





'''simple docstring'''




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			):




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[Any]    ):
      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(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "sgugger/tiny-distilbert-classification"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			only_pretrain_model=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			torchscript=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _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      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			fpaa=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      # set architectures equal to `None`
      _UpperCAmelCase     							=   None
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			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      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _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      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			fpaa=snake_case_	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result    )
      self.check_results_dict_not_empty(results.memory_train_result    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			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      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "sshleifer/tinier_bart"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			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      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			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      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "sshleifer/tinier_bart"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			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      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      with tempfile.TemporaryDirectory() as tmp_dir:
           _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			save_to_csv=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			inference_time_csv_file=os.path.join(snake_case_	,			"inf_time.csv"    )	,			train_memory_csv_file=os.path.join(snake_case_	,			"train_mem.csv"    )	,			inference_memory_csv_file=os.path.join(snake_case_	,			"inf_mem.csv"    )	,			train_time_csv_file=os.path.join(snake_case_	,			"train_time.csv"    )	,			env_info_csv_file=os.path.join(snake_case_	,			"env.csv"    )	,			multi_process=snake_case_	,			)
           _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
           benchmark.run()
           self.assertTrue(Path(os.path.join(snake_case_	,			"inf_time.csv"    )    ).exists()    )
           self.assertTrue(Path(os.path.join(snake_case_	,			"train_time.csv"    )    ).exists()    )
           self.assertTrue(Path(os.path.join(snake_case_	,			"inf_mem.csv"    )    ).exists()    )
           self.assertTrue(Path(os.path.join(snake_case_	,			"train_mem.csv"    )    ).exists()    )
           self.assertTrue(Path(os.path.join(snake_case_	,			"env.csv"    )    ).exists()    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"

      def _check_summary_is_not_empty(snake_case_       :    Any    ):
           self.assertTrue(hasattr(snake_case_	,			"sequential"    )    )
           self.assertTrue(hasattr(snake_case_	,			"cumulative"    )    )
           self.assertTrue(hasattr(snake_case_	,			"current"    )    )
           self.assertTrue(hasattr(snake_case_	,			"total"    )    )

      with tempfile.TemporaryDirectory() as tmp_dir:
           _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			log_filename=os.path.join(snake_case_	,			"log.txt"    )	,			log_print=snake_case_	,			trace_memory_line_by_line=snake_case_	,			multi_process=snake_case_	,			)
           _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
           _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(snake_case_	,			"log.txt"    )    ).exists()    )











'''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      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()

      _UpperCAmelCase     							=   BlipImageProcessor()
      _UpperCAmelCase     							=   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

      _UpperCAmelCase     							=   BlipProcessor(snake_case_	,			snake_case_    )

      processor.save_pretrained(self.tmpdirname    )




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).tokenizer




 def      lowercase	(					self       :    Dict	,			**snake_case_       :    Any    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).image_processor




 def      lowercase	(					self       :    int    ):
      shutil.rmtree(self.tmpdirname    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   [np.random.randint(2_5_5	,			size=(3, 3_0, 4_0_0)	,			dtype=np.uinta    )]

      _UpperCAmelCase     							=   [Image.fromarray(np.moveaxis(snake_case_	,			0	,			-1    )    ) for x in image_inputs]

      return image_inputs




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   BlipProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      _UpperCAmelCase     							=   self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)"    )
      _UpperCAmelCase     							=   self.get_image_processor(do_normalize=snake_case_	,			padding_value=1.0    )

      _UpperCAmelCase     							=   BlipProcessor.from_pretrained(
          self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=snake_case_	,			padding_value=1.0    )

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

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




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="np"    )
      _UpperCAmelCase     							=   processor(images=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      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"

      _UpperCAmelCase     							=   processor(text=snake_case_    )

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_token_type_ids=snake_case_    )

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




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=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(snake_case_    ):
           processor()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _UpperCAmelCase     							=   processor.batch_decode(snake_case_    )
      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_    )

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=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"]    )




