




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class        __snake_case  (nn.Module		):



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[int]=0.0		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		str = "geglu"		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		str = "layer_norm"		,	_UpperCAmelCase      :		bool = False		,	)  ->    Tuple:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Dict										=      only_cross_attention

								_lowerCAmelCase    :    Union[str, Any]										=      (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
								_lowerCAmelCase    :    Optional[int]										=      (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

								if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
												raise ValueError(
												    f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
												    f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."      )

								# Define 3 blocks. Each block has its own normalization layer.
								# 1. Self-Attn
								if self.use_ada_layer_norm:
												_lowerCAmelCase    :    Union[str, Any]										=      AdaLayerNorm(UpperCAmelCase_		,	UpperCAmelCase_      )
								elif self.use_ada_layer_norm_zero:
												_lowerCAmelCase    :    Optional[int]										=      AdaLayerNormZero(UpperCAmelCase_		,	UpperCAmelCase_      )
								else:
												_lowerCAmelCase    :    Tuple										=      nn.LayerNorm(UpperCAmelCase_		,	elementwise_affine=UpperCAmelCase_      )
								_lowerCAmelCase    :    Optional[Any]										=      Attention(
								    query_dim=UpperCAmelCase_		,	heads=UpperCAmelCase_		,	dim_head=UpperCAmelCase_		,	dropout=UpperCAmelCase_		,	bias=UpperCAmelCase_		,	cross_attention_dim=cross_attention_dim if only_cross_attention else None		,	upcast_attention=UpperCAmelCase_		,	)

								# 2. Cross-Attn
								if cross_attention_dim is not None or double_self_attention:
												# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
												# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
												# the second cross attention block.
												_lowerCAmelCase    :    List[str]										=      (
												    AdaLayerNorm(UpperCAmelCase_		,	UpperCAmelCase_      )
												    if self.use_ada_layer_norm
												    else nn.LayerNorm(UpperCAmelCase_		,	elementwise_affine=UpperCAmelCase_      )
												)
												_lowerCAmelCase    :    List[str]										=      Attention(
												    query_dim=UpperCAmelCase_		,	cross_attention_dim=cross_attention_dim if not double_self_attention else None		,	heads=UpperCAmelCase_		,	dim_head=UpperCAmelCase_		,	dropout=UpperCAmelCase_		,	bias=UpperCAmelCase_		,	upcast_attention=UpperCAmelCase_		,	)  # is self-attn if encoder_hidden_states is none
								else:
												_lowerCAmelCase    :    List[Any]										=      None
												_lowerCAmelCase    :    Any										=      None

								# 3. Feed-forward
								_lowerCAmelCase    :    Union[str, Any]										=      nn.LayerNorm(UpperCAmelCase_		,	elementwise_affine=UpperCAmelCase_      )
								_lowerCAmelCase    :    List[Any]										=      FeedForward(UpperCAmelCase_		,	dropout=UpperCAmelCase_		,	activation_fn=UpperCAmelCase_		,	final_dropout=UpperCAmelCase_      )

								# let chunk size default to None
								_lowerCAmelCase    :    Dict										=      None
								_lowerCAmelCase    :    str										=      0



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      chunk_size
								_lowerCAmelCase    :    List[str]										=      dim



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		torch.FloatTensor		,	_UpperCAmelCase      :		Optional[torch.FloatTensor] = None		,	_UpperCAmelCase      :		Optional[torch.FloatTensor] = None		,	_UpperCAmelCase      :		Optional[torch.FloatTensor] = None		,	_UpperCAmelCase      :		Optional[torch.LongTensor] = None		,	_UpperCAmelCase      :		Dict[str, Any] = None		,	_UpperCAmelCase      :		Optional[torch.LongTensor] = None		,	)  ->    Union[str, Any]:





								'''simple docstring'''




								if self.use_ada_layer_norm:
												_lowerCAmelCase    :    Union[str, Any]										=      self.norma(UpperCAmelCase_		,	UpperCAmelCase_      )
								elif self.use_ada_layer_norm_zero:
												_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self.norma(
												    UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	hidden_dtype=hidden_states.dtype      )
								else:
												_lowerCAmelCase    :    Tuple										=      self.norma(UpperCAmelCase_      )

								_lowerCAmelCase    :    List[str]										=      cross_attention_kwargs if cross_attention_kwargs is not None else {}

								_lowerCAmelCase    :    Tuple										=      self.attna(
								    UpperCAmelCase_		,	encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None		,	attention_mask=UpperCAmelCase_		,	**UpperCAmelCase_		,	)
								if self.use_ada_layer_norm_zero:
												_lowerCAmelCase    :    Optional[Any]										=      gate_msa.unsqueeze(1      ) * attn_output
								_lowerCAmelCase    :    List[str]										=      attn_output + hidden_states

								# 2. Cross-Attention
								if self.attna is not None:
												_lowerCAmelCase    :    Dict										=      (
												    self.norma(UpperCAmelCase_		,	UpperCAmelCase_      ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase_      )
												)

												_lowerCAmelCase    :    int										=      self.attna(
												    UpperCAmelCase_		,	encoder_hidden_states=UpperCAmelCase_		,	attention_mask=UpperCAmelCase_		,	**UpperCAmelCase_		,	)
												_lowerCAmelCase    :    Optional[Any]										=      attn_output + hidden_states

								# 3. Feed-forward
								_lowerCAmelCase    :    Tuple										=      self.norma(UpperCAmelCase_      )

								if self.use_ada_layer_norm_zero:
												_lowerCAmelCase    :    Dict										=      norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

								if self._chunk_size is not None:
												# "feed_forward_chunk_size" can be used to save memory
												if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																raise ValueError(
																    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."      )

												_lowerCAmelCase    :    List[str]										=      norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
												_lowerCAmelCase    :    Union[str, Any]										=      torch.cat(
												    [self.ff(UpperCAmelCase_      ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase_		,	dim=self._chunk_dim      )]		,	dim=self._chunk_dim		,	)
								else:
												_lowerCAmelCase    :    Tuple										=      self.ff(UpperCAmelCase_      )

								if self.use_ada_layer_norm_zero:
												_lowerCAmelCase    :    List[str]										=      gate_mlp.unsqueeze(1      ) * ff_output

								_lowerCAmelCase    :    Any										=      ff_output + hidden_states

								return hidden_states






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		int = 4		,	_UpperCAmelCase      :		float = 0.0		,	_UpperCAmelCase      :		str = "geglu"		,	_UpperCAmelCase      :		bool = False		,	)  ->    List[str]:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Union[str, Any]										=      int(dim * mult      )
								_lowerCAmelCase    :    Optional[int]										=      dim_out if dim_out is not None else dim

								if activation_fn == "gelu":
												_lowerCAmelCase    :    List[Any]										=      GELU(UpperCAmelCase_		,	UpperCAmelCase_      )
								if activation_fn == "gelu-approximate":
												_lowerCAmelCase    :    Any										=      GELU(UpperCAmelCase_		,	UpperCAmelCase_		,	approximate="""tanh"""      )
								elif activation_fn == "geglu":
												_lowerCAmelCase    :    List[str]										=      GEGLU(UpperCAmelCase_		,	UpperCAmelCase_      )
								elif activation_fn == "geglu-approximate":
												_lowerCAmelCase    :    Union[str, Any]										=      ApproximateGELU(UpperCAmelCase_		,	UpperCAmelCase_      )

								_lowerCAmelCase    :    List[str]										=      nn.ModuleList([]      )
								# project in
								self.net.append(UpperCAmelCase_      )
								# project dropout
								self.net.append(nn.Dropout(UpperCAmelCase_      )      )
								# project out
								self.net.append(nn.Linear(UpperCAmelCase_		,	UpperCAmelCase_      )      )
								# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
								if final_dropout:
												self.net.append(nn.Dropout(UpperCAmelCase_      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		int      )  ->    Any:





								'''simple docstring'''




								for module in self.net:
												_lowerCAmelCase    :    str										=      module(UpperCAmelCase_      )
								return hidden_states






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		str = "none"      )  ->    Dict:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Optional[int]										=      nn.Linear(UpperCAmelCase_		,	UpperCAmelCase_      )
								_lowerCAmelCase    :    Union[str, Any]										=      approximate



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Any      )  ->    int:





								'''simple docstring'''




								if gate.device.type != "mps":
												return F.gelu(UpperCAmelCase_		,	approximate=self.approximate      )
								# mps: gelu is not implemented for float16
								return F.gelu(gate.to(dtype=torch.floataa      )		,	approximate=self.approximate      ).to(dtype=gate.dtype      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      self.proj(UpperCAmelCase_      )
								_lowerCAmelCase    :    List[Any]										=      self.gelu(UpperCAmelCase_      )
								return hidden_states






class        __snake_case  (nn.Module		):



				def __init__(      self      :		List[Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Optional[Any]										=      nn.Linear(UpperCAmelCase_		,	dim_out * 2      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								if gate.device.type != "mps":
												return F.gelu(UpperCAmelCase_      )
								# mps: gelu is not implemented for float16
								return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      self.proj(UpperCAmelCase_      ).chunk(2		,	dim=-1      )
								return hidden_states * self.gelu(UpperCAmelCase_      )






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Optional[int]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    List[Any]										=      nn.Linear(UpperCAmelCase_		,	UpperCAmelCase_      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.proj(UpperCAmelCase_      )
								return x * torch.sigmoid(1.702 * x      )






class        __snake_case  (nn.Module		):



				def __init__(      self      :		Optional[int]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    str										=      nn.Embedding(UpperCAmelCase_		,	UpperCAmelCase_      )
								_lowerCAmelCase    :    List[Any]										=      nn.SiLU()
								_lowerCAmelCase    :    List[str]										=      nn.Linear(UpperCAmelCase_		,	embedding_dim * 2      )
								_lowerCAmelCase    :    str										=      nn.LayerNorm(UpperCAmelCase_		,	elementwise_affine=UpperCAmelCase_      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Tuple      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.linear(self.silu(self.emb(UpperCAmelCase_      )      )      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      torch.chunk(UpperCAmelCase_		,	2      )
								_lowerCAmelCase    :    Optional[int]										=      self.norm(UpperCAmelCase_      ) * (1 + scale) + shift
								return x






class        __snake_case  (nn.Module		):



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		List[str]      )  ->    Tuple:





								'''simple docstring'''




								super().__init__()

								_lowerCAmelCase    :    Any										=      CombinedTimestepLabelEmbeddings(UpperCAmelCase_		,	UpperCAmelCase_      )

								_lowerCAmelCase    :    Optional[Any]										=      nn.SiLU()
								_lowerCAmelCase    :    Union[str, Any]										=      nn.Linear(UpperCAmelCase_		,	6 * embedding_dim		,	bias=UpperCAmelCase_      )
								_lowerCAmelCase    :    Tuple										=      nn.LayerNorm(UpperCAmelCase_		,	elementwise_affine=UpperCAmelCase_		,	eps=1E-6      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Any=None      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.linear(self.silu(self.emb(UpperCAmelCase_		,	UpperCAmelCase_		,	hidden_dtype=UpperCAmelCase_      )      )      )
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      emb.chunk(6		,	dim=1      )
								_lowerCAmelCase    :    Tuple										=      self.norm(UpperCAmelCase_      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
								return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class        __snake_case  (nn.Module		):



				def __init__(      self      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[str] = None		,	_UpperCAmelCase      :		float = 1E-5      )  ->    str:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Union[str, Any]										=      num_groups
								_lowerCAmelCase    :    Optional[Any]										=      eps

								if act_fn is None:
												_lowerCAmelCase    :    Union[str, Any]										=      None
								else:
												_lowerCAmelCase    :    Any										=      get_activation(UpperCAmelCase_      )

								_lowerCAmelCase    :    Optional[Any]										=      nn.Linear(UpperCAmelCase_		,	out_dim * 2      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								if self.act:
												_lowerCAmelCase    :    Union[str, Any]										=      self.act(UpperCAmelCase_      )
								_lowerCAmelCase    :    Optional[int]										=      self.linear(UpperCAmelCase_      )
								_lowerCAmelCase    :    Dict										=      emb[:, :, None, None]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      emb.chunk(2		,	dim=1      )

								_lowerCAmelCase    :    Any										=      F.group_norm(UpperCAmelCase_		,	self.num_groups		,	eps=self.eps      )
								_lowerCAmelCase    :    Tuple										=      x * (1 + scale) + shift
								return x







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		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["""bs"""]		,	model_result["""ss"""]      ):
																_lowerCAmelCase    :    List[Any]										=      model_result["""result"""][batch_size][sequence_length]
																self.assertIsNotNone(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      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[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """sgugger/tiny-distilbert-classification"""
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	only_pretrain_model=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      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      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[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(torch_device == """cpu"""		,	"""Cant do half precision"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	fpaa=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      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      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    int										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								# set architectures equal to `None`
								_lowerCAmelCase    :    Optional[int]										=      None
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    List[Any]										=      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      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      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      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	fpaa=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      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      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[Any]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Optional[int]										=      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      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Tuple										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Any										=      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[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[int]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    Optional[Any]										=      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      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    int										=      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      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase    :    Optional[int]										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	save_to_csv=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	inference_time_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )		,	train_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )		,	inference_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )		,	train_time_csv_file=os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )		,	env_info_csv_file=os.path.join(_UpperCAmelCase		,	"""env.csv"""      )		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
												benchmark.run()
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""env.csv"""      )      ).exists()      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_UpperCAmelCase      :		int      ):
												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:
												_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	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		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
												_lowerCAmelCase    :    Any										=      benchmark.run()
												_check_summary_is_not_empty(result.inference_summary      )
												_check_summary_is_not_empty(result.train_summary      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""log.txt"""      )      ).exists()      )

