


"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__snake_case         =					logging.get_logger(__name__)







def 						__lowerCAmelCase		(		lowercase						:						Union[tf.Tensor, np.ndarray]				)			->		List[int]:


    """simple docstring"""





    if isinstance(lowercase		,							np.ndarray				):
        return list(tensor.shape				)

    snake_case					:   Optional[Any]         =					tf.shape(lowercase				)

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

    snake_case					:   Any         =					tensor.shape.as_list()

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







def 						__lowerCAmelCase		(		lowercase						:						tf.Tensor		,							lowercase						:						Optional[int] = None		,							lowercase						:						Optional[str] = None				)			->		tf.Tensor:


    """simple docstring"""





    return tf.nn.softmax(logits=logits + 1e-9		,							axis=lowercase		,							name=lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						Union[str, Any]		,							lowercase						:						List[Any]		,							lowercase						:						Tuple=1e-5		,							lowercase						:						Dict=-1				)			->		Optional[Any]:


    """simple docstring"""





    if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowercase		,							lowercase				):
        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
    snake_case      ,snake_case					:   Any         =					tf.nn.moments(lowercase		,							axes=[axis]		,							keepdims=lowercase				)

    if axis != -1:
        # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
        # on every dimension except axis
        snake_case					:   int         =					[1] * inputs.shape.rank
        snake_case					:   Optional[Any]         =					shape_list(lowercase				)[axis]
        snake_case					:   Tuple         =					tf.reshape(lowercase		,							lowercase				)
        snake_case					:   List[str]         =					tf.reshape(lowercase		,							lowercase				)

    # Compute layer normalization using the batch_normalization
    # function.
    snake_case					:   int         =					tf.nn.batch_normalization(
        lowercase		,							lowercase		,							lowercase		,							offset=lowercase		,							scale=lowercase		,							variance_epsilon=lowercase		,							)
    return outputs







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						str=0		,							lowercase						:						List[Any]=-1				)			->		str:


    """simple docstring"""





    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

    snake_case					:   Any         =					tf.shape(lowercase				)
    snake_case					:   int         =					tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
    snake_case					:   Optional[Any]         =					tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]		,							axis=0				)
    return tf.reshape(lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						tf.Tensor				)			->		tf.Tensor:


    """simple docstring"""





    if not isinstance(lowercase		,							tf.Tensor				):
        snake_case					:   str         =					tf.convert_to_tensor(lowercase				)  # Catches stray NumPy inputs
    if encoder_attention_mask.shape.rank == 3:
        snake_case					:   Any         =					encoder_attention_mask[:, None, :, :]
    if encoder_attention_mask.shape.rank == 2:
        snake_case					:   List[str]         =					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))
    snake_case					:   Dict         =					(
        tf.cast(1		,							encoder_attention_mask.dtype				) - encoder_extended_attention_mask
    ) * encoder_extended_attention_mask.dtype.min

    return encoder_extended_attention_mask







def 						__lowerCAmelCase		(		lowercase						:						tf.Tensor		,							lowercase						:						int		,							lowercase						:						str = "input_ids"				)			->		None:


    """simple docstring"""





    tf.debugging.assert_less(
        lowercase		,							tf.cast(lowercase		,							dtype=tensor.dtype				)		,							message=(
            F'The maximum value of {tensor_name} ({tf.math.reduce_max(lowercase				)}) must be smaller than the embedding '
            F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
        )		,							)







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						int		,							lowercase						:						Union[str, Any]				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   List[str]         =					6_4512
    # 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.
    snake_case					:   Union[str, Any]         =					[x for x in data if len(lowercase				) > 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}'				)

    snake_case					:   int         =					np.asarray(lowercase				)

    snake_case					:   str         =					1
    snake_case					:   Any         =					np.array_split(lowercase		,							lowercase				)

    # 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
        snake_case					:   Tuple         =					np.array_split(lowercase		,							lowercase				)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(lowercase				):
            snake_case					:   Dict         =					chunk_data
    else:
        snake_case					:   Tuple         =					data







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						List[Any]				)			->		Optional[Any]:


    """simple docstring"""





    if name in group.attrs:
        snake_case					:   Dict         =					[n.decode("utf8"				) if hasattr(lowercase		,							"decode"				) else n for n in group.attrs[name]]
    else:
        snake_case					:   List[str]         =					[]
        snake_case					:   str         =					0
        while "%s%d" % (name, chunk_id) in group.attrs:
            data.extend(
                [n.decode("utf8"				) if hasattr(lowercase		,							"decode"				) else n for n in group.attrs["%s%d" % (name, chunk_id)]]				)
            chunk_id += 1
    return data







def 						__lowerCAmelCase		(		lowercase						:						Dict				)			->		Optional[int]:


    """simple docstring"""





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

    return tf.nest.map_structure(_expand_single_ad_tensor		,							lowercase				)









"""simple docstring"""



from typing import Any







def 						__lowerCAmelCase		(		lowercase						:						list		,							lowercase						:						list		,							lowercase						:						dict		,							lowercase						:						dict		,							lowercase						:						dict		,							)			->		list:


    """simple docstring"""





    _validation(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    # Creates data structures and fill initial step
    snake_case					:   dict         =					{}
    snake_case					:   dict         =					{}
    for state in states_space:
        snake_case					:   int         =					observations_space[0]
        snake_case					:   Any         =					(
            initial_probabilities[state] * emission_probabilities[state][observation]
        )
        snake_case					:   Union[str, Any]         =					None

    # Fills the data structure with the probabilities of
    # different transitions and pointers to previous states
    for o in range(1		,							len(lowercase				)				):
        snake_case					:   Optional[Any]         =					observations_space[o]
        snake_case					:   str         =					observations_space[o - 1]
        for state in states_space:
            # Calculates the argmax for probability function
            snake_case					:   str         =					""
            snake_case					:   List[Any]         =					-1
            for k_state in states_space:
                snake_case					:   Tuple         =					(
                    probabilities[(k_state, prior_observation)]
                    * transition_probabilities[k_state][state]
                    * emission_probabilities[state][observation]
                )
                if probability > max_probability:
                    snake_case					:   Optional[Any]         =					probability
                    snake_case					:   int         =					k_state

            # Update probabilities and pointers dicts
            snake_case					:   List[str]         =					(
                probabilities[(arg_max, prior_observation)]
                * transition_probabilities[arg_max][state]
                * emission_probabilities[state][observation]
            )

            snake_case					:   List[Any]         =					arg_max

    # The final observation
    snake_case					:   Dict         =					observations_space[len(lowercase				) - 1]

    # argmax for given final observation
    snake_case					:   str         =					""
    snake_case					:   Optional[int]         =					-1
    for k_state in states_space:
        snake_case					:   int         =					probabilities[(k_state, final_observation)]
        if probability > max_probability:
            snake_case					:   Optional[int]         =					probability
            snake_case					:   List[Any]         =					k_state
    snake_case					:   str         =					arg_max

    # Process pointers backwards
    snake_case					:   List[str]         =					last_state
    snake_case					:   Optional[int]         =					[]
    for o in range(len(lowercase				) - 1		,							-1		,							-1				):
        result.append(lowercase				)
        snake_case					:   List[str]         =					pointers[previous, observations_space[o]]
    result.reverse()

    return result







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_not_empty(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase		,							)
    _validate_lists(lowercase		,							lowercase				)
    _validate_dicts(
        lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    if not all(
        [
            observations_space,
            states_space,
            initial_probabilities,
            transition_probabilities,
            emission_probabilities,
        ]				):
        raise ValueError("There's an empty parameter"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any				)			->		None:


    """simple docstring"""





    _validate_list(lowercase		,							"observations_space"				)
    _validate_list(lowercase		,							"states_space"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   List[str]         =					F'{var_name} must be a list'
        raise ValueError(lowercase				)
    else:
        for x in _object:
            if not isinstance(lowercase		,							lowercase				):
                snake_case					:   Tuple         =					F'{var_name} must be a list of strings'
                raise ValueError(lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Any		,							lowercase						:						Any		,							)			->		None:


    """simple docstring"""





    _validate_dict(lowercase		,							"initial_probabilities"		,							lowercase				)
    _validate_nested_dict(lowercase		,							"transition_probabilities"				)
    _validate_nested_dict(lowercase		,							"emission_probabilities"				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str				)			->		None:


    """simple docstring"""





    _validate_dict(_object		,							lowercase		,							lowercase				)
    for x in _object.values():
        _validate_dict(lowercase		,							lowercase		,							lowercase		,							lowercase				)







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						str		,							lowercase						:						type		,							lowercase						:						bool = False				)			->		None:


    """simple docstring"""





    if not isinstance(_object		,							lowercase				):
        snake_case					:   int         =					F'{var_name} must be a dict'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object				):
        snake_case					:   Optional[Any]         =					F'{var_name} all keys must be strings'
        raise ValueError(lowercase				)
    if not all(isinstance(lowercase		,							lowercase				) for x in _object.values()				):
        snake_case					:   Optional[int]         =					"nested dictionary " if nested else ""
        snake_case					:   int         =					F'{var_name} {nested_text}all values must be {value_type.__name__}'
        raise ValueError(lowercase				)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





