


from random import shuffle

import tensorflow as tf
from numpy import array




def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      Optional[Any]):
      A_						:  str          =				int(lowerCamelCase)
      assert noofclusters < len(lowerCamelCase)

      # Find out the dimensionality
      A_						:  Union[str, Any]          =				len(vectors[0])

      # Will help select random centroids from among the available vectors
      A_						:  int          =				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.

      A_						:  Dict          =				tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

            A_						:  Union[str, 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
            A_						:  List[Any]          =				[
                tf.Variable(vectors[vector_indices[i]]) for i in range(lowerCamelCase)
            ]
            ##These nodes will assign the centroid Variables the appropriate
            ##values
            A_						:  str          =				tf.placeholder("""float64"""							,						[dim])
            A_						:  Dict          =				[]
            for centroid in centroids:
                  cent_assigns.append(tf.assign(lowerCamelCase							,						lowerCamelCase))

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

            ##Node for computing Euclidean distances
            # Placeholders for input
            A_						:  List[Any]          =				tf.placeholder("""float"""							,						[dim])
            A_						:  str          =				tf.placeholder("""float"""							,						[dim])
            A_						:  str          =				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
            A_						:  int          =				tf.placeholder("""float"""							,						[noofclusters])
            A_						:  Optional[int]          =				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.
            A_						:  List[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.
            A_						:  Tuple          =				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)):
                        A_						:  Tuple          =				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.
                        A_						:  Dict          =				[
                            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
                        A_						:  int          =				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
                        A_						:  str          =				[
                            vectors[i]
                            for i in range(len(lowerCamelCase))
                            if sess.run(assignments[i]) == cluster_n
                        ]
                        # Compute new centroid location
                        A_						:  str          =				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
            A_						:  Optional[Any]          =				sess.run(lowerCamelCase)
            A_						:  Dict          =				sess.run(lowerCamelCase)
            return centroids, assignments




'''simple docstring'''


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      str=True							,						lowerCamelCase  :      Optional[Any]="pt"):
      A_						:  Optional[int]          =				{"""add_prefix_space""": True} if isinstance(lowerCamelCase							,						lowerCamelCase) and not line.startswith(""" """) else {}
      A_						:  Optional[int]          =				padding_side
      return tokenizer(
          [line]							,						max_length=lowerCamelCase							,						padding="""max_length""" if pad_to_max_length else None							,						truncation=lowerCamelCase							,						return_tensors=lowerCamelCase							,						add_special_tokens=lowerCamelCase							,						**lowerCamelCase							,						)




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]=None							,						):
      A_						:  Dict          =				input_ids.ne(lowerCamelCase).any(dim=0)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       List[Any]	,_a :       Optional[Any]	,_a :       Tuple	,_a :       Dict	,_a :       Tuple	,_a :       Tuple="train"	,_a :       Optional[int]=None	,_a :       Any=None	,_a :       int=None	,_a :       Union[str, Any]=""	,):

        '''simple docstring'''




        super().__init__()
        A_						:  Union[str, Any]          =				Path(_a ).joinpath(type_path + """.source""" )
        A_						:  Any          =				Path(_a ).joinpath(type_path + """.target""" )
        A_						:  Dict          =				self.get_char_lens(self.src_file )
        A_						:  Optional[int]          =				max_source_length
        A_						:  List[str]          =				max_target_length
        assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
        A_						:  List[Any]          =				tokenizer
        A_						:  Optional[Any]          =				prefix
        if n_obs is not None:
              A_						:  Any          =				self.src_lens[:n_obs]
        A_						:  Optional[int]          =				src_lang
        A_						:  Tuple          =				tgt_lang






  def __len__(							self :       Tuple ):

        '''simple docstring'''




        return len(self.src_lens )






  def __getitem__(							self :       List[str]	,_a :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				index + 1  # linecache starts at 1
        A_						:  Union[str, Any]          =				self.prefix + linecache.getline(str(self.src_file )	,_a ).rstrip("""\n""" )
        A_						:  Dict          =				linecache.getline(str(self.tgt_file )	,_a ).rstrip("""\n""" )
        assert source_line, f'empty source line for index {index}'
        assert tgt_line, f'empty tgt line for index {index}'

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer	,_a ):
              source_line += self.tokenizer.eos_token
              tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        A_						:  List[str]          =				(
            self.tokenizer.question_encoder if isinstance(self.tokenizer	,_a ) else self.tokenizer
        )
        A_						:  Any          =				self.tokenizer.generator if isinstance(self.tokenizer	,_a ) else self.tokenizer

        A_						:  Optional[int]          =				encode_line(_a	,_a	,self.max_source_length	,"""right""" )
        A_						:  Optional[int]          =				encode_line(_a	,_a	,self.max_target_length	,"""right""" )

        A_						:  Optional[Any]          =				source_inputs["""input_ids"""].squeeze()
        A_						:  Dict          =				target_inputs["""input_ids"""].squeeze()
        A_						:  Union[str, Any]          =				source_inputs["""attention_mask"""].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }






  @staticmethod
  def        _a							(							_a :       int ):

        '''simple docstring'''




        return [len(_a ) for x in Path(_a ).open().readlines()]






  def        _a							(							self :       Optional[int]	,_a :       Dict ):

        '''simple docstring'''




        A_						:  str          =				torch.stack([x["""input_ids"""] for x in batch] )
        A_						:  Optional[Any]          =				torch.stack([x["""attention_mask"""] for x in batch] )
        A_						:  str          =				torch.stack([x["""decoder_input_ids"""] for x in batch] )
        A_						:  Union[str, Any]          =				(
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer	,_a )
            else self.tokenizer.pad_token_id
        )
        A_						:  str          =				(
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer	,_a )
            else self.tokenizer.pad_token_id
        )
        A_						:  List[str]          =				trim_batch(_a	,_a )
        A_   ,					A_						:  Union[str, Any]          =				trim_batch(_a	,_a	,attention_mask=_a )
        A_						:  List[str]          =				{
            """input_ids""": source_ids,
            """attention_mask""": source_mask,
            """decoder_input_ids""": y,
        }
        return batch







__magic_name__			=      getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      List[List]):
      return list(itertools.chain.from_iterable(lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  Union[str, Any]          =				get_git_info()
      save_json(lowerCamelCase							,						os.path.join(lowerCamelCase							,						"""git_log.json"""))




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[str]=4							,						**lowerCamelCase  :      List[str]):
      with open(lowerCamelCase							,						"""w""") as f:
            json.dump(lowerCamelCase							,						lowerCamelCase							,						indent=lowerCamelCase							,						**lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      Any):
      with open(lowerCamelCase) as f:
            return json.load(lowerCamelCase)




def      lowerCamelCase					(      ):
      A_						:  List[str]          =				git.Repo(search_parent_directories=lowerCamelCase)
      A_						:  Union[str, Any]          =				{
          """repo_id""": str(lowerCamelCase),
          """repo_sha""": str(repo.head.object.hexsha),
          """repo_branch""": str(repo.active_branch),
          """hostname""": str(socket.gethostname()),
      }
      return repo_infos




def      lowerCamelCase					(      lowerCamelCase  :      Callable							,						lowerCamelCase  :      Iterable):
      return list(map(lowerCamelCase							,						lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Union[str, Any]):
      with open(lowerCamelCase							,						"""wb""") as f:
            return pickle.dump(lowerCamelCase							,						lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):

      def remove_articles(lowerCamelCase  :      Any):
            return re.sub(r"""\b(a|an|the)\b"""							,						""" """							,						lowerCamelCase)

      def white_space_fix(lowerCamelCase  :      List[Any]):
            return " ".join(text.split())

      def remove_punc(lowerCamelCase  :      Union[str, Any]):
            A_						:  Optional[int]          =				set(string.punctuation)
            return "".join(ch for ch in text if ch not in exclude)

      def lower(lowerCamelCase  :      List[str]):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      int):
      A_						:  Tuple          =				normalize_answer(lowerCamelCase).split()
      A_						:  Dict          =				normalize_answer(lowerCamelCase).split()
      A_						:  int          =				Counter(lowerCamelCase) & Counter(lowerCamelCase)
      A_						:  Any          =				sum(common.values())
      if num_same == 0:
            return 0
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				(2 * precision * recall) / (precision + recall)
      return fa




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Any):
      return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[str]):
      assert len(lowerCamelCase) == len(lowerCamelCase)
      A_						:  Any          =				0
      for hypo, pred in zip(lowerCamelCase							,						lowerCamelCase):
            em += exact_match_score(lowerCamelCase							,						lowerCamelCase)
      if len(lowerCamelCase) > 0:
            em /= len(lowerCamelCase)
      return {"em": em}




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      return model_prefix.startswith("""rag""")






def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Union[str, Any]):
      A_						:  Optional[Any]          =				{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      A_						:  Tuple          =				"""dropout_rate"""
      for p in extra_params:
            if getattr(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase):
                  if not hasattr(lowerCamelCase							,						lowerCamelCase) and not hasattr(lowerCamelCase							,						equivalent_param[p]):
                        logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
                        delattr(lowerCamelCase							,						lowerCamelCase)
                        continue
                  A_						:  Tuple          =				p if hasattr(lowerCamelCase							,						lowerCamelCase) else equivalent_param[p]
                  setattr(lowerCamelCase							,						lowerCamelCase							,						getattr(lowerCamelCase							,						lowerCamelCase))
                  delattr(lowerCamelCase							,						lowerCamelCase)
      return hparams, config
