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					):


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



         for model_result in results.values():
               for batch_size, sequence_length in zip(model_result["bs"]	,  model_result["ss"]		):
                     UpperCAmelCase__								=  model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(__UpperCAmelCase		)


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



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  eager_mode=__UpperCAmelCase	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase		)
         UpperCAmelCase__								=  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[Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  "sgugger/tiny-distilbert-classification"
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  only_pretrain_model=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase		)
         UpperCAmelCase__								=  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					:						Any		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase		)
         UpperCAmelCase__								=  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					:						Any		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  eager_mode=__UpperCAmelCase	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase	,  [config]		)
         UpperCAmelCase__								=  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[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase	,  [config]		)
         UpperCAmelCase__								=  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					:						Union[str, Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase		)
         UpperCAmelCase__								=  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					:						Optional[int]		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase	,  [config]		)
         UpperCAmelCase__								=  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					:						List[str]		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  "patrickvonplaten/t5-tiny-random"
         UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCAmelCase		)
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase	,  configs=[config]		)
         UpperCAmelCase__								=  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					:						Any		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"
         UpperCAmelCase__								=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]	,  training=__UpperCAmelCase	,  inference=__UpperCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  use_xla=__UpperCAmelCase	,  multi_process=__UpperCAmelCase	,  )
         UpperCAmelCase__								=  TensorFlowBenchmark(__UpperCAmelCase		)
         UpperCAmelCase__								=  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					:						int		)						->    Optional[Any]:
         """simple docstring"""



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





   def       lowercase_				(self					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  "sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(__UpperCAmelCase					:						str		):
               self.assertTrue(hasattr(__UpperCAmelCase	,  "sequential"		)		)
               self.assertTrue(hasattr(__UpperCAmelCase	,  "cumulative"		)		)
               self.assertTrue(hasattr(__UpperCAmelCase	,  "current"		)		)
               self.assertTrue(hasattr(__UpperCAmelCase	,  "total"		)		)

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

from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



