
"""simple docstring"""





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_a									=				importlib.metadata.version('numpy')
_a									=				'.'.join([str(x) for x in sys.version_info[:3]])


class A_      (lowercase__ ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # lt + different version strings
     require_version_core("numpy<1000.4.5"    )
     require_version_core("numpy<1000.4"    )
     require_version_core("numpy<1000"    )

     # le
     require_version_core("numpy<=1000.4.5"    )
     require_version_core(F"""numpy<={numpy_ver}"""    )

     # eq
     require_version_core(F"""numpy=={numpy_ver}"""    )

     # ne
     require_version_core("numpy!=1000.4.5"    )

     # ge
     require_version_core("numpy>=1.0"    )
     require_version_core("numpy>=1.0.0"    )
     require_version_core(F"""numpy>={numpy_ver}"""    )

     # gt
     require_version_core("numpy>1.0.0"    )

     # mix
     require_version_core("numpy>1.0.0,<1000"    )

     # requirement w/o version
     require_version_core("numpy"    )

     # unmet requirements due to version conflict
     for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
        try:
           require_version_core(lowercase_    )
        except ImportError as e:
           self.assertIn(F"""{req} is required"""							,  str(lowercase_    )    )
           self.assertIn("but found"							,  str(lowercase_    )    )

        # unmet requirements due to missing module
     for req in ["numpipypie>1", "numpipypie2"]:
        try:
           require_version_core(lowercase_    )
        except importlib.metadata.PackageNotFoundError as e:
           self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""							,  str(lowercase_    )    )
           self.assertIn("Try: pip install transformers -U"							,  str(lowercase_    )    )

        # bogus requirements formats:
        # 1. whole thing
     for req in ["numpy??1.0.0", "numpy1.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ValueError as e:
           self.assertIn("requirement needs to be in the pip package format"							,  str(lowercase_    )    )
        # 2. only operators
     for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ValueError as e:
           self.assertIn("need one of "							,  str(lowercase_    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # matching requirement
     require_version("python>=3.6.0"    )

     # not matching requirements
     for req in ["python>9.9.9", "python<3.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ImportError as e:
           self.assertIn(F"""{req} is required"""							,  str(lowercase_    )    )
           self.assertIn(F"""but found python=={python_ver}"""							,  str(lowercase_    )    )




"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a									=				logging.get_logger(__name__)

_a									=				{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						"""squeezebert"""







  def __init__(						self							,  lowercase_=3_0522							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=3072							,  lowercase_="gelu"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=512							,  lowercase_=2							,  lowercase_=0.02							,  lowercase_=1E-1_2							,  lowercase_=0							,  lowercase_=768							,  lowercase_=4							,  lowercase_=4							,  lowercase_=4							,  lowercase_=1							,  lowercase_=4							,  lowercase_=4							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_							,  **lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_size
     UpperCAmelCase_   :   Tuple     	=       num_hidden_layers
     UpperCAmelCase_   :   Optional[int]     	=       num_attention_heads
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       intermediate_size
     UpperCAmelCase_   :   Union[str, Any]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   List[Any]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   int     	=       max_position_embeddings
     UpperCAmelCase_   :   List[str]     	=       type_vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       initializer_range
     UpperCAmelCase_   :   Optional[int]     	=       layer_norm_eps
     UpperCAmelCase_   :   Tuple     	=       embedding_size
     UpperCAmelCase_   :   Dict     	=       q_groups
     UpperCAmelCase_   :   List[Any]     	=       k_groups
     UpperCAmelCase_   :   List[str]     	=       v_groups
     UpperCAmelCase_   :   Optional[Any]     	=       post_attention_groups
     UpperCAmelCase_   :   List[str]     	=       intermediate_groups
     UpperCAmelCase_   :   List[Any]     	=       output_groups




class A_      (lowercase__ ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_   :   List[str]     	=       {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_   :   Optional[int]     	=       {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]    )


