
from random import shuffle

import tensorflow as tf
from numpy import array




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	Any       )  ->     str:
						_lowercase					       =					int(SCREAMING_SNAKE_CASE_       )
						assert noofclusters < len(SCREAMING_SNAKE_CASE_       )

						# Find out the dimensionality
						_lowercase					       =					len(vectors[0]       )

						# Will help select random centroids from among the available vectors
						_lowercase					       =					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.

						_lowercase					       =					tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

												_lowercase					       =					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
												_lowercase					       =					[
												    tf.Variable(vectors[vector_indices[i]]       ) for i in range(SCREAMING_SNAKE_CASE_       )
												]
												##These nodes will assign the centroid Variables the appropriate
												##values
												_lowercase					       =					tf.placeholder("""float64"""		,   [dim]       )
												_lowercase					       =					[]
												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)
												_lowercase					       =					[tf.Variable(0       ) for i in range(len(SCREAMING_SNAKE_CASE_       )       )]
												##These nodes will assign an assignment Variable the appropriate
												##value
												_lowercase					       =					tf.placeholder("""int32"""       )
												_lowercase					       =					[]
												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
												_lowercase					       =					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
												_lowercase					       =					tf.reduce_mean(SCREAMING_SNAKE_CASE_		,   0       )

												##Node for computing Euclidean distances
												# Placeholders for input
												_lowercase					       =					tf.placeholder("""float"""		,   [dim]       )
												_lowercase					       =					tf.placeholder("""float"""		,   [dim]       )
												_lowercase					       =					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
												_lowercase					       =					tf.placeholder("""float"""		,   [noofclusters]       )
												_lowercase					       =					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.
												_lowercase					       =					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.
												_lowercase					       =					1_00
												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_       )       ):
																								_lowercase					       =					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.
																								_lowercase					       =					[
																								    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
																								_lowercase					       =					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
																								_lowercase					       =					[
																								    vectors[i]
																								    for i in range(len(SCREAMING_SNAKE_CASE_       )       )
																								    if sess.run(assignments[i]       ) == cluster_n
																								]
																								# Compute new centroid location
																								_lowercase					       =					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
												_lowercase					       =					sess.run(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					sess.run(SCREAMING_SNAKE_CASE_       )
												return centroids, assignments

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A		: Any								=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_lowercase					       =					k.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return k




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	dict		,   SCREAMING_SNAKE_CASE_	:	dict       )  ->     PegasusForConditionalGeneration:
						_lowercase					       =					DEFAULTS.copy()
						cfg_kwargs.update(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusConfig(**SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.model.state_dict()
						_lowercase					       =					{}
						for k, v in tf_weights.items():
												_lowercase					       =					rename_state_dict_key(SCREAMING_SNAKE_CASE_       )
												if new_k not in sd:
																		raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

												if "dense" in k or "proj" in new_k:
																		_lowercase					       =					v.T
												_lowercase					       =					torch.tensor(SCREAMING_SNAKE_CASE_		,   dtype=sd[new_k].dtype       )
												assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						_lowercase					       =					torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					{k: torch.zeros_like(SCREAMING_SNAKE_CASE_       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
						mapping.update(**SCREAMING_SNAKE_CASE_       )
						_lowercase       ,						_lowercase					       =					torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_		,   strict=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[
						    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
						]
						assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], f"""no matches found for the following tf keys {extra}"""
						return torch_model




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]="./ckpt/aeslc/model.ckpt-32000"       )  ->     Dict:
						_lowercase					       =					tf.train.list_variables(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{}
						_lowercase					       =					["""Adafactor""", """global_step"""]
						for name, shape in tqdm(SCREAMING_SNAKE_CASE_		,   desc="""converting tf checkpoint to dict"""       ):
												_lowercase					       =					any(pat in name for pat in ignore_name       )
												if skip_key:
																		continue
												_lowercase					       =					tf.train.load_variable(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					array
						return tf_weights




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						# save tokenizer first
						_lowercase					       =					Path(SCREAMING_SNAKE_CASE_       ).parent.name
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
						_lowercase					       =					PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,   model_max_length=SCREAMING_SNAKE_CASE_       )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(SCREAMING_SNAKE_CASE_       )

						# convert model
						_lowercase					       =					get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												_lowercase					       =					task_specific_params
						_lowercase					       =					convert_pegasus(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						torch_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""       )
						sd.pop("""model.encoder.embed_positions.weight"""       )
						torch.save(SCREAMING_SNAKE_CASE_		,   Path(SCREAMING_SNAKE_CASE_       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
		A		: List[str]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		A		: str								=     parser.parse_args()
		if args.save_dir is None:
				A		: Tuple								=     Path(args.tf_ckpt_path).parent.name
				A		: List[Any]								=     os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)