





'''simple docstring'''






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 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      ViTImageProcessor if is_vision_available() else None

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[Any]    =  (3, 3_2, 1_2_8)
    snake_case__     :					List[Any]    =  tempfile.mkdtemp()

    # fmt: off
    snake_case__     :					str    =  ["""[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
    snake_case__     :					List[str]    =  dict(zip(__SCREAMING_SNAKE_CASE	,		range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

    snake_case__     :					Optional[Any]    =  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(__SCREAMING_SNAKE_CASE       ) + """\n"""       )

    snake_case__     :					Optional[int]    =  {
        """do_normalize""": False,
        """do_resize""": True,
        """image_processor_type""": """ViTImageProcessor""",
        """resample""": 3,
        """size""": {"""height""": 3_2, """width""": 1_2_8},
    }
    snake_case__     :					str    =  os.path.join(self.tmpdirname	,		__SCREAMING_SNAKE_CASE       )
    with open(self.image_processor_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     json.dump(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  np.random.randint(2_5_5	,		size=(3, 3_0, 4_0_0)	,		dtype=np.uinta       )

    snake_case__     :					List[str]    =  Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,		0	,		-1       )       )

    return image_input

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_tokenizer()
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(self.tmpdirname       )
    snake_case__     :					Tuple    =  MgpstrProcessor.from_pretrained(self.tmpdirname	,		use_fast=__SCREAMING_SNAKE_CASE       )

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

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					int    =  self.get_tokenizer()
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(self.tmpdirname       )

    snake_case__     :					str    =  self.get_tokenizer(bos_token="""(BOS)"""	,		eos_token="""(EOS)"""       )
    snake_case__     :					List[Any]    =  self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,		padding_value=1.0       )

    snake_case__     :					Optional[int]    =  MgpstrProcessor.from_pretrained(
        self.tmpdirname	,		bos_token="""(BOS)"""	,		eos_token="""(EOS)"""	,		do_normalize=__SCREAMING_SNAKE_CASE	,		padding_value=1.0       )

    self.assertEqual(processor.char_tokenizer.get_vocab()	,		tokenizer_add_kwargs.get_vocab()       )
    self.assertIsInstance(processor.char_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 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  self.get_image_processor()
    snake_case__     :					Tuple    =  self.get_tokenizer()

    snake_case__     :					Optional[Any]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Dict    =  self.prepare_image_inputs()

    snake_case__     :					Tuple    =  image_processor(__SCREAMING_SNAKE_CASE	,		return_tensors="""np"""       )
    snake_case__     :					Optional[int]    =  processor(images=__SCREAMING_SNAKE_CASE	,		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 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  self.get_image_processor()
    snake_case__     :					Union[str, Any]    =  self.get_tokenizer()

    snake_case__     :					int    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  """test"""

    snake_case__     :					Optional[Any]    =  processor(text=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  tokenizer(__SCREAMING_SNAKE_CASE       )
    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,		encoded_processor[key]       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()
    snake_case__     :					List[Any]    =  self.get_tokenizer()

    snake_case__     :					Any    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  """test"""
    snake_case__     :					Any    =  self.prepare_image_inputs()

    snake_case__     :					Union[str, Any]    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

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

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_image_processor()
    snake_case__     :					Optional[Any]    =  self.get_tokenizer()

    snake_case__     :					List[str]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    snake_case__     :					Any    =  processor.char_decode(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[int]    =  [seq.replace(""" """	,		""""""       ) for seq in decoded_tok]

    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_image_processor()
    snake_case__     :					str    =  self.get_tokenizer()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  None
    snake_case__     :					Optional[int]    =  self.prepare_image_inputs()

    snake_case__     :					List[Any]    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

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


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  self.get_image_processor()
    snake_case__     :					List[Any]    =  self.get_tokenizer()

    snake_case__     :					Optional[Any]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					str    =  torch.randn(1	,		2_7	,		3_8       )
    snake_case__     :					Dict    =  torch.randn(1	,		2_7	,		5_0_2_5_7       )
    snake_case__     :					Union[str, Any]    =  torch.randn(1	,		2_7	,		3_0_5_2_2       )

    snake_case__     :					List[str]    =  processor.batch_decode([char_input, bpe_input, wp_input]       )

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











import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict





@dataclass
class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ComputeEnvironment.AMAZON_SAGEMAKER
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     True
				SCREAMING_SNAKE_CASE__     :   int		      =     '''ml.p3.2xlarge'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''accelerate_sagemaker_execution_role'''
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''hf-sm'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''us-east-1'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     1
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''accelerate-sagemaker-1'''
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''1.6'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''4.4'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''train.py'''
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''False''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''--do_test''',
				    '''False''',
				    '''--do_predict''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]





class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
										assert isinstance(converted_args['''model_name_or_path''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''do_train''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''epochs''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''learning_rate''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''max_steps''']			, _lowerCAmelCase					)

										with pytest.raises(_lowerCAmelCase					):
																_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)



