


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Any       )			->	List[str]:

       """simple docstring"""

       assert isinstance(a_						,					a_       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    List[Any]       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / '''cache'''
       a_      :	Optional[int]							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	List[Any]							=						JsonDatasetReader(a_						,					cache_dir=a_						,					keep_in_memory=a_       ).read()
       _check_json_dataset(a_						,					a_       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    Union[str, Any]						,					__A				:    int       )			->	Any:

       """simple docstring"""

       a_      :	str							=						tmp_path / '''cache'''
       a_      :	List[Any]							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       a_      :	int							=						features.copy() if features else default_expected_features
       a_      :	Dict							=						(
           Features({feature: Value(a_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Optional[Any]							=						JsonDatasetReader(a_						,					features=a_						,					cache_dir=a_       ).read()
       _check_json_dataset(a_						,					a_       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    int						,					__A				:    str       )			->	Tuple:

       """simple docstring"""

       a_      :	Union[str, Any]							=						tmp_path / '''cache'''
       a_      :	List[Any]							=						{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
       a_      :	str							=						features.copy() if features else default_expected_features
       a_      :	Optional[int]							=						(
           Features({feature: Value(a_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	int							=						JsonDatasetReader(a_						,					features=a_						,					cache_dir=a_       ).read()
       assert isinstance(a_						,					a_       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_3", "col_1", "col_2"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    int       )			->	Tuple:

       """simple docstring"""

       a_      :	Any							=						{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
       a_      :	int							=						features.copy()
       a_      :	Dict							=						(
           Features({feature: Value(a_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						tmp_path / '''cache'''
       a_      :	List[str]							=						JsonDatasetReader(a_						,					features=a_						,					cache_dir=a_       ).read()
       assert isinstance(a_						,					a_       )
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_2", "col_3", "col_1"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    List[Any]						,					__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       a_      :	Any							=						tmp_path / '''cache'''
       a_      :	Tuple							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       a_      :	Any							=						JsonDatasetReader(a_						,					cache_dir=a_						,					split=a_       ).read()
       _check_json_dataset(a_						,					a_       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Union[str, Any]						,					__A				:    Tuple       )			->	List[str]:

       """simple docstring"""

       if issubclass(a_						,					a_       ):
              a_      :	str							=						jsonl_path
       elif issubclass(a_						,					a_       ):
              a_      :	List[Any]							=						[jsonl_path]
       a_      :	Any							=						tmp_path / '''cache'''
       a_      :	List[str]							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       a_      :	Dict							=						JsonDatasetReader(a_						,					cache_dir=a_       ).read()
       _check_json_dataset(a_						,					a_       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Dict						,					__A				:    Any=("train",)       )			->	Dict:

       """simple docstring"""

       assert isinstance(a_						,					a_       )
       for split in splits:
              a_      :	Tuple							=						dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    List[Any]						,					__A				:    Dict       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Dict							=						tmp_path / '''cache'''
       a_      :	int							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Union[str, Any]							=						JsonDatasetReader({'train': jsonl_path}						,					cache_dir=a_						,					keep_in_memory=a_       ).read()
       _check_json_datasetdict(a_						,					a_       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Union[str, Any]						,					__A				:    int       )			->	List[str]:

       """simple docstring"""

       a_      :	List[Any]							=						tmp_path / '''cache'''
       a_      :	List[Any]							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       a_      :	List[str]							=						features.copy() if features else default_expected_features
       a_      :	Any							=						(
           Features({feature: Value(a_       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						JsonDatasetReader({'train': jsonl_path}						,					features=a_						,					cache_dir=a_       ).read()
       _check_json_datasetdict(a_						,					a_       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Any						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       if split:
              a_      :	Any							=						{split: jsonl_path}
       else:
              a_      :	Optional[Any]							=						'''train'''
              a_      :	List[str]							=						{'''train''': jsonl_path, '''test''': jsonl_path}
       a_      :	List[Any]							=						tmp_path / '''cache'''
       a_      :	Tuple							=						{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       a_      :	Dict							=						JsonDatasetReader(a_						,					cache_dir=a_       ).read()
       _check_json_datasetdict(a_						,					a_						,					splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	Union[str, Any]:

       """simple docstring"""

       return json.load(a_       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]       )			->	Optional[Any]:

       """simple docstring"""

       return [json.loads(a_       ) for line in buffer]
class 				SCREAMING_SNAKE_CASE__						:
      @pytest.mark.parametrize('lines, load_json_function'  ,							[(True, load_json_lines), (False, load_json)]  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    str:
             with io.BytesIO() as buffer:
                    JsonDatasetWriter(__snake_case  ,							__snake_case  ,							lines=__snake_case  ).write()
                    buffer.seek(0  )
                    a_      :	Optional[int]							=						load_json_function(__snake_case  )
             assert isinstance(__snake_case  ,							__snake_case  )
             assert isinstance(exported_content[0]  ,							__snake_case  )
             assert len(__snake_case  ) == 1_0
      @pytest.mark.parametrize(
          'orient, container, keys, len_at'  ,							[
              ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
              ('split', dict, {'columns', 'data'}, 'data'),
              ('index', dict, set('0123456789'  ), None),
              ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
              ('values', list, None, None),
              ('table', dict, {'schema', 'data'}, 'data'),
          ]  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Optional[Any]:
             with io.BytesIO() as buffer:
                    JsonDatasetWriter(__snake_case  ,							__snake_case  ,							lines=__snake_case  ,							orient=__snake_case  ).write()
                    buffer.seek(0  )
                    a_      :	str							=						load_json(__snake_case  )
             assert isinstance(__snake_case  ,							__snake_case  )
             if keys:
                    if container is dict:
                           assert exported_content.keys() == keys
                    else:
                           assert exported_content[0].keys() == keys
             else:
                    assert not hasattr(__snake_case  ,							'keys'  ) and not hasattr(exported_content[0]  ,							'keys'  )
             if len_at:
                    assert len(exported_content[len_at]  ) == 1_0
             else:
                    assert len(__snake_case  ) == 1_0
      @pytest.mark.parametrize('lines, load_json_function'  ,							[(True, load_json_lines), (False, load_json)]  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : List[str]  )	->    str:
             with io.BytesIO() as buffer:
                    JsonDatasetWriter(__snake_case  ,							__snake_case  ,							lines=__snake_case  ,							num_proc=2  ).write()
                    buffer.seek(0  )
                    a_      :	Any							=						load_json_function(__snake_case  )
             assert isinstance(__snake_case  ,							__snake_case  )
             assert isinstance(exported_content[0]  ,							__snake_case  )
             assert len(__snake_case  ) == 1_0
      @pytest.mark.parametrize(
          'orient, container, keys, len_at'  ,							[
              ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
              ('split', dict, {'columns', 'data'}, 'data'),
              ('index', dict, set('0123456789'  ), None),
              ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
              ('values', list, None, None),
              ('table', dict, {'schema', 'data'}, 'data'),
          ]  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             with io.BytesIO() as buffer:
                    JsonDatasetWriter(__snake_case  ,							__snake_case  ,							lines=__snake_case  ,							orient=__snake_case  ,							num_proc=2  ).write()
                    buffer.seek(0  )
                    a_      :	Tuple							=						load_json(__snake_case  )
             assert isinstance(__snake_case  ,							__snake_case  )
             if keys:
                    if container is dict:
                           assert exported_content.keys() == keys
                    else:
                           assert exported_content[0].keys() == keys
             else:
                    assert not hasattr(__snake_case  ,							'keys'  ) and not hasattr(exported_content[0]  ,							'keys'  )
             if len_at:
                    assert len(exported_content[len_at]  ) == 1_0
             else:
                    assert len(__snake_case  ) == 1_0
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    int:
             with pytest.raises(__snake_case  ):
                    with io.BytesIO() as buffer:
                           JsonDatasetWriter(__snake_case  ,							__snake_case  ,							num_proc=0  )

      @pytest.mark.parametrize('compression, extension'  ,							[('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    Dict:
             a_      :	Any							=						tmp_path_factory.mktemp('data'  ) / F"""test.json.{extension}"""
             a_      :	int							=						str(shared_datadir / F"""test_file.json.{extension}"""  )
             JsonDatasetWriter(__snake_case  ,							__snake_case  ,							compression=__snake_case  ).write()

             with fsspec.open(__snake_case  ,							'rb'  ,							compression='infer'  ) as f:
                    a_      :	List[Any]							=						f.read()
             with fsspec.open(__snake_case  ,							'rb'  ,							compression='infer'  ) as f:
                    a_      :	int							=						f.read()
             assert exported_content == original_content






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_   :				Optional[int]						      = [
    # 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  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						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:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						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
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	Optional[Any]							=						[
           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  SCREAMING_SNAKE_CASE_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

       # convert model
       a_      :	List[Any]							=						get_tf_weights_as_numpy(__A       )
       a_      :	List[str]							=						task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              a_      :	Union[str, Any]							=						task_specific_params
       a_      :	Optional[int]							=						convert_pegasus(__A						,					__A       )
       torch_model.save_pretrained(__A       )
       a_      :	Dict							=						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_   :				Dict						      = 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_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


