
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 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				lowercase_       =					[]
				for part_id in partition_order:
								lowercase_       =					df.where(F'''SPARK_PARTITION_ID() = {part_id}'''					).collect()
								for row_idx, row in enumerate(UpperCAmelCase_					):
												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 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(100					).repartition(1					)
				lowercase_       =					Spark(UpperCAmelCase_					)
				# 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 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(10					).repartition(2					)
				lowercase_       =					[1, 0]
				lowercase_       =					_generate_iterable_examples(UpperCAmelCase_						,   UpperCAmelCase_					)  # Reverse the partitions.
				lowercase_       =					_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_						,   UpperCAmelCase_					)

				for i, (row_id, row_dict) in enumerate(generate_fn()					):
								lowercase_       =					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 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(10					).repartition(1					)
				lowercase_       =					SparkExamplesIterable(UpperCAmelCase_					)
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(UpperCAmelCase_					):
								assert row_id == F'''0_{i}'''
								assert row_dict == {"id": i}




@require_not_windows
@require_dill_gt_0_3_2
def 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(30					).repartition(3					)
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator'''					) as generator_mock:
								lowercase_       =					lambda snake_case__					: x.reverse()
								lowercase_       =					_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_						,   [2, 1, 0]					)

								lowercase_       =					SparkExamplesIterable(UpperCAmelCase_					).shuffle_data_sources(UpperCAmelCase_					)
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(UpperCAmelCase_					):
												lowercase_       =					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 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(20					).repartition(4					)

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

				# Partitions 1 and 3
				lowercase_       =					SparkExamplesIterable(UpperCAmelCase_					).shard_data_sources(worker_id=1						,   num_workers=2					)
				assert shard_it_a.n_shards == 2
				lowercase_       =					_get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_						,   [1, 3]					)
				for i, (row_id, row_dict) in enumerate(UpperCAmelCase_					):
								lowercase_       =					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 				a						(							):


				'''simple docstring'''
				lowercase_       =					pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				lowercase_       =					spark.range(100					).repartition(1					)
				lowercase_       =					Spark(UpperCAmelCase_					)
				# 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








import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]="attention"						):




      """simple docstring"""





      a						:Optional[int] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      a						:int 							=       params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      if split_mlp_wi:
            a						:int 							=       params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            a						:Dict 							=       (wi_a, wi_a)
      else:
            a						:Optional[Any] 							=       params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      a						:Dict 							=       params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  *, UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:str 							=       traverse_util.flatten_dict(variables['''target''']						)
      a						:Any 							=       {'''/'''.join(UpperCAmelCase_						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      a						:Any 							=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' ,  UpperCAmelCase_						)

      a						:Optional[Any] 							=       collections.OrderedDict()

      # Shared embeddings.
      a						:Union[str, Any] 							=       old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_						):
            # Block i, layer 0 (Self Attention).
            a						:Optional[Any] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_attention_layer_norm'''						)
            a							,				a							,				a							,				a						:Optional[int] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''attention'''						)
            a						:List[Any] 							=       layer_norm
            a						:str 							=       k.T
            a						:Dict 							=       o.T
            a						:int 							=       q.T
            a						:Optional[Any] 							=       v.T

            # Block i, layer 1 (MLP).
            a						:Tuple 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  '''pre_mlp_layer_norm'''						)
            a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''encoder''' ,  UpperCAmelCase_						)
            a						:Any 							=       layer_norm
            if split_mlp_wi:
                  a						:Any 							=       wi[0].T
                  a						:Tuple 							=       wi[1].T
            else:
                  a						:List[str] 							=       wi.T
            a						:List[Any] 							=       wo.T

      a						:Union[str, Any] 							=       old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      a						:Optional[Any] 							=       old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_						):
                  # Block i, layer 0 (Self Attention).
                  a						:List[str] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_self_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:List[Any] 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''self_attention'''						)
                  a						:List[Any] 							=       layer_norm
                  a						:Tuple 							=       k.T
                  a						:int 							=       o.T
                  a						:Any 							=       q.T
                  a						:Optional[int] 							=       v.T

                  # Block i, layer 1 (Cross Attention).
                  a						:str 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_cross_attention_layer_norm'''						)
                  a							,				a							,				a							,				a						:Any 							=       tax_attention_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''encoder_decoder_attention'''						)
                  a						:str 							=       layer_norm
                  a						:Optional[Any] 							=       k.T
                  a						:Any 							=       o.T
                  a						:Dict 							=       q.T
                  a						:Optional[Any] 							=       v.T

                  # Block i, layer 2 (MLP).
                  a						:Optional[int] 							=       tax_layer_norm_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  '''pre_mlp_layer_norm'''						)
                  a							,				a						:List[Any] 							=       tax_mlp_lookup(UpperCAmelCase_ ,  UpperCAmelCase_ ,  '''decoder''' ,  UpperCAmelCase_						)
                  a						:Optional[int] 							=       layer_norm
                  if split_mlp_wi:
                        a						:int 							=       wi[0].T
                        a						:Tuple 							=       wi[1].T
                  else:
                        a						:str 							=       wi.T
                  a						:Dict 							=       wo.T

            a						:Any 							=       old['''decoder/decoder_norm/scale''']
            a						:Optional[Any] 							=       old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  a						:Union[str, Any] 							=       old['''decoder/logits_dense/kernel'''].T

      return new
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		bool						):




      """simple docstring"""





      a						:List[Any] 							=       collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            a						:Optional[Any] 							=       state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a						:Tuple 							=       state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''						)
                  a						:Optional[Any] 							=       state_dict['''shared.weight''']

      return state_dict
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Tuple 							=       checkpoints.load_tax_checkpoint(UpperCAmelCase_						)
      a						:Optional[int] 							=       convert_tax_to_pytorch(UpperCAmelCase_ ,  num_layers=config.num_layers ,  is_encoder_only=UpperCAmelCase_						)
      a						:Tuple 							=       make_state_dict(UpperCAmelCase_ ,  UpperCAmelCase_						)
      model.load_state_dict(UpperCAmelCase_ ,  strict=UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		bool = False						):




      """simple docstring"""





      a						:List[Any] 							=       TaConfig.from_json_file(UpperCAmelCase_						)
      print(F'''Building PyTorch model from configuration: {config}'''						)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            a						:Any 							=       TaEncoderModel(UpperCAmelCase_						)
      else:
            a						:List[str] 							=       TaForConditionalGeneration(UpperCAmelCase_						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
      model.save_pretrained(UpperCAmelCase_						)

      # Verify that we can load the checkpoint.
      model.from_pretrained(UpperCAmelCase_						)
      print('''Done'''						)


if __name__ == "__main__":
     snake_case				:    Any       				=     argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
     )
     snake_case				:    Optional[Any]       				=     parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





