





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 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   str		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,    model_result["""ss"""]		):
             __snake_case:						str			  =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						List[str]			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Optional[int]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						str			  =						PyTorchBenchmark(A		)
         __snake_case:						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 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						str			  =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						List[str]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    only_pretrain_model=A							,    )
         __snake_case:						Optional[Any]			  =						PyTorchBenchmark(A		)
         __snake_case:						Tuple			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Dict			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[str]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    torchscript=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						Tuple			  =						PyTorchBenchmark(A		)
         __snake_case:						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 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						str			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Dict			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    fpaa=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						Union[str, Any]			  =						PyTorchBenchmark(A		)
         __snake_case:						Dict			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						List[str]			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]			  =						AutoConfig.from_pretrained(A		)
         # set architectures equal to `None`
         __snake_case:						Union[str, Any]			  =						None
         __snake_case:						Any			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						List[Any]			  =						PyTorchBenchmark(A							,    configs=[config]		)
         __snake_case:						Tuple			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						str			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						str			  =						PyTorchBenchmark(A		)
         __snake_case:						Tuple			  =						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 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						List[str]			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[str]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    fpaa=A							,    multi_process=A							,    )
         __snake_case:						int			  =						PyTorchBenchmark(A		)
         __snake_case:						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 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Optional[int]			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple			  =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						List[Any]			  =						PyTorchBenchmark(A							,    configs=[config]		)
         __snake_case:						Tuple			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Tuple			  =						"""sshleifer/tinier_bart"""
         __snake_case:						Optional[int]			  =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						Union[str, Any]			  =						PyTorchBenchmark(A							,    configs=[config]		)
         __snake_case:						Tuple			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Tuple			  =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]			  =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						int			  =						PyTorchBenchmark(A							,    configs=[config]		)
         __snake_case:						List[Any]			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Tuple			  =						"""sshleifer/tinier_bart"""
         __snake_case:						List[Any]			  =						AutoConfig.from_pretrained(A		)
         __snake_case:						Tuple			  =						PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    multi_process=A							,    )
         __snake_case:						Optional[Any]			  =						PyTorchBenchmark(A							,    configs=[config]		)
         __snake_case:						int			  =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




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






       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						int			  =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A			:   List[str]		):
           self.assertTrue(hasattr(A							,    """sequential"""		)		)
           self.assertTrue(hasattr(A							,    """cumulative"""		)		)
           self.assertTrue(hasattr(A							,    """current"""		)		)
           self.assertTrue(hasattr(A							,    """total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Optional[int]			  =						PyTorchBenchmarkArguments(
               models=[MODEL_ID]							,    training=A							,    inference=A							,    sequence_lengths=[8]							,    batch_sizes=[1]							,    log_filename=os.path.join(A							,    """log.txt"""		)							,    log_print=A							,    trace_memory_line_by_line=A							,    multi_process=A							,    )
           __snake_case:						List[Any]			  =						PyTorchBenchmark(A		)
           __snake_case:						List[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(A							,    """log.txt"""		)		).exists()		)













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






