






"""simple docstring"""



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf

						from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[Any]    ,						A     :						Union[str, Any]   )  ->  Dict:
							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['''bs''']    ,						model_result['''ss''']   ):
											lowercase_ : Any							       =   model_result["result"][batch_size][sequence_length]
											self.assertIsNotNone(__lowerCamelCase   )




					def   A  (				self     :						Any   )  ->  Tuple:
							lowercase_ : List[str]							       =   "sshleifer/tiny-gpt2"
							lowercase_ : Tuple							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=__lowerCamelCase    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Any							       =   TensorFlowBenchmark(__lowerCamelCase   )
							lowercase_ : List[str]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							lowercase_ : Union[str, Any]							       =   "sgugger/tiny-distilbert-classification"
							lowercase_ : List[str]							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						only_pretrain_model=__lowerCamelCase    ,						)
							lowercase_ : List[Any]							       =   TensorFlowBenchmark(__lowerCamelCase   )
							lowercase_ : Dict							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   "sshleifer/tiny-gpt2"
							lowercase_ : List[str]							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Union[str, Any]							       =   TensorFlowBenchmark(__lowerCamelCase   )
							lowercase_ : List[str]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Tuple   )  ->  Optional[int]:
							lowercase_ : Optional[Any]							       =   "sshleifer/tiny-gpt2"
							lowercase_ : str							       =   AutoConfig.from_pretrained(__lowerCamelCase   )
							lowercase_ : List[str]							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=__lowerCamelCase    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Union[str, Any]							       =   TensorFlowBenchmark(__lowerCamelCase    ,						[config]   )
							lowercase_ : 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   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   "sshleifer/tiny-gpt2"
							lowercase_ : List[Any]							       =   AutoConfig.from_pretrained(__lowerCamelCase   )
							lowercase_ : Optional[int]							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Dict							       =   TensorFlowBenchmark(__lowerCamelCase    ,						[config]   )
							lowercase_ : Any							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : str							       =   "sshleifer/tiny-gpt2"
							lowercase_ : int							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Union[str, Any]							       =   TensorFlowBenchmark(__lowerCamelCase   )
							lowercase_ : 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   A  (				self     :						Tuple   )  ->  List[str]:
							lowercase_ : str							       =   "sshleifer/tiny-gpt2"
							lowercase_ : Optional[int]							       =   AutoConfig.from_pretrained(__lowerCamelCase   )
							lowercase_ : Any							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : Union[str, Any]							       =   TensorFlowBenchmark(__lowerCamelCase    ,						[config]   )
							lowercase_ : Optional[int]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					def   A  (				self     :						str   )  ->  Union[str, Any]:
							lowercase_ : Optional[Any]							       =   "patrickvonplaten/t5-tiny-random"
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(__lowerCamelCase   )
							lowercase_ : List[str]							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : List[str]							       =   TensorFlowBenchmark(__lowerCamelCase    ,						configs=[config]   )
							lowercase_ : 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(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''   )   ) == 0    ,						'''Cannot do xla on CPU.'''   )
					def   A  (				self     :						Dict   )  ->  str:
							lowercase_ : Dict							       =   "sshleifer/tiny-gpt2"
							lowercase_ : Dict							       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=__lowerCamelCase    ,						inference=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=__lowerCamelCase    ,						multi_process=__lowerCamelCase    ,						)
							lowercase_ : List[Any]							       =   TensorFlowBenchmark(__lowerCamelCase   )
							lowercase_ : str							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						str   )  ->  Optional[int]:
							lowercase_ : Dict							       =   "sshleifer/tiny-gpt2"
							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_ : List[Any]							       =   TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]    ,						inference=__lowerCamelCase    ,						save_to_csv=__lowerCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(__lowerCamelCase    ,						'''inf_time.csv'''   )    ,						inference_memory_csv_file=os.path.join(__lowerCamelCase    ,						'''inf_mem.csv'''   )    ,						env_info_csv_file=os.path.join(__lowerCamelCase    ,						'''env.csv'''   )    ,						multi_process=__lowerCamelCase    ,						)
									lowercase_ : Union[str, Any]							       =   TensorFlowBenchmark(__lowerCamelCase   )
									benchmark.run()
									self.assertTrue(Path(os.path.join(__lowerCamelCase    ,						'''inf_time.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(__lowerCamelCase    ,						'''inf_mem.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(__lowerCamelCase    ,						'''env.csv'''   )   ).exists()   )






					def   A  (				self     :						Any   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   "sshleifer/tiny-gpt2"

							def _check_summary_is_not_empty(A     :						Dict   ):
									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:
									lowercase_ : Union[str, Any]							       =   TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]    ,						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    ,						eager_mode=__lowerCamelCase    ,						multi_process=__lowerCamelCase    ,						)
									lowercase_ : Optional[int]							       =   TensorFlowBenchmark(__lowerCamelCase   )
									lowercase_ : Union[str, Any]							       =   benchmark.run()
									_check_summary_is_not_empty(result.inference_summary   )
									self.assertTrue(Path(os.path.join(__lowerCamelCase    ,						'''log.txt'''   )   ).exists()   )







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = 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__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            if text is None and query_images is None and images is None:
                   raise ValueError(
                       "You have to specify at least one text or query image or image. All three cannot be none.")

            if text is not None:
                   if isinstance(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = np.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "jax" and is_flax_available():
                          import jax.numpy as jnp

                          _A      :       Optional[int]				    = jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[int]				    = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "pt" and is_torch_available():
                          import torch

                          _A      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, Any]				    = torch.cat([encoding["attention_mask"] for encoding in encodings] ,						dim=0)

                   elif return_tensors == "tf" and is_tf_available():
                          import tensorflow as tf

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   else:
                          raise ValueError("Target return tensor type could not be returned")

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       int				    = image_features.pixel_values
                   return encoding
            elif text is not None or query_images is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


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


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
