



'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase_		       =  logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__						)
class __A			(  _a						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       field(default=_a   ,   metadata={'help': 'Whether to use SortishSampler or not.'}						)
 __lowerCamelCase    :       Optional[Any]          =       field(
     default=_a   ,   metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}						)
 __lowerCamelCase    :       Tuple          =       field(
     default=_a   ,   metadata={
         'help': (
             'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
             'to the `max_length` value of the model configuration.'
         )
     }   ,   )
 __lowerCamelCase    :       Tuple          =       field(
     default=_a   ,   metadata={
         'help': (
             'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
             'to the `num_beams` value of the model configuration.'
         )
     }   ,   )
 __lowerCamelCase    :       List[str]          =       field(
     default=_a   ,   metadata={
         'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
     }   ,   )



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  super().to_dict()
      for k, v in d.items():
           if isinstance(snake_case_ ,   snake_case_       ):
                _a      =  v.to_dict()
      return d








'''simple docstring'''





__a          =		frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])

__a          =		frozenset([])

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image"])

__a          =		frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "negative_prompt"])

__a          =		frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__a          =		frozenset(["prompt", "image", "mask_image", "negative_prompt"])

__a          =		frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["image", "mask_image"])

__a          =		frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__a          =		frozenset(["example_image", "image", "mask_image"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["class_labels"])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(["batch_size"])

__a          =		frozenset([])

__a          =		frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__a          =		frozenset(["prompt", "negative_prompt"])
__a          =		frozenset(["input_tokens"])

__a          =		frozenset(["input_tokens"])
