import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCamelCase__											=					datasets.utils.logging.get_logger(__name__)





@dataclass
class      A							(				datasets.BuilderConfig     ):
   __UpperCAmelCase : int      =			1_00_00
   __UpperCAmelCase : Optional[List[str]]      =			None
   __UpperCAmelCase : Optional[datasets.Features]      =			None





class      A							(				datasets.ArrowBasedBuilder     ):
   __UpperCAmelCase : List[Any]      =			ParquetConfig


   def 			lowercase_      (self     :						Optional[Any]		)						->				Optional[Any]:
         """simple docstring"""



         return datasets.DatasetInfo(features=self.config.features		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int		)						->				Union[str, Any]:
         """simple docstring"""



         if not self.config.data_files:
               raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""		)
         UpperCAmelCase__								=  dl_manager.download_and_extract(self.config.data_files		)
         if isinstance(__UpperCAmelCase       ,  (str, list, tuple)		):
               UpperCAmelCase__								=  data_files
               if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
                     UpperCAmelCase__								=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               UpperCAmelCase__								=  [dl_manager.iter_files(__UpperCAmelCase		) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,  gen_kwargs={"files": files}		)]
         UpperCAmelCase__								=  []
         for split_name, files in data_files.items():
               if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		):
                     UpperCAmelCase__								=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               UpperCAmelCase__								=  [dl_manager.iter_files(__UpperCAmelCase		) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                     for file in itertools.chain.from_iterable(__UpperCAmelCase		):
                           with open(__UpperCAmelCase       ,  "rb"		) as f:
                                 UpperCAmelCase__								=  datasets.Features.from_arrow_schema(pq.read_schema(__UpperCAmelCase		)		)
                           break
               splits.append(datasets.SplitGenerator(name=__UpperCAmelCase       ,  gen_kwargs={"files": files}		)		)
         return splits


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						pa.Table		)						->				pa.Table:
         """simple docstring"""



         if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               UpperCAmelCase__								=  table_cast(__UpperCAmelCase       ,  self.info.features.arrow_schema		)
         return pa_table





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.info.features.arrow_schema if self.info.features is not None else None
         if self.info.features is not None and self.config.columns is not None:
               if sorted(field.name for field in schema		) != sorted(self.config.columns		):
                     raise ValueError(
                         f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""		)
         for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase		)		):
               with open(__UpperCAmelCase       ,  "rb"		) as f:
                     UpperCAmelCase__								=  pq.ParquetFile(__UpperCAmelCase		)
                     try:
                           for batch_idx, record_batch in enumerate(
                               parquet_file.iter_batches(batch_size=self.config.batch_size       ,  columns=self.config.columns		)		):
                                 UpperCAmelCase__								=  pa.Table.from_batches([record_batch]		)
                                 # Uncomment for debugging (will print the Arrow table size and elements)
                                 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                 yield f"""{file_idx}_{batch_idx}""", self._cast_table(__UpperCAmelCase		)
                     except ValueError as e:
                           logger.error(f"""Failed to read file '{file}' with error {type(__UpperCAmelCase		)}: {e}"""		)
                           raise










import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self					,  lowercase)			->						Dict:







          '''simple docstring'''

          for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result['bs']					,  model_result['ss']):
                        a__						:	int =				model_result['result'][batch_size][sequence_length]
                        self.assertIsNotNone(lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Tuple =				'sshleifer/tiny-gpt2'
          a__						:	str =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  eager_mode=lowercase					,  multi_process=lowercase					,  )
          a__						:	List[Any] =				TensorFlowBenchmark(lowercase)
          a__						:	Any =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				'sgugger/tiny-distilbert-classification'
          a__						:	List[str] =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  only_pretrain_model=lowercase					,  )
          a__						:	str =				TensorFlowBenchmark(lowercase)
          a__						:	int =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

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







          '''simple docstring'''

          a__						:	Union[str, Any] =				'sshleifer/tiny-gpt2'
          a__						:	Dict =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  )
          a__						:	List[str] =				TensorFlowBenchmark(lowercase)
          a__						:	Tuple =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				'sshleifer/tiny-gpt2'
          a__						:	str =				AutoConfig.from_pretrained(lowercase)
          a__						:	Any =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  eager_mode=lowercase					,  multi_process=lowercase					,  )
          a__						:	Optional[Any] =				TensorFlowBenchmark(lowercase					,  [config])
          a__						:	Union[str, Any] =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Dict =				'sshleifer/tiny-gpt2'
          a__						:	Union[str, Any] =				AutoConfig.from_pretrained(lowercase)
          a__						:	Optional[int] =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  )
          a__						:	str =				TensorFlowBenchmark(lowercase					,  [config])
          a__						:	List[str] =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Optional[int] =				'sshleifer/tiny-gpt2'
          a__						:	Any =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  )
          a__						:	Tuple =				TensorFlowBenchmark(lowercase)
          a__						:	str =				benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result)
          self.check_results_dict_not_empty(results.memory_train_result)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Optional[Any] =				'sshleifer/tiny-gpt2'
          a__						:	List[Any] =				AutoConfig.from_pretrained(lowercase)
          a__						:	List[Any] =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  )
          a__						:	Optional[Any] =				TensorFlowBenchmark(lowercase					,  [config])
          a__						:	List[Any] =				benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result)
          self.check_results_dict_not_empty(results.memory_train_result)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Optional[int] =				'patrickvonplaten/t5-tiny-random'
          a__						:	Optional[int] =				AutoConfig.from_pretrained(lowercase)
          a__						:	Any =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  multi_process=lowercase					,  )
          a__						:	List[Any] =				TensorFlowBenchmark(lowercase					,  configs=[config])
          a__						:	Union[str, Any] =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU')) == 0					,  'Cannot do xla on CPU.')
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				'sshleifer/tiny-gpt2'
          a__						:	str =				TensorFlowBenchmarkArguments(
              models=[MODEL_ID]					,  training=lowercase					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  use_xla=lowercase					,  multi_process=lowercase					,  )
          a__						:	int =				TensorFlowBenchmark(lowercase)
          a__						:	Tuple =				benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result)
          self.check_results_dict_not_empty(results.memory_inference_result)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	str =				'sshleifer/tiny-gpt2'
          with tempfile.TemporaryDirectory() as tmp_dir:
                 a__						:	int =				TensorFlowBenchmarkArguments(
                     models=[MODEL_ID]					,  inference=lowercase					,  save_to_csv=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  inference_time_csv_file=os.path.join(lowercase					,  'inf_time.csv')					,  inference_memory_csv_file=os.path.join(lowercase					,  'inf_mem.csv')					,  env_info_csv_file=os.path.join(lowercase					,  'env.csv')					,  multi_process=lowercase					,  )
                 a__						:	List[str] =				TensorFlowBenchmark(lowercase)
                 benchmark.run()
                 self.assertTrue(Path(os.path.join(lowercase					,  'inf_time.csv')).exists())
                 self.assertTrue(Path(os.path.join(lowercase					,  'inf_mem.csv')).exists())
                 self.assertTrue(Path(os.path.join(lowercase					,  'env.csv')).exists())

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Union[str, Any] =				'sshleifer/tiny-gpt2'

          def _check_summary_is_not_empty(lowercase):
                 self.assertTrue(hasattr(lowercase					,  'sequential'))
                 self.assertTrue(hasattr(lowercase					,  'cumulative'))
                 self.assertTrue(hasattr(lowercase					,  'current'))
                 self.assertTrue(hasattr(lowercase					,  'total'))

          with tempfile.TemporaryDirectory() as tmp_dir:
                 a__						:	List[str] =				TensorFlowBenchmarkArguments(
                     models=[MODEL_ID]					,  inference=lowercase					,  sequence_lengths=[8]					,  batch_sizes=[1]					,  log_filename=os.path.join(lowercase					,  'log.txt')					,  log_print=lowercase					,  trace_memory_line_by_line=lowercase					,  eager_mode=lowercase					,  multi_process=lowercase					,  )
                 a__						:	List[str] =				TensorFlowBenchmark(lowercase)
                 a__						:	Any =				benchmark.run()
                 _check_summary_is_not_empty(result.inference_summary)
                 self.assertTrue(Path(os.path.join(lowercase					,  'log.txt')).exists())


