

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput


class 			A						(							nn.Module			):







				"""simple docstring"""





				def __init__(				self			:	Optional[int],lowercase_			:	str = 1_6,lowercase_			:	List[Any] = 8_8,lowercase_			:	Dict = None,lowercase_			:	List[str] = 1,lowercase_			:	Union[str, Any] = 0.0,lowercase_			:	Optional[int] = 3_2,lowercase_			:	Union[str, Any] = None,lowercase_			:	Tuple = False,lowercase_			:	Union[str, Any] = None,lowercase_			:	List[Any] = None,lowercase_			:	Optional[Any] = "geglu",lowercase_			:	Any = None,)->   Union[str, Any]:





								'''simple docstring'''







								super().__init__()
								A__   				=       nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=SCREAMING_SNAKE_CASE_,attention_head_dim=SCREAMING_SNAKE_CASE_,in_channels=SCREAMING_SNAKE_CASE_,num_layers=SCREAMING_SNAKE_CASE_,dropout=SCREAMING_SNAKE_CASE_,norm_num_groups=SCREAMING_SNAKE_CASE_,cross_attention_dim=SCREAMING_SNAKE_CASE_,attention_bias=SCREAMING_SNAKE_CASE_,sample_size=SCREAMING_SNAKE_CASE_,num_vector_embeds=SCREAMING_SNAKE_CASE_,activation_fn=SCREAMING_SNAKE_CASE_,num_embeds_ada_norm=SCREAMING_SNAKE_CASE_,)
								        for _ in range(2		)
								    ]		)

								# Variables that can be set by a pipeline:

								# The ratio of transformer1 to transformer2's output states to be combined during inference
								A__   				=       0.5

								# The shape of `encoder_hidden_states` is expected to be
								# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
								A__   				=       [7_7, 2_5_7]

								# Which transformer to use to encode which condition.
								# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
								A__   				=       [1, 0]


				def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Optional[int],lowercase_			:	str,lowercase_			:	str=None,lowercase_			:	Dict=None,lowercase_			:	List[Any]=None,lowercase_			:	Tuple = True,)->   List[str]:





								'''simple docstring'''







								A__   				=       hidden_states

								A__   				=       []
								A__   				=       0
								# attention_mask is not used yet
								for i in range(2		):
												# for each of the two transformers, pass the corresponding condition tokens
												A__   				=       encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
												A__   				=       self.transformer_index_for_condition[i]
												A__   				=       self.transformers[transformer_index](
												    SCREAMING_SNAKE_CASE_,encoder_hidden_states=SCREAMING_SNAKE_CASE_,timestep=SCREAMING_SNAKE_CASE_,cross_attention_kwargs=SCREAMING_SNAKE_CASE_,return_dict=SCREAMING_SNAKE_CASE_,)[0]
												encoded_states.append(encoded_state - input_states		)
												tokens_start += self.condition_lengths[i]

								A__   				=       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
								A__   				=       output_states + input_states

								if not return_dict:
												return (output_states,)

								return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_		)




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 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''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(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        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 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,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 						snake_case__					(				self			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        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 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,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 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,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 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,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 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,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 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            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__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,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_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            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(lowercase_,'log.txt'		)		).exists()		)

