




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a=None		,     _a=None		,     _a=0				)						->							List[Any]:
           _A							:     int			=						1.0 if scale is None else scale
           _A							:     Dict			=						0.0 if loc is None else loc

           super().__init__(_a		,     [AffineTransform(loc=self.loc		,     scale=self.scale		,     event_dim=_a				)]				)

     @property
     def 					a__	(  self				)						->							Tuple:
           return self.base_dist.mean * self.scale + self.loc

     @property
     def 					a__	(  self				)						->							int:
           return self.base_dist.variance * self.scale**2



     @property
     def 					a__	(  self				)						->							Tuple:
           return self.variance.sqrt()







class 						lowercase							(   nn.Module		):

     def __init__(  self		,     _a		,     _a		,     _a		,     **_a				)						->							None:
           super().__init__(**_a				)
           _A							:     List[Any]			=						args_dim
           _A							:     Optional[Any]			=						nn.ModuleList([nn.Linear(_a		,     _a				) for dim in args_dim.values()]				)
           _A							:     Dict			=						domain_map



     def 					a__	(  self		,     _a				)						->							Tuple[torch.Tensor]:
           _A							:     Optional[Any]			=						[proj(_a				) for proj in self.proj]

           return self.domain_map(*_a				)







class 						lowercase							(   nn.Module		):

     def __init__(  self		,     _a				)						->							List[Any]:
           super().__init__()
           _A							:     Any			=						function



     def 					a__	(  self		,     _a		,     *_a				)						->							Optional[int]:
           return self.function(_a		,     *_a				)







class 						lowercase							:
     _a							 =						42
     _a							 =						42
     _a							 =						42

     def __init__(  self		,     _a = 1				)						->							None:
           _A							:     Tuple			=						dim
           _A							:     Tuple			=						{k: dim * self.args_dim[k] for k in self.args_dim}

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           if self.dim == 1:
                 return self.distribution_class(*_a				)
           else:
                 return Independent(self.distribution_class(*_a				)		,     1				)

     def 					a__	(  self		,     _a		,     _a = None		,     _a = None		,     )						->							Distribution:
           _A							:     Union[str, Any]			=						self._base_distribution(_a				)
           if loc is None and scale is None:
                 return distr
           else:
                 return AffineTransformed(_a		,     loc=_a		,     scale=_a		,     event_dim=self.event_dim				)

     @property
     def 					a__	(  self				)						->							Tuple:
           return () if self.dim == 1 else (self.dim,)

     @property
     def 					a__	(  self				)						->							int:
           return len(self.event_shape				)

     @property
     def 					a__	(  self				)						->							float:
           return 0.0

     def 					a__	(  self		,     _a				)						->							nn.Module:
           return ParameterProjection(
               in_features=_a		,     args_dim=self.args_dim		,     domain_map=LambdaLayer(self.domain_map				)		,     )

     def 					a__	(  self		,     *_a				)						->							Dict:
           raise NotImplementedError()



     @staticmethod
     def 					a__	(  _a				)						->							torch.Tensor:
           return (x + torch.sqrt(torch.square(_a				) + 4.0				)) / 2.0







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						{"df": 1, "loc": 1, "scale": 1}
     _a							 =						StudentT

     @classmethod
     def 					a__	(  cls		,     _a		,     _a		,     _a				)						->							int:
           _A							:     int			=						cls.squareplus(_a				).clamp_min(torch.finfo(scale.dtype				).eps				)
           _A							:     Any			=						2.0 + cls.squareplus(_a				)
           return df.squeeze(-1				), loc.squeeze(-1				), scale.squeeze(-1				)







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						{"loc": 1, "scale": 1}
     _a							 =						Normal

     @classmethod
     def 					a__	(  cls		,     _a		,     _a				)						->							List[Any]:
           _A							:     List[str]			=						cls.squareplus(_a				).clamp_min(torch.finfo(scale.dtype				).eps				)
           return loc.squeeze(-1				), scale.squeeze(-1				)







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						{"total_count": 1, "logits": 1}
     _a							 =						NegativeBinomial

     @classmethod
     def 					a__	(  cls		,     _a		,     _a				)						->							Union[str, Any]:
           _A							:     List[str]			=						cls.squareplus(_a				)
           return total_count.squeeze(-1				), logits.squeeze(-1				)

     def 					a__	(  self		,     _a				)						->							Distribution:
           _A			,   _A							:     Tuple			=						distr_args
           if self.dim == 1:
                 return self.distribution_class(total_count=_a		,     logits=_a				)
           else:
                 return Independent(self.distribution_class(total_count=_a		,     logits=_a				)		,     1				)



     def 					a__	(  self		,     _a		,     _a = None		,     _a = None				)						->							Distribution:
           _A			,   _A							:     List[Any]			=						distr_args

           if scale is not None:
                 # See scaling property of Gamma.
                 logits += scale.log()

           return self._base_distribution((total_count, logits)				)









"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
       from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[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(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sgugger/tiny-distilbert-classification'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       only_pretrain_model=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       torchscript=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __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(torch_device == '''cpu''',       '''Cant do half precision'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       fpaa=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     # set architectures equal to `None`
     __A									= None
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       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  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   @unittest.skipIf(torch_device == '''cpu''',       '''Can\'t do half precision'''  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       fpaa=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       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  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       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  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[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 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= PyTorchBenchmarkArguments(
           models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       save_to_csv=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       inference_time_csv_file=os.path.join(_lowerCamelCase,       '''inf_time.csv'''  ),       train_memory_csv_file=os.path.join(_lowerCamelCase,       '''train_mem.csv'''  ),       inference_memory_csv_file=os.path.join(_lowerCamelCase,       '''inf_mem.csv'''  ),       train_time_csv_file=os.path.join(_lowerCamelCase,       '''train_time.csv'''  ),       env_info_csv_file=os.path.join(_lowerCamelCase,       '''env.csv'''  ),       multi_process=_lowerCamelCase,       )
       __A									= PyTorchBenchmark(_lowerCamelCase  )
       benchmark.run()
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''inf_time.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''train_time.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''inf_mem.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''train_mem.csv'''  )  ).exists()  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''env.csv'''  )  ).exists()  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase :     List[Any]  ):
       self.assertTrue(hasattr(_lowerCamelCase,       '''sequential'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''cumulative'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''current'''  )  )
       self.assertTrue(hasattr(_lowerCamelCase,       '''total'''  )  )

     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= PyTorchBenchmarkArguments(
           models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       log_filename=os.path.join(_lowerCamelCase,       '''log.txt'''  ),       log_print=_lowerCamelCase,       trace_memory_line_by_line=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
       __A									= PyTorchBenchmark(_lowerCamelCase  )
       __A									= benchmark.run()
       _check_summary_is_not_empty(result.inference_summary  )
       _check_summary_is_not_empty(result.train_summary  )
       self.assertTrue(Path(os.path.join(_lowerCamelCase,       '''log.txt'''  )  ).exists()  )




