
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        UpperCamelCase				(							self       ,							A__      )    ->			Tuple:



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






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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]:
          _SCREAMING_SNAKE_CASE      =				"sgugger/tiny-distilbert-classification"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							only_pretrain_model=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							torchscript=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							fpaa=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(__lowerCamelCase      )
          # set architectures equal to `None`
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase       ,							configs=[config]      )
          _SCREAMING_SNAKE_CASE      =				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:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							fpaa=__lowerCamelCase       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				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      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase       ,							configs=[config]      )
          _SCREAMING_SNAKE_CASE      =				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[Any]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tinier_bart"
          _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase       ,							configs=[config]      )
          _SCREAMING_SNAKE_CASE      =				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:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase       ,							configs=[config]      )
          _SCREAMING_SNAKE_CASE      =				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      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tinier_bart"
          _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmarkArguments(
              models=[MODEL_ID]       ,							training=__lowerCamelCase       ,							inference=__lowerCamelCase       ,							sequence_lengths=[8]       ,							batch_sizes=[1]       ,							multi_process=__lowerCamelCase       ,							)
          _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase       ,							configs=[config]      )
          _SCREAMING_SNAKE_CASE      =				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      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"
          with tempfile.TemporaryDirectory() as tmp_dir:
             _SCREAMING_SNAKE_CASE      =				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       ,							)
             _SCREAMING_SNAKE_CASE      =				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        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				"sshleifer/tiny-gpt2"

          def _check_summary_is_not_empty(A__      ):
             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:
             _SCREAMING_SNAKE_CASE      =				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       ,							)
             _SCREAMING_SNAKE_CASE      =				PyTorchBenchmark(__lowerCamelCase      )
             _SCREAMING_SNAKE_CASE      =				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()      )








'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'ChineseCLIPImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`."""      )
          if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`."""      )

          super().__init__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:

          if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

          if text is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

          if text is not None and images is not None:
             _SCREAMING_SNAKE_CASE      =				image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






