






'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					['image_processor', 'tokenizer']
		_UpperCamelCase				  =					'BlipImageProcessor'
		_UpperCamelCase				  =					'AutoTokenizer'






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )

								# add QFormer tokenizer
								lowerCamelCase__         =  qformer_tokenizer






		def __call__(      self   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = True   ,_lowerCAmelCase = False   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = 0   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = False   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								if images is None and text is None:
														raise ValueError("""You have to specify at least images or text."""       )

								lowerCamelCase__         =  BatchFeature()

								if text is not None:
														lowerCamelCase__         =  self.tokenizer(
														    text=_lowerCAmelCase   ,add_special_tokens=_lowerCAmelCase   ,padding=_lowerCAmelCase   ,truncation=_lowerCAmelCase   ,max_length=_lowerCAmelCase   ,stride=_lowerCAmelCase   ,pad_to_multiple_of=_lowerCAmelCase   ,return_attention_mask=_lowerCAmelCase   ,return_overflowing_tokens=_lowerCAmelCase   ,return_special_tokens_mask=_lowerCAmelCase   ,return_offsets_mapping=_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase   ,return_length=_lowerCAmelCase   ,verbose=_lowerCAmelCase   ,return_tensors=_lowerCAmelCase   ,**_lowerCAmelCase   ,)
														encoding.update(_lowerCAmelCase       )
														lowerCamelCase__         =  self.qformer_tokenizer(
														    text=_lowerCAmelCase   ,add_special_tokens=_lowerCAmelCase   ,padding=_lowerCAmelCase   ,truncation=_lowerCAmelCase   ,max_length=_lowerCAmelCase   ,stride=_lowerCAmelCase   ,pad_to_multiple_of=_lowerCAmelCase   ,return_attention_mask=_lowerCAmelCase   ,return_overflowing_tokens=_lowerCAmelCase   ,return_special_tokens_mask=_lowerCAmelCase   ,return_offsets_mapping=_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase   ,return_length=_lowerCAmelCase   ,verbose=_lowerCAmelCase   ,return_tensors=_lowerCAmelCase   ,**_lowerCAmelCase   ,)
														lowerCamelCase__         =  qformer_text_encoding.pop("""input_ids"""       )
														lowerCamelCase__         =  qformer_text_encoding.pop("""attention_mask"""       )

								if images is not None:
														lowerCamelCase__         =  self.image_processor(_lowerCAmelCase   ,return_tensors=_lowerCAmelCase       )
														encoding.update(_lowerCAmelCase       )

								return encoding






		def 			UpperCamelCase_					(      self   ,*_lowerCAmelCase   ,**_lowerCAmelCase       ):
								return self.tokenizer.batch_decode(*_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,*_lowerCAmelCase   ,**_lowerCAmelCase       ):
								return self.tokenizer.decode(*_lowerCAmelCase   ,**_lowerCAmelCase       )






		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.tokenizer.model_input_names
								lowerCamelCase__         =  self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,**_lowerCAmelCase       ):
								if os.path.isfile(_lowerCAmelCase       ):
														raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''       )
								os.makedirs(_lowerCAmelCase   ,exist_ok=_lowerCAmelCase       )
								lowerCamelCase__         =  os.path.join(_lowerCAmelCase   ,"""qformer_tokenizer"""       )
								self.qformer_tokenizer.save_pretrained(_lowerCAmelCase       )
								return super().save_pretrained(_lowerCAmelCase   ,**_lowerCAmelCase       )






		@classmethod
		def 			UpperCamelCase_					(      cls   ,_lowerCAmelCase   ,**_lowerCAmelCase       ):
								lowerCamelCase__         =  AutoTokenizer.from_pretrained(_lowerCAmelCase   ,subfolder="""qformer_tokenizer"""       )
								lowerCamelCase__         =  cls._get_arguments_from_pretrained(_lowerCAmelCase   ,**_lowerCAmelCase       )
								args.append(_lowerCAmelCase       )
								return cls(*_lowerCAmelCase       )









'''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			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result["""bs"""]   ,model_result["""ss"""]       ):
																				lowerCamelCase__         =  model_result["""result"""][batch_size][sequence_length]
																				self.assertIsNotNone(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sgugger/tiny-distilbert-classification"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,only_pretrain_model=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,eager_mode=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,[config]       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """patrickvonplaten/t5-tiny-random"""
								lowerCamelCase__         =  AutoConfig.from_pretrained(_lowerCAmelCase       )
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase   ,configs=[config]       )
								lowerCamelCase__         =  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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								lowerCamelCase__         =  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,training=_lowerCAmelCase   ,inference=_lowerCAmelCase   ,sequence_lengths=[8]   ,batch_sizes=[1]   ,use_xla=_lowerCAmelCase   ,multi_process=_lowerCAmelCase   ,)
								lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
								lowerCamelCase__         =  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       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__         =  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   ,)
														lowerCamelCase__         =  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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_lowerCAmelCase       ):
														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:
														lowerCamelCase__         =  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   ,)
														lowerCamelCase__         =  TensorFlowBenchmark(_lowerCAmelCase       )
														lowerCamelCase__         =  benchmark.run()
														_check_summary_is_not_empty(result.inference_summary       )
														self.assertTrue(Path(os.path.join(_lowerCAmelCase   ,"""log.txt"""       )       ).exists()       )

