.shape.rank == 3:
              UpperCamelCase_          =  encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.shape.rank == 2:
              UpperCamelCase_          =  encoder_attention_mask[:, None, None, :]
       # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
       # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
       # /transformer/transformer_layers.py#L270
       # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
       # encoder_extended_attention_mask.transpose(-1, -2))
       UpperCamelCase_          =  (
           tf.cast(1					,		encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
       ) * encoder_extended_attention_mask.dtype.min

       return encoder_extended_attention_mask




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = "input_ids"  )     ->		List[Any]:
       tf.debugging.assert_less(
           a_					,		tf.cast(a_					,		dtype=tensor.dtype  )					,		message=(
               F'''The maximum value of {tensor_name} ({tf.math.reduce_max(a_  )}) must be smaller than the embedding '''
               F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
           )					,		)




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Dict:
       UpperCamelCase_          =  64512
       # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
       # because in that case even chunking the array would not make the saving
       # possible.
       UpperCamelCase_          =  [x for x in data if len(a_  ) > HDF5_OBJECT_HEADER_LIMIT]

       # Expecting this to never be true.
       if bad_attributes:
              raise RuntimeError(
                  "The following attributes cannot be saved to HDF5 file because "
                  F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
                  F'''bytes: {bad_attributes}'''  )

       UpperCamelCase_          =  np.asarray(a_  )

       UpperCamelCase_          =  1
       UpperCamelCase_          =  np.array_split(a_					,		a_  )

       # This will never loop forever thanks to the test above.
       while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data  ):
              num_chunks += 1
              UpperCamelCase_          =  np.array_split(a_					,		a_  )

       if num_chunks > 1:
              for chunk_id, chunk_data in enumerate(a_  ):
                     UpperCamelCase_          =  chunk_data
       else:
              UpperCamelCase_          =  data




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       if name in group.attrs:
              UpperCamelCase_          =  [n.decode("utf8"  ) if hasattr(a_					,		"decode"  ) else n for n in group.attrs[name]]
       else:
              UpperCamelCase_          =  []
              UpperCamelCase_          =  0
              while "%s%d" % (name, chunk_id) in group.attrs:
                     data.extend(
                         [n.decode("utf8"  ) if hasattr(a_					,		"decode"  ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]  )
                     chunk_id += 1
       return data







def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:

       def _expand_single_ad_tensor(UpperCamelCase_  ):
              if isinstance(a_					,		tf.Tensor  ) and t.shape.rank == 1:
                     return tf.expand_dims(a_					,		axis=-1  )
              return t

       return tf.nest.map_structure(_expand_single_ad_tensor					,		a_  )








import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

