





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 SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





     """simple docstring"""


     def 					__magic_name__ ( 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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sgugger/tiny-distilbert-classification"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  only_pretrain_model=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  torchscript=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_lowerCAmelCase	)
          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(torch_device == "cpu"	,  "Cant do half precision"	)
     def 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  fpaa=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       AutoConfig.from_pretrained(_lowerCAmelCase	)
          # set architectures equal to `None`
          lowerCamelCase__       =       None
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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	)





     def 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_lowerCAmelCase	)
          lowerCamelCase__       =       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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  fpaa=_lowerCAmelCase	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       AutoConfig.from_pretrained(_lowerCAmelCase	)
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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	)





     def 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tinier_bart"
          lowerCamelCase__       =       AutoConfig.from_pretrained(_lowerCAmelCase	)
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_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	)





     def 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          lowerCamelCase__       =       AutoConfig.from_pretrained(_lowerCAmelCase	)
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_lowerCAmelCase	,  configs=[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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tinier_bart"
          lowerCamelCase__       =       AutoConfig.from_pretrained(_lowerCAmelCase	)
          lowerCamelCase__       =       PyTorchBenchmarkArguments(
              models=[MODEL_ID]	,  training=_lowerCAmelCase	,  inference=_lowerCAmelCase	,  sequence_lengths=[8]	,  batch_sizes=[1]	,  multi_process=_lowerCAmelCase	,  )
          lowerCamelCase__       =       PyTorchBenchmark(_lowerCAmelCase	,  configs=[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 					__magic_name__ ( self	):
          lowerCamelCase__       =       "sshleifer/tiny-gpt2"
          with tempfile.TemporaryDirectory() as tmp_dir:
               lowerCamelCase__       =       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	,  )
               lowerCamelCase__       =       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 					__magic_name__ ( 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__       =       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	,  )
               lowerCamelCase__       =       PyTorchBenchmark(_lowerCAmelCase	)
               lowerCamelCase__       =       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()	)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor

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





					"""simple docstring"""


					A__				 =    ViTImageProcessor if is_vision_available() else None





					@property
					def 					__magic_name__ ( self	):
										return self.image_processor_tester.prepare_image_processor_dict()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       (3, 32, 128)
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
										# fmt: on
										lowerCamelCase__       =       dict(zip(_lowerCAmelCase	,  range(len(_lowerCAmelCase	)	)	)	)

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)

										lowerCamelCase__       =       {
										    "do_normalize": False,
										    "do_resize": True,
										    "image_processor_type": "ViTImageProcessor",
										    "resample": 3,
										    "size": {"height": 32, "width": 128},
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)

										lowerCamelCase__       =       Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	)

										return image_input





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(self.tmpdirname	,  use_fast=_lowerCAmelCase	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)

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

										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)
										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["pixel_values", "labels"]	)

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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

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

										lowerCamelCase__       =       processor.char_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       [seq.replace(" "	,  ""	) for seq in decoded_tok]

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       None
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       torch.randn(1	,  27	,  38	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  5_0257	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  3_0522	)

										lowerCamelCase__       =       processor.batch_decode([char_input, bpe_input, wp_input]	)

										self.assertListEqual(list(results.keys()	)	,  ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]	)

