

"""simple docstring"""


from random import shuffle

import tensorflow as tf
from numpy import array




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      int      		=	int(__lowerCAmelCase							)
      assert noofclusters < len(__lowerCAmelCase							)

      # Find out the dimensionality
      SCREAMING_SNAKE_CASE__   :      int      		=	len(vectors[0]							)

      # Will help select random centroids from among the available vectors
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	list(range(len(__lowerCAmelCase							)							)							)
      shuffle(__lowerCAmelCase							)

      # GRAPH OF COMPUTATION
      # We initialize a new graph and set it as the default during each run
      # of this algorithm. This ensures that as this function is called
      # multiple times, the default graph doesn't keep getting crowded with
      # unused ops and Variables from previous function calls.

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

            SCREAMING_SNAKE_CASE__   :      Any      		=	tf.Session()

            ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

            ##First lets ensure we have a Variable vector for each centroid,
            ##initialized to one of the vectors from the available data points
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[
                tf.Variable(vectors[vector_indices[i]]							) for i in range(__lowerCAmelCase							)
            ]
            ##These nodes will assign the centroid Variables the appropriate
            ##values
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tf.placeholder("""float64"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	[]
            for centroid in centroids:
                  cent_assigns.append(tf.assign(__lowerCAmelCase  ,					__lowerCAmelCase							)							)

            ##Variables for cluster assignments of individual vectors(initialized
            ##to 0 at first)
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[tf.Variable(0							) for i in range(len(__lowerCAmelCase							)							)]
            ##These nodes will assign an assignment Variable the appropriate
            ##value
            SCREAMING_SNAKE_CASE__   :      Dict      		=	tf.placeholder("""int32"""							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	[]
            for assignment in assignments:
                  cluster_assigns.append(tf.assign(__lowerCAmelCase  ,					__lowerCAmelCase							)							)

            ##Now lets construct the node that will compute the mean
            # The placeholder for the input
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tf.placeholder("""float"""  ,					[None, dim]							)
            # The Node/op takes the input and computes a mean along the 0th
            # dimension, i.e. the list of input vectors
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tf.reduce_mean(__lowerCAmelCase  ,					0							)

            ##Node for computing Euclidean distances
            # Placeholders for input
            SCREAMING_SNAKE_CASE__   :      Dict      		=	tf.placeholder("""float"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	tf.placeholder("""float"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCAmelCase  ,					__lowerCAmelCase							)  ,					2							)							)							)

            ##This node will figure out which cluster to assign a vector to,
            ##based on Euclidean distances of the vector from the centroids.
            # Placeholder for input
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tf.placeholder("""float"""  ,					[noofclusters]							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	tf.argmin(__lowerCAmelCase  ,					0							)

            ##INITIALIZING STATE VARIABLES

            ##This will help initialization of all Variables defined with respect
            ##to the graph. The Variable-initializer should be defined after
            ##all the Variables have been constructed, so that each of them
            ##will be included in the initialization.
            SCREAMING_SNAKE_CASE__   :      Any      		=	tf.initialize_all_variables()

            # Initialize all variables
            sess.run(__lowerCAmelCase							)

            ##CLUSTERING ITERATIONS

            # Now perform the Expectation-Maximization steps of K-Means clustering
            # iterations. To keep things simple, we will only do a set number of
            # iterations, instead of using a Stopping Criterion.
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	100
            for _ in range(__lowerCAmelCase							):
                  ##EXPECTATION STEP
                  ##Based on the centroid locations till last iteration, compute
                  ##the _expected_ centroid assignments.
                  # Iterate over each vector
                  for vector_n in range(len(__lowerCAmelCase							)							):
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	vectors[vector_n]
                        # Compute Euclidean distance between this vector and each
                        # centroid. Remember that this list cannot be named
                        #'centroid_distances', since that is the input to the
                        # cluster assignment node.
                        SCREAMING_SNAKE_CASE__   :      int      		=	[
                            sess.run(__lowerCAmelCase  ,					feed_dict={va: vect, va: sess.run(__lowerCAmelCase							)}							)
                            for centroid in centroids
                        ]
                        # Now use the cluster assignment node, with the distances
                        # as the input
                        SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sess.run(
                            __lowerCAmelCase  ,					feed_dict={centroid_distances: distances}							)
                        # Now assign the value to the appropriate state variable
                        sess.run(
                            cluster_assigns[vector_n]  ,					feed_dict={assignment_value: assignment}							)

                  ##MAXIMIZATION STEP
                  # Based on the expected state computed from the Expectation Step,
                  # compute the locations of the centroids so as to maximize the
                  # overall objective of minimizing within-cluster Sum-of-Squares
                  for cluster_n in range(__lowerCAmelCase							):
                        # Collect all the vectors assigned to this cluster
                        SCREAMING_SNAKE_CASE__   :      Any      		=	[
                            vectors[i]
                            for i in range(len(__lowerCAmelCase							)							)
                            if sess.run(assignments[i]							) == cluster_n
                        ]
                        # Compute new centroid location
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	sess.run(
                            __lowerCAmelCase  ,					feed_dict={mean_input: array(__lowerCAmelCase							)}							)
                        # Assign value to appropriate variable
                        sess.run(
                            cent_assigns[cluster_n]  ,					feed_dict={centroid_value: new_location}							)

        # Return centroids and assignments
            SCREAMING_SNAKE_CASE__   :      Dict      		=	sess.run(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	sess.run(__lowerCAmelCase							)
            return centroids, assignments







"""simple docstring"""


from typing import Mapping

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


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

a					:Union[str, 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      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

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

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





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

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

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




