


'''simple docstring'''






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


__UpperCAmelCase					       =							[
    # 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       _snake_case     (  A )		->					Tuple:
							for pegasus_name, hf_name in PATTERNS:
														lowerCAmelCase__      =			k.replace(A				,					A )
							return k


def       _snake_case     (  A				,					A )		->					PegasusForConditionalGeneration:
							lowerCAmelCase__      =			DEFAULTS.copy()
							cfg_kwargs.update(A )
							lowerCAmelCase__      =			PegasusConfig(**A )
							lowerCAmelCase__      =			PegasusForConditionalGeneration(A )
							lowerCAmelCase__      =			torch_model.model.state_dict()
							lowerCAmelCase__      =			{}
							for k, v in tf_weights.items():
														lowerCAmelCase__      =			rename_state_dict_key(A )
														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:
																					lowerCAmelCase__      =			v.T
														lowerCAmelCase__      =			torch.tensor(A				,					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
							lowerCAmelCase__      =			torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
							lowerCAmelCase__      =			mapping['''shared.weight''']
							lowerCAmelCase__      =			mapping['''shared.weight''']
							lowerCAmelCase__      =			{k: torch.zeros_like(A ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
							mapping.update(**A )
							lowerCAmelCase__     ,      lowerCAmelCase__      =			torch_model.model.load_state_dict(A				,					strict=A )
							lowerCAmelCase__      =			[
							    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       _snake_case     (  A="./ckpt/aeslc/model.ckpt-32000" )		->					Dict:
							lowerCAmelCase__      =			tf.train.list_variables(A )
							lowerCAmelCase__      =			{}
							lowerCAmelCase__      =			['''Adafactor''', '''global_step''']
							for name, shape in tqdm(A				,					desc='''converting tf checkpoint to dict''' ):
														lowerCAmelCase__      =			any(pat in name for pat in ignore_name )
														if skip_key:
																					continue
														lowerCAmelCase__      =			tf.train.load_variable(A				,					A )
														lowerCAmelCase__      =			array
							return tf_weights


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

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


if __name__ == "__main__":
		__UpperCAmelCase					       =							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.''')
		__UpperCAmelCase					       =							parser.parse_args()
		if args.save_dir is None:
				__UpperCAmelCase					       =							Path(args.tf_ckpt_path).parent.name
				__UpperCAmelCase					       =							os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



'''simple docstring'''






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def       _snake_case     (  A				,					A )		->					List[Any]:
							lowerCAmelCase__      =			[]
							for part_id in partition_order:
														lowerCAmelCase__      =			df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
														for row_idx, row in enumerate(A ):
																					expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
							return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
							# that each partition can hold 2 rows.
							spark_builder._repartition_df_if_needed(max_shard_size=16 )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[int]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(2 )
							lowerCAmelCase__      =			[1, 0]
							lowerCAmelCase__      =			_generate_iterable_examples(A				,					A )  # Reverse the partitions.
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					A )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(1 )
							lowerCAmelCase__      =			SparkExamplesIterable(A )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(A ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Union[str, Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch('''numpy.random.Generator''' ) as generator_mock:
														lowerCAmelCase__      =			lambda A : x.reverse()
														lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[2, 1, 0] )

														lowerCAmelCase__      =			SparkExamplesIterable(A ).shuffle_data_sources(A )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(A ):
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
																					assert row_id == expected_row_id
																					assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=0				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=1				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# Choose a small max_shard_size for maximum partitioning.
							spark_builder._repartition_df_if_needed(max_shard_size=1 )
							# The new number of partitions should not be greater than the number of rows.
							assert spark_builder.df.rdd.getNumPartitions() == 100