






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


lowerCamelCase__       		=  ["""text""", """image""", """audio"""]






def 	UpperCamelCase	(							snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    []

     for input_type in input_types:
          if input_type == "text":
               inputs.append("""Text input"""	)
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""	)	) / """000000039769.png"""	).resize((512, 512)	)	)
          elif input_type == "audio":
               inputs.append(torch.ones(3000	)	)
          elif isinstance(snake_case__						,snake_case__	):
               inputs.append(create_inputs(snake_case__	)	)
          else:
               raise ValueError(f'''Invalid type requested: {input_type}'''	)

     return inputs






def 	UpperCamelCase	(							snake_case__  : List	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    []

     for output in outputs:
          if isinstance(snake_case__						,(str, AgentText)	):
               output_types.append("""text"""	)
          elif isinstance(snake_case__						,(Image.Image, AgentImage)	):
               output_types.append("""image"""	)
          elif isinstance(snake_case__						,(torch.Tensor, AgentAudio)	):
               output_types.append("""audio"""	)
          else:
               raise ValueError(f'''Invalid output: {output}'''	)

     return output_types




@is_tool_test
class       snake_case__ :

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            self.assertTrue(hasattr(self.tool      ,    """inputs"""     )     )
            self.assertTrue(hasattr(self.tool      ,    """outputs"""     )     )

            __snake_case      :Optional[Any] 							=    self.tool.inputs
            for _input in inputs:
                 if isinstance(_input      ,    a__     ):
                      for __input in _input:
                           self.assertTrue(__input in authorized_types     )
                 else:
                      self.assertTrue(_input in authorized_types     )

            __snake_case      :str 							=    self.tool.outputs
            for _output in outputs:
                 self.assertTrue(_output in authorized_types     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    create_inputs(self.tool.inputs     )
            __snake_case      :int 							=    self.tool(*a__     )

            # There is a single output
            if len(self.tool.outputs     ) == 1:
                 __snake_case      :Dict 							=    [outputs]

            self.assertListEqual(output_types(a__     )      ,    self.tool.outputs     )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            self.assertTrue(hasattr(self.tool      ,    """description"""     )     )
            self.assertTrue(hasattr(self.tool      ,    """default_checkpoint"""     )     )
            self.assertTrue(self.tool.description.startswith("""This is a tool that"""     )     )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    create_inputs(self.tool.inputs     )
            __snake_case      :Tuple 							=    self.tool(*a__     )

            if not isinstance(a__      ,    a__     ):
                 __snake_case      :List[Any] 							=    [outputs]

            self.assertEqual(len(a__     )      ,    len(self.tool.outputs     )     )

            for output, output_type in zip(a__      ,    self.tool.outputs     ):
                 __snake_case      :Dict 							=    AGENT_TYPE_MAPPING[output_type]
                 self.assertTrue(isinstance(a__      ,    a__     )     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Any 							=    create_inputs(self.tool.inputs     )

            __snake_case      :Optional[Any] 							=    []

            for _input, input_type in zip(a__      ,    self.tool.inputs     ):
                 if isinstance(a__      ,    a__     ):
                      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input     ) for _input_type in input_type]     )
                 else:
                      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input     )     )

        # Should not raise an error
            __snake_case      :Tuple 							=    self.tool(*a__     )

            if not isinstance(a__      ,    a__     ):
                 __snake_case      :Dict 							=    [outputs]

            self.assertEqual(len(a__     )      ,    len(self.tool.outputs     )     )










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 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    []
     for part_id in partition_order:
          __snake_case      :int 							=    df.where(f'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
          for row_idx, row in enumerate(snake_case__	):
               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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Any 							=    spark.range(100	).repartition(1	)
     __snake_case      :int 							=    Spark(snake_case__	)
     # 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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Tuple 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :int 							=    spark.range(10	).repartition(2	)
     __snake_case      :str 							=    [1, 0]
     __snake_case      :List[Any] 							=    _generate_iterable_examples(snake_case__						,snake_case__	)  # Reverse the partitions.
     __snake_case      :Union[str, Any] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,snake_case__	)

     for i, (row_id, row_dict) in enumerate(generate_fn()	):
          __snake_case							,			__snake_case      :Union[str, Any] 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(10	).repartition(1	)
     __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Union[str, Any] 							=    spark.range(30	).repartition(3	)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""	) as generator_mock:
          __snake_case      :Dict 							=    lambda snake_case__	: x.reverse()
          __snake_case      :int 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[2, 1, 0]	)

          __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	).shuffle_data_sources(snake_case__	)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(snake_case__	):
               __snake_case							,			__snake_case      :List[str] 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(20	).repartition(4	)

     # Partitions 0 and 2
     __snake_case      :List[Any] 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=0						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Dict 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[0, 2]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Tuple 							=    expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __snake_case      :str 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=1						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Optional[int] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[1, 3]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Dict 							=    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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(100	).repartition(1	)
     __snake_case      :Dict 							=    Spark(snake_case__	)
     # 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


