




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		):







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						Tuple):

        """simple docstring"""


        for model_result in results.values():
             for batch_size, sequence_length in zip(model_result['''bs''']					,						model_result['''ss''']):
                  UpperCAmelCase_          =							model_result['''result'''][batch_size][sequence_length]
                  self.assertIsNotNone(lowerCAmelCase__)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''sgugger/tiny-distilbert-classification'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						only_pretrain_model=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						torchscript=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						fpaa=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							AutoConfig.from_pretrained(lowerCAmelCase__)
        # set architectures equal to `None`
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__					,						configs=[config])
        UpperCAmelCase_          =							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		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						fpaa=lowerCAmelCase__					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
        UpperCAmelCase_          =							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		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							AutoConfig.from_pretrained(lowerCAmelCase__)
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__					,						configs=[config])
        UpperCAmelCase_          =							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		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tinier_bart'''
        UpperCAmelCase_          =							AutoConfig.from_pretrained(lowerCAmelCase__)
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__					,						configs=[config])
        UpperCAmelCase_          =							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		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        UpperCAmelCase_          =							AutoConfig.from_pretrained(lowerCAmelCase__)
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__					,						configs=[config])
        UpperCAmelCase_          =							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		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tinier_bart'''
        UpperCAmelCase_          =							AutoConfig.from_pretrained(lowerCAmelCase__)
        UpperCAmelCase_          =							PyTorchBenchmarkArguments(
            models=[MODEL_ID]					,						training=lowerCAmelCase__					,						inference=lowerCAmelCase__					,						sequence_lengths=[8]					,						batch_sizes=[1]					,						multi_process=lowerCAmelCase__					,						)
        UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__					,						configs=[config])
        UpperCAmelCase_          =							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		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''
        with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCAmelCase_          =							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__					,						)
             UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							'''sshleifer/tiny-gpt2'''

        def _check_summary_is_not_empty(_snake_case		:						Optional[int]):
             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:
             UpperCAmelCase_          =							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__					,						)
             UpperCAmelCase_          =							PyTorchBenchmark(lowerCAmelCase__)
             UpperCAmelCase_          =							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 json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=7						,       lowerCAmelCase__=3						,       lowerCAmelCase__=18						,       lowerCAmelCase__=30						,       lowerCAmelCase__=400						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       )     ->							int:
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'height': 18, 'width': 18}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize







		def 							__A						(	self				)     ->							Optional[Any]:
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
								            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
								        ]				),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}





@require_torch
@require_vision
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ImageGPTImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'clusters'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_resize'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'size'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_normalize'				)				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'height': 18, 'width': 18}				)

								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict						,       size=42				)
								self.assertEqual(image_processor.size						,       {'height': 42, 'width': 42}				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								SCREAMING_SNAKE_CASE													=					json.loads(image_processor.to_json_string()				)
								for key, value in self.image_processor_dict.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       obj[key]				)				)
														else:
																				self.assertEqual(obj[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       'image_processor.json'				)
														image_processor_first.to_json_file(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_json_file(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_pretrained(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		@unittest.skip('ImageGPT requires clusters at initialization'				)
		def 							__A						(	self				)     ->							Optional[Any]:
								pass





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					load_dataset('hf-internal-testing/fixtures_image_utils' ,			split='test'  )

						SCREAMING_SNAKE_CASE													=					Image.open(dataset[4]['file']  )
						SCREAMING_SNAKE_CASE													=					Image.open(dataset[5]['file']  )

						SCREAMING_SNAKE_CASE													=					[imagea, imagea]

						return images


@require_vision
@require_torch
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

								SCREAMING_SNAKE_CASE													=					prepare_images()

								# test non-batched
								SCREAMING_SNAKE_CASE													=					image_processing(images[0]						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (1, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[306, 191, 191]
								self.assertEqual(encoding.input_ids[0, :3].tolist()						,       lowerCAmelCase__				)

								# test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (2, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[303, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()						,       lowerCAmelCase__				)


