






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    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      int					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						eager_mode=UpperCamelCase_			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						"""sgugger/tiny-distilbert-classification"""
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						only_pretrain_model=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						AutoConfig.from_pretrained(UpperCamelCase_					)
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						eager_mode=UpperCamelCase_			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_			,						[config]					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						AutoConfig.from_pretrained(UpperCamelCase_					)
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_			,						[config]					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result					)
         self.check_results_dict_not_empty(results.memory_train_result					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						AutoConfig.from_pretrained(UpperCamelCase_					)
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_			,						[config]					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result					)
         self.check_results_dict_not_empty(results.memory_train_result					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						"""patrickvonplaten/t5-tiny-random"""
         __A       =						AutoConfig.from_pretrained(UpperCamelCase_					)
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_			,						configs=[config]					)
         __A       =						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 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         __A       =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]			,						training=UpperCamelCase_			,						inference=UpperCamelCase_			,						sequence_lengths=[8]			,						batch_sizes=[1]			,						use_xla=UpperCamelCase_			,						multi_process=UpperCamelCase_			,						)
         __A       =						TensorFlowBenchmark(UpperCamelCase_					)
         __A       =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result					)
         self.check_results_dict_not_empty(results.memory_inference_result					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
             __A       =						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_			,						)
             __A       =						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 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(UpperCamelCase_							:      Union[str, Any]					):
             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:
             __A       =						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_			,						)
             __A       =						TensorFlowBenchmark(UpperCamelCase_					)
             __A       =						benchmark.run()
             _check_summary_is_not_empty(result.inference_summary					)
             self.assertTrue(Path(os.path.join(UpperCamelCase_			,						"""log.txt"""					)					).exists()					)












from __future__ import annotations

from typing import Generic, TypeVar

__a   :							str						= TypeVar("T")


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Any			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						data
         __A       =						self
         __A       =						0


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						DisjointSetTreeNode(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						self.map[data]
         if elem_ref != elem_ref.parent:
             __A       =						self.find_set(elem_ref.parent.data					)
         return elem_ref.parent




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      DisjointSetTreeNode[T]			,						UpperCamelCase_							:      DisjointSetTreeNode[T]					):


         """simple docstring"""



         if nodea.rank > nodea.rank:
             __A       =						nodea
         else:
             __A       =						nodea
             if nodea.rank == nodea.rank:
                 nodea.rank += 1





     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         self.link(self.find_set(UpperCamelCase_					)			,						self.find_set(UpperCamelCase_					)					)


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         if node not in self.connections:
             __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         self.add_node(UpperCamelCase_					)
         self.add_node(UpperCamelCase_					)
         __A       =						weight
         __A       =						weight





     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						[]
         __A       =						set()
         for start in self.connections:
             for end in self.connections[start]:
                 if (start, end) not in seen:
                     seen.add((end, start)					)
                     edges.append((start, end, self.connections[start][end])					)
         edges.sort(key=lambda UpperCamelCase_					: x[2]					)

         # creating the disjoint set
         __A       =						DisjointSetTree[T]()
         for node in self.connections:
             disjoint_set.make_set(UpperCamelCase_					)

         # MST generation
         __A       =						0
         __A       =						0
         __A       =						GraphUndirectedWeighted[T]()
         while num_edges < len(self.connections					) - 1:
             __A     ,						__A     ,						__A       =						edges[index]
             index += 1
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             if parent_u != parent_v:
                 num_edges += 1
                 graph.add_edge(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
                 disjoint_set.union(UpperCamelCase_			,						UpperCamelCase_					)
         return graph




