
"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Any			     =  tempfile.mkdtemp()

						lowerCAmelCase_    :Union[str, Any]			     =  [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "want",
						    "##want",
						    "##ed",
						    "wa",
						    "un",
						    "runn",
						    "##ing",
						    ",",
						    "low",
						    "lowest",
						]
						lowerCAmelCase_    :Optional[Any]			     =  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["""vocab_file"""]       )
						with open(self.vocab_file       ,   """w"""       ,   encoding="""utf-8"""       ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

						lowerCAmelCase_    :Union[str, Any]			     =  {
						    "do_resize": True,
						    "size": 20,
						    "do_center_crop": True,
						    "crop_size": 18,
						    "do_normalize": True,
						    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
						    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
						}
						lowerCAmelCase_    :Union[str, Any]			     =  os.path.join(self.tmpdirname       ,   lowerCAmelCase__       )
						with open(self.image_processor_file       ,   """w"""       ,   encoding="""utf-8"""       ) as fp:
									json.dump(lowerCAmelCase__       ,   lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       ,   **__A       )			->     Union[str, Any]:
						return BertTokenizer.from_pretrained(self.tmpdirname       ,   **lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       ,   **__A       )			->     List[Any]:
						return BertTokenizerFast.from_pretrained(self.tmpdirname       ,   **lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       ,   **__A       )			->     Tuple:
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,   **lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       )			->     str:
						shutil.rmtree(self.tmpdirname       )






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :str			     =  [np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta       )]
						lowerCAmelCase_    :Dict			     =  [Image.fromarray(np.moveaxis(lowerCAmelCase__       ,   0       ,   -1       )       ) for x in image_inputs]
						return image_inputs






			def  __lowerCAmelCase (					self       )			->     Optional[int]:
						lowerCAmelCase_    :Union[str, Any]			     =  self.get_tokenizer()
						lowerCAmelCase_    :Tuple			     =  self.get_rust_tokenizer()
						lowerCAmelCase_    :Dict			     =  self.get_image_processor()

						lowerCAmelCase_    :int			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :Tuple			     =  AlignProcessor.from_pretrained(self.tmpdirname       ,   use_fast=lowerCAmelCase__       )

						lowerCAmelCase_    :Optional[Any]			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :List[str]			     =  AlignProcessor.from_pretrained(self.tmpdirname       )

						self.assertEqual(processor_slow.tokenizer.get_vocab()       ,   tokenizer_slow.get_vocab()       )
						self.assertEqual(processor_fast.tokenizer.get_vocab()       ,   tokenizer_fast.get_vocab()       )
						self.assertEqual(tokenizer_slow.get_vocab()       ,   tokenizer_fast.get_vocab()       )
						self.assertIsInstance(processor_slow.tokenizer       ,   lowerCAmelCase__       )
						self.assertIsInstance(processor_fast.tokenizer       ,   lowerCAmelCase__       )

						self.assertEqual(processor_slow.image_processor.to_json_string()       ,   image_processor.to_json_string()       )
						self.assertEqual(processor_fast.image_processor.to_json_string()       ,   image_processor.to_json_string()       )
						self.assertIsInstance(processor_slow.image_processor       ,   lowerCAmelCase__       )
						self.assertIsInstance(processor_fast.image_processor       ,   lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :int			     =  AlignProcessor(tokenizer=self.get_tokenizer()       ,   image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						lowerCAmelCase_    :List[str]			     =  self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       )
						lowerCAmelCase_    :Dict			     =  self.get_image_processor(do_normalize=lowerCAmelCase__       ,   padding_value=1.0       )

						lowerCAmelCase_    :List[Any]			     =  AlignProcessor.from_pretrained(
						    self.tmpdirname       ,   bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""       ,   do_normalize=lowerCAmelCase__       ,   padding_value=1.0       )

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

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






			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :Dict			     =  self.get_image_processor()
						lowerCAmelCase_    :Tuple			     =  self.get_tokenizer()

						lowerCAmelCase_    :Optional[int]			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )

						lowerCAmelCase_    :str			     =  self.prepare_image_inputs()

						lowerCAmelCase_    :Any			     =  image_processor(lowerCAmelCase__       ,   return_tensors="""np"""       )
						lowerCAmelCase_    :int			     =  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  __lowerCAmelCase (					self       )			->     Optional[int]:
						lowerCAmelCase_    :str			     =  self.get_image_processor()
						lowerCAmelCase_    :str			     =  self.get_tokenizer()

						lowerCAmelCase_    :Any			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )

						lowerCAmelCase_    :Dict			     =  "lower newer"

						lowerCAmelCase_    :Optional[int]			     =  processor(text=lowerCAmelCase__       )

						lowerCAmelCase_    :Any			     =  tokenizer(lowerCAmelCase__       ,   padding="""max_length"""       ,   max_length=64       )

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






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :List[Any]			     =  self.get_image_processor()
						lowerCAmelCase_    :Dict			     =  self.get_tokenizer()

						lowerCAmelCase_    :List[Any]			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )

						lowerCAmelCase_    :Union[str, Any]			     =  "lower newer"
						lowerCAmelCase_    :Optional[Any]			     =  self.prepare_image_inputs()

						lowerCAmelCase_    :Optional[int]			     =  processor(text=lowerCAmelCase__       ,   images=lowerCAmelCase__       )

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

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






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :int			     =  self.get_image_processor()
						lowerCAmelCase_    :Optional[Any]			     =  self.get_tokenizer()

						lowerCAmelCase_    :int			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )

						lowerCAmelCase_    :Tuple			     =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase_    :Tuple			     =  processor.batch_decode(lowerCAmelCase__       )
						lowerCAmelCase_    :Tuple			     =  tokenizer.batch_decode(lowerCAmelCase__       )

						self.assertListEqual(lowerCAmelCase__       ,   lowerCAmelCase__       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Optional[int]			     =  self.get_image_processor()
						lowerCAmelCase_    :int			     =  self.get_tokenizer()

						lowerCAmelCase_    :Any			     =  AlignProcessor(tokenizer=lowerCAmelCase__       ,   image_processor=lowerCAmelCase__       )

						lowerCAmelCase_    :List[Any]			     =  "lower newer"
						lowerCAmelCase_    :Union[str, Any]			     =  self.prepare_image_inputs()

						lowerCAmelCase_    :Tuple			     =  processor(text=lowerCAmelCase__       ,   images=lowerCAmelCase__       )

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













"""simple docstring"""




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


class        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> List[str]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								a      :		str	=				_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      )


