
"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				tempfile.mkdtemp()

			__lowerCAmelCase      :						Any						      =				BlipImageProcessor()
			__lowerCAmelCase      :						List[str]						      =				BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'			)

			__lowerCAmelCase      :						Tuple						      =				BlipProcessor(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			processor.save_pretrained(self.tmpdirname			)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return AutoProcessor.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			).tokenizer

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			return AutoProcessor.from_pretrained(self.tmpdirname	,		**_SCREAMING_SNAKE_CASE			).image_processor

		def 							__lowerCamelCase							(				self			):
			shutil.rmtree(self.tmpdirname			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				[np.random.randint(2_55	,		size=(3, 30, 4_00)	,		dtype=np.uinta			)]

			__lowerCAmelCase      :						List[str]						      =				[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE	,		0	,		-1			)			) for x in image_inputs]

			return image_inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				BlipProcessor(tokenizer=self.get_tokenizer()	,		image_processor=self.get_image_processor()			)
			processor.save_pretrained(self.tmpdirname			)

			__lowerCAmelCase      :						Union[str, Any]						      =				self.get_tokenizer(bos_token='(BOS)'	,		eos_token='(EOS)'			)
			__lowerCAmelCase      :						int						      =				self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

			__lowerCAmelCase      :						Dict						      =				BlipProcessor.from_pretrained(
			    self.tmpdirname	,		bos_token='(BOS)'	,		eos_token='(EOS)'	,		do_normalize=_SCREAMING_SNAKE_CASE	,		padding_value=1.0			)

			self.assertEqual(processor.tokenizer.get_vocab()	,		tokenizer_add_kwargs.get_vocab()			)
			self.assertIsInstance(processor.tokenizer	,		_SCREAMING_SNAKE_CASE			)

			self.assertEqual(processor.image_processor.to_json_string()	,		image_processor_add_kwargs.to_json_string()			)
			self.assertIsInstance(processor.image_processor	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.get_image_processor()
			__lowerCAmelCase      :						List[str]						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Optional[Any]						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Dict						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						List[str]						      =				image_processor(_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)
			__lowerCAmelCase      :						Optional[int]						      =				processor(images=_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)

			for key in input_feat_extract.keys():
				self.assertAlmostEqual(input_feat_extract[key].sum()	,		input_processor[key].sum()	,		delta=1E-2			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				self.get_image_processor()
			__lowerCAmelCase      :						int						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Tuple						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				'lower newer'

			__lowerCAmelCase      :						List[str]						      =				processor(text=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				tokenizer(_SCREAMING_SNAKE_CASE	,		return_token_type_ids=_SCREAMING_SNAKE_CASE			)

			for key in encoded_tok.keys():
				self.assertListEqual(encoded_tok[key]	,		encoded_processor[key]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				self.get_image_processor()
			__lowerCAmelCase      :						Any						      =				self.get_tokenizer()

			__lowerCAmelCase      :						Tuple						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[Any]						      =				'lower newer'
			__lowerCAmelCase      :						Tuple						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						List[str]						      =				processor(text=_SCREAMING_SNAKE_CASE	,		images=_SCREAMING_SNAKE_CASE			)

			self.assertListEqual(list(inputs.keys()			)	,		['pixel_values', 'input_ids', 'attention_mask']			)

			# test if it raises when no input is passed
			with pytest.raises(_SCREAMING_SNAKE_CASE			):
				processor()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.get_image_processor()
			__lowerCAmelCase      :						Dict						      =				self.get_tokenizer()

			__lowerCAmelCase      :						int						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowerCAmelCase      :						Tuple						      =				processor.batch_decode(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			)

			self.assertListEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.get_image_processor()
			__lowerCAmelCase      :						List[Any]						      =				self.get_tokenizer()

			__lowerCAmelCase      :						List[Any]						      =				BlipProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,		image_processor=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						int						      =				'lower newer'
			__lowerCAmelCase      :						List[str]						      =				self.prepare_image_inputs()

			__lowerCAmelCase      :						int						      =				processor(text=_SCREAMING_SNAKE_CASE	,		images=_SCREAMING_SNAKE_CASE			)

			# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
			self.assertListEqual(list(inputs.keys()			)	,		['pixel_values', 'input_ids', 'attention_mask']			)

"""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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			for model_result in results.values():
				for batch_size, sequence_length in zip(model_result['bs']	,		model_result['ss']			):
					__lowerCAmelCase      :						Optional[Any]						      =				model_result['result'][batch_size][sequence_length]
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				'sgugger/tiny-distilbert-classification'
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		only_pretrain_model=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Tuple						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Any						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'patrickvonplaten/t5-tiny-random'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		configs=[config]			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		use_xla=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'sshleifer/tiny-gpt2'

			def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE			):
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'sequential'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'cumulative'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'current'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'total'			)			)

			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		log_filename=os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)	,		log_print=_SCREAMING_SNAKE_CASE	,		trace_memory_line_by_line=_SCREAMING_SNAKE_CASE	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				benchmark.run()
				_check_summary_is_not_empty(result.inference_summary			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)			).exists()			)