





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A=None							,					_A=None							,					_A=0		):






				'''simple docstring'''







				UpperCAmelCase   		=				1.0 if scale is None else scale
				UpperCAmelCase   		=				0.0 if loc is None else loc

				super().__init__(_A							,					[AffineTransform(loc=self.loc							,					scale=self.scale							,					event_dim=_A		)]		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.base_dist.mean * self.scale + self.loc
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.base_dist.variance * self.scale**2







	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.variance.sqrt()

class 	A_       (nn.Module			):
	def __init__(    self							,					_A							,					_A							,					_A							,					**_A		):






				'''simple docstring'''







				super().__init__(**_A		)
				UpperCAmelCase   		=				args_dim
				UpperCAmelCase   		=				nn.ModuleList([nn.Linear(_A							,					_A		) for dim in args_dim.values()]		)
				UpperCAmelCase   		=				domain_map







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				[proj(_A		) for proj in self.proj]

				return self.domain_map(*_A		)

class 	A_       (nn.Module			):
	def __init__(    self							,					_A		):






				'''simple docstring'''







				super().__init__()
				UpperCAmelCase   		=				function







	def      _lowercase				(    self							,					_A							,					*_A		):






				'''simple docstring'''







				return self.function(_A							,					*_A		)

class 	A_       :
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A = 1		):






				'''simple docstring'''







				UpperCAmelCase   		=				dim
				UpperCAmelCase   		=				{k: dim * self.args_dim[k] for k in self.args_dim}
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				if self.dim == 1:
							return self.distribution_class(*_A		)
				else:
							return Independent(self.distribution_class(*_A		)							,					1		)
	def      _lowercase				(    self							,					_A							,					_A = None							,					_A = None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self._base_distribution(_A		)
				if loc is None and scale is None:
							return distr
				else:
							return AffineTransformed(_A							,					loc=_A							,					scale=_A							,					event_dim=self.event_dim		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return () if self.dim == 1 else (self.dim,)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return len(self.event_shape		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 0.0
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				return ParameterProjection(
				    in_features=_A							,					args_dim=self.args_dim							,					domain_map=LambdaLayer(self.domain_map		)							,					)
	def      _lowercase				(    self							,					*_A		):






				'''simple docstring'''







				raise NotImplementedError()







	@staticmethod
	def      _lowercase				(    _A		):






				'''simple docstring'''







				return (x + torch.sqrt(torch.square(_A		) + 4.0		)) / 2.0

class 	A_       (a_			):
	UpperCAmelCase__					=			{"df": 1, "loc": 1, "scale": 1}
	UpperCAmelCase__					=			StudentT
	@classmethod
	def      _lowercase				(    cls							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cls.squareplus(_A		).clamp_min(torch.finfo(scale.dtype		).eps		)
				UpperCAmelCase   		=				2.0 + cls.squareplus(_A		)
				return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)

class 	A_       (a_			):
	UpperCAmelCase__					=			{"loc": 1, "scale": 1}
	UpperCAmelCase__					=			Normal
	@classmethod
	def      _lowercase				(    cls							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cls.squareplus(_A		).clamp_min(torch.finfo(scale.dtype		).eps		)
				return loc.squeeze(-1		), scale.squeeze(-1		)

class 	A_       (a_			):
	UpperCAmelCase__					=			{"total_count": 1, "logits": 1}
	UpperCAmelCase__					=			NegativeBinomial
	@classmethod
	def      _lowercase				(    cls							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cls.squareplus(_A		)
				return total_count.squeeze(-1		), logits.squeeze(-1		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				distr_args
				if self.dim == 1:
							return self.distribution_class(total_count=_A							,					logits=_A		)
				else:
							return Independent(self.distribution_class(total_count=_A							,					logits=_A		)							,					1		)







	def      _lowercase				(    self							,					_A							,					_A = None							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				distr_args

				if scale is not None:
							# See scaling property of Gamma.
							logits += scale.log()

				return self._base_distribution((total_count, logits)		)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



