





"""simple docstring"""




import os

import pytest
from attr import dataclass


lowerCamelCase_     :						List[Any]						=					'us-east-1'  # defaults region

@dataclass
class  _UpperCAmelCase  :



      '''simple docstring'''
      lowercase_      : str
      lowercase_      : Tuple	    =   """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
      lowercase_      : List[str]	    =   {
          """task_name""": """mnli""",
          """per_device_train_batch_size""": 16,
          """per_device_eval_batch_size""": 16,
          """do_train""": True,
          """do_eval""": True,
          """do_predict""": True,
          """output_dir""": """/opt/ml/model""",
          """overwrite_output_dir""": True,
          """max_steps""": 500,
          """save_steps""": 5_500,
      }
      lowercase_      : Tuple	    =   {**hyperparameters, """max_steps""": 1_000}

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.framework == "pytorch":
          return [
              {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
              {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
              {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
          ]
        else:
          return [
              {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
              {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
              {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
          ]

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return F"""{self.framework}-transfromers-test"""

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return F"""./tests/sagemaker/scripts/{self.framework}"""

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.framework == "pytorch":
          return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
          return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"





@pytest.fixture(scope='class'					)
def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  SageMakerTestEnvironment(framework=request.cls.framework					)






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )