





import os
import sys
import unittest


SCREAMING_SNAKE_CASE_						   =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE_						   =       os.path.join(git_repo_path, """src""", """transformers""")

SCREAMING_SNAKE_CASE_						   =       """
{0} = None
"""

SCREAMING_SNAKE_CASE_						   =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


SCREAMING_SNAKE_CASE_						   =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""       )
           self.assertIsNone(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						find_backend("""    if not is_tokenizers_available():"""       )
           self.assertEqual(lowerCamelCase__						,"""tokenizers"""       )

           SCREAMING_SNAKE_CASE		=						find_backend("""    if not is_tensorflow_text_available():"""       )
           self.assertEqual(lowerCamelCase__						,"""tensorflow_text"""       )

           SCREAMING_SNAKE_CASE		=						find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""       )
           self.assertEqual(lowerCamelCase__						,"""sentencepiece_and_tokenizers"""       )

           SCREAMING_SNAKE_CASE		=						find_backend(
               """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""       )
           self.assertEqual(lowerCamelCase__						,"""sentencepiece_and_tensorflow_text"""       )

           SCREAMING_SNAKE_CASE		=						find_backend(
               """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""       )
           self.assertEqual(lowerCamelCase__						,"""sentencepiece_and_tokenizers_and_vision"""       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("""torch"""						,lowerCamelCase__       )
           self.assertIn("""tensorflow_text"""						,lowerCamelCase__       )
           self.assertIn("""sentencepiece_and_tokenizers"""						,lowerCamelCase__       )

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("""BertModel"""						,objects["""torch"""]       )
           self.assertIn("""TFBertModel"""						,objects["""tf"""]       )
           self.assertIn("""FlaxBertModel"""						,objects["""flax"""]       )
           self.assertIn("""BertModel"""						,objects["""torch"""]       )
           self.assertIn("""TFBertTokenizer"""						,objects["""tensorflow_text"""]       )
           self.assertIn("""convert_slow_tokenizer"""						,objects["""sentencepiece_and_tokenizers"""]       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						create_dummy_object("""CONSTANT"""						,"""'torch'"""       )
           self.assertEqual(lowerCamelCase__						,"""\nCONSTANT = None\n"""       )

           SCREAMING_SNAKE_CASE		=						create_dummy_object("""function"""						,"""'torch'"""       )
           self.assertEqual(
               lowerCamelCase__						,"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""       )

           SCREAMING_SNAKE_CASE		=						"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
           SCREAMING_SNAKE_CASE		=						create_dummy_object("""FakeClass"""						,"""'torch'"""       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
           SCREAMING_SNAKE_CASE		=						create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}       )
           self.assertEqual(dummy_files["""torch"""]						,lowerCamelCase__       )









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_						   =       logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[float]			=			field(
         default=0.0							,      metadata={"help": "The label smoothing epsilon to apply (if not zero)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to SortishSamler or not."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "whether to use adafactor"}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Encoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Decoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(default=lowerCAmelCase_							,      metadata={"help": "Dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Attention dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[str]			=			field(
         default="linear"							,      metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}"}							,      )


