

"""simple docstring"""


from random import shuffle

import tensorflow as tf
from numpy import array




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

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

      # Will help select random centroids from among the available vectors
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	list(range(len(_SCREAMING_SNAKE_CASE							)							)							)
      shuffle(_SCREAMING_SNAKE_CASE							)

      # 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__   :      Optional[Any]      		=	tf.Graph()

      with graph.as_default():
            # SESSION OF COMPUTATION

            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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__   :      List[Any]      		=	[
                tf.Variable(vectors[vector_indices[i]]							) for i in range(_SCREAMING_SNAKE_CASE							)
            ]
            ##These nodes will assign the centroid Variables the appropriate
            ##values
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tf.placeholder("""float64"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      int      		=	[]
            for centroid in centroids:
                  cent_assigns.append(tf.assign(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							)							)

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

            ##Now lets construct the node that will compute the mean
            # The placeholder for the input
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	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__   :      Any      		=	tf.reduce_mean(_SCREAMING_SNAKE_CASE  ,					0							)

            ##Node for computing Euclidean distances
            # Placeholders for input
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tf.placeholder("""float"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tf.placeholder("""float"""  ,					[dim]							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE							)  ,					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__   :      int      		=	tf.placeholder("""float"""  ,					[noofclusters]							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.argmin(_SCREAMING_SNAKE_CASE  ,					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__   :      str      		=	tf.initialize_all_variables()

            # Initialize all variables
            sess.run(_SCREAMING_SNAKE_CASE							)

            ##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(_SCREAMING_SNAKE_CASE							):
                  ##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(_SCREAMING_SNAKE_CASE							)							):
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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__   :      List[str]      		=	[
                            sess.run(_SCREAMING_SNAKE_CASE  ,					feed_dict={va: vect, va: sess.run(_SCREAMING_SNAKE_CASE							)}							)
                            for centroid in centroids
                        ]
                        # Now use the cluster assignment node, with the distances
                        # as the input
                        SCREAMING_SNAKE_CASE__   :      Tuple      		=	sess.run(
                            _SCREAMING_SNAKE_CASE  ,					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(_SCREAMING_SNAKE_CASE							):
                        # Collect all the vectors assigned to this cluster
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	[
                            vectors[i]
                            for i in range(len(_SCREAMING_SNAKE_CASE							)							)
                            if sess.run(assignments[i]							) == cluster_n
                        ]
                        # Compute new centroid location
                        SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	sess.run(
                            _SCREAMING_SNAKE_CASE  ,					feed_dict={mean_input: array(_SCREAMING_SNAKE_CASE							)}							)
                        # Assign value to appropriate variable
                        sess.run(
                            cent_assigns[cluster_n]  ,					feed_dict={centroid_value: new_location}							)

                        # Return centroids and assignments
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sess.run(_SCREAMING_SNAKE_CASE							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	sess.run(_SCREAMING_SNAKE_CASE							)
            return centroids, assignments







"""simple docstring"""


# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a					:str    = 2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a					:int    = {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
a					:Dict    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  a					:List[Any]    = "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  a					:str    = "allenai"




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE__   :      str      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[int]:
      # prep
      assert os.path.exists(__lowerCAmelCase							)
      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      print(F'''Writing results to {pytorch_dump_folder_path}'''							)

      # handle various types of models

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	basename(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dirname(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      int      		=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cls.hub_models()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""."""
      # note: since the model dump is old, fairseq has upgraded its model some
      # time later, and it does a whole lot of rewrites and splits on the saved
      # weights, therefore we can't use torch.load() directly on the model file.
      # see: upgrade_state_dict(state_dict) in fairseq_model.py
      print(F'''using checkpoint {checkpoint_file}'''							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hub_utils.from_pretrained(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					archive_map=__lowerCAmelCase  ,					**__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vars(chkpt["""args"""]["""model"""]							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""source_lang"""]
      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""target_lang"""]

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dirname(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	basename(__lowerCAmelCase							)

      # dicts
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{src_lang}.txt'''							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{tgt_lang}.txt'''							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	rewrite_dict_keys(src_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-src.json"""							)
      print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # detect whether this is a do_lower_case situation, which can be derived by checking whether we
      # have at least one uppercase letter in the source vocab
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True
      for k in src_vocab.keys():
            if not k.islower():
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                  break

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rewrite_dict_keys(tgt_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					"""vocab-tgt.json"""							)
      print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # merges_file (bpecodes)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					VOCAB_FILES_NAMES["""merges_file"""]							)
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
            if os.path.exists(__lowerCAmelCase							):
                  break
      with open(__lowerCAmelCase  ,					encoding="""utf-8"""							) as fin:
            SCREAMING_SNAKE_CASE__   :      Any      		=	fin.read()
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	re.sub(r""" \d+$"""  ,					""""""  ,					__lowerCAmelCase  ,					0  ,					re.M							)  # remove frequency number
      print(F'''Generating {merges_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as fout:
            fout.write(__lowerCAmelCase							)

      # model config
      SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(__lowerCAmelCase  ,					"""config.json"""							)

      # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
      # may have to modify the tokenizer if a different type is used by a future model
      assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
      assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """architectures""": ["""FSMTForConditionalGeneration"""],
          """model_type""": """fsmt""",
          """activation_dropout""": args["""activation_dropout"""],
          """activation_function""": """relu""",
          """attention_dropout""": args["""attention_dropout"""],
          """d_model""": args["""decoder_embed_dim"""],
          """dropout""": args["""dropout"""],
          """init_std""": 0.02,
          """max_position_embeddings""": args["""max_source_positions"""],
          """num_hidden_layers""": args["""encoder_layers"""],
          """src_vocab_size""": src_vocab_size,
          """tgt_vocab_size""": tgt_vocab_size,
          """langs""": [src_lang, tgt_lang],
          """encoder_attention_heads""": args["""encoder_attention_heads"""],
          """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
          """encoder_layerdrop""": args["""encoder_layerdrop"""],
          """encoder_layers""": args["""encoder_layers"""],
          """decoder_attention_heads""": args["""decoder_attention_heads"""],
          """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
          """decoder_layerdrop""": args["""decoder_layerdrop"""],
          """decoder_layers""": args["""decoder_layers"""],
          """bos_token_id""": 0,
          """pad_token_id""": 1,
          """eos_token_id""": 2,
          """is_encoder_decoder""": True,
          """scale_embedding""": not args["""no_scale_embedding"""],
          """tie_word_embeddings""": args["""share_all_embeddings"""],
      }

      # good hparam defaults to start with
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	5
      SCREAMING_SNAKE_CASE__   :      str      		=	False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	best_score_hparams[model_dir]["""length_penalty"""]
      else:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1.0

      print(F'''Generating {fsmt_model_config_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # tokenizer config
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
          """langs""": [src_lang, tgt_lang],
          """model_max_length""": 1024,
          """do_lower_case""": do_lower_case,
      }

      print(F'''Generating {fsmt_tokenizer_config_file}'''							)
      with open(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # model
      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""models"""][0]
      SCREAMING_SNAKE_CASE__   :      int      		=	model.state_dict()

      # rename keys to start with 'model.'
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()							)

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      str      		=	[
          """model.model""",
          """model.encoder.version""",
          """model.decoder.version""",
          """model.encoder_embed_tokens.weight""",
          """model.decoder_embed_tokens.weight""",
          """model.encoder.embed_positions._float_tensor""",
          """model.decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            model_state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	FSMTConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FSMTForConditionalGeneration(__lowerCAmelCase							)

      # check that it loads ok
      model_new.load_state_dict(__lowerCAmelCase  ,					strict=__lowerCAmelCase							)

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
      print(F'''Generating {pytorch_weights_dump_path}'''							)
      torch.save(__lowerCAmelCase  ,					__lowerCAmelCase							)

      print("""Conversion is done!"""							)
      print("""\nLast step is to upload the files to s3"""							)
      print(F'''cd {data_root}'''							)
      print(F'''transformers-cli upload {model_dir}'''							)


if __name__ == "__main__":
  a					:Optional[Any]    = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fsmt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
          " bpecodes, etc."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a					:List[str]    = parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




