"""simple docstring"""






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		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=0		):







            """simple docstring"""



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

            super().__init__(__lowerCAmelCase						,			[AffineTransform(loc=self.loc						,			scale=self.scale						,			event_dim=__lowerCAmelCase		)]		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.base_dist.mean * self.scale + self.loc

      @property
      def 							A__			(		self		):







            """simple docstring"""



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







      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.variance.sqrt()






class 					_A		(			nn.Module      ):

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







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      [proj(__lowerCAmelCase		) for proj in self.proj]

            return self.domain_map(*__lowerCAmelCase		)






class 					_A		(			nn.Module      ):

      def __init__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            lowercase								=      function







      def 							A__			(		self						,			__lowerCAmelCase						,			*__lowerCAmelCase		):







            """simple docstring"""



            return self.function(__lowerCAmelCase						,			*__lowerCAmelCase		)






class 					_A		:
      snake_case__     :  type
      snake_case__     :  int
      snake_case__     :  Dict[str, int]

      def __init__(		self						,			__lowerCAmelCase = 1		):







            """simple docstring"""



            lowercase								=      dim
            lowercase								=      {k: dim * self.args_dim[k] for k in self.args_dim}

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if self.dim == 1:
                  return self.distribution_class(*__lowerCAmelCase		)
            else:
                  return Independent(self.distribution_class(*__lowerCAmelCase		)						,			1		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			):







            """simple docstring"""



            lowercase								=      self._base_distribution(__lowerCAmelCase		)
            if loc is None and scale is None:
                  return distr
            else:
                  return AffineTransformed(__lowerCAmelCase						,			loc=__lowerCAmelCase						,			scale=__lowerCAmelCase						,			event_dim=self.event_dim		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return () if self.dim == 1 else (self.dim,)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return len(self.event_shape		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return 0.0

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return ParameterProjection(
                in_features=__lowerCAmelCase						,			args_dim=self.args_dim						,			domain_map=LambdaLayer(self.domain_map		)						,			)

      def 							A__			(		self						,			*__lowerCAmelCase		):







            """simple docstring"""



            raise NotImplementedError()







      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



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






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Dict[str, int]				    =      {"df": 1, "loc": 1, "scale": 1}
      snake_case__     :  type				    =      StudentT

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Dict[str, int]				    =      {"loc": 1, "scale": 1}
      snake_case__     :  type				    =      Normal

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Dict[str, int]				    =      {"total_count": 1, "logits": 1}
      snake_case__     :  type				    =      NegativeBinomial

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      cls.squareplus(__lowerCAmelCase		)
            return total_count.squeeze(-1		), logits.squeeze(-1		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase				,				lowercase								=      distr_args

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

            return self._base_distribution((total_count, logits)		)




"""simple docstring"""






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      TransfoXLTokenizer
      snake_case__     :  Union[str, Any]				    =      False
      snake_case__     :  Union[str, Any]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()

            lowercase								=      [
                """<unk>""",
                """[CLS]""",
                """[SEP]""",
                """want""",
                """unwanted""",
                """wa""",
                """un""",
                """running""",
                """,""",
                """low""",
                """l""",
            ]
            lowercase								=      os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]		)
            with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""		) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      """<unk> UNwanted , running"""
            lowercase								=      """<unk> unwanted, running"""
            return input_text, output_text

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(vocab_file=self.vocab_file						,			lower_case=__lowerCAmelCase		)

            lowercase								=      tokenizer.tokenize("""<unk> UNwanted , running"""		)
            self.assertListEqual(__lowerCAmelCase						,			["""<unk>""", """unwanted""", """,""", """running"""]		)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,			[0, 4, 8, 7]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""hello""", """!""", """how""", """are""", """you""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)

            self.assertListEqual(
                tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)						,			["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      TransfoXLTokenizer(lower_case=__lowerCAmelCase		)
            lowercase								=      """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
            lowercase								=      [
                """Hello""",
                """(""",
                """bracket""",
                """)""",
                """and""",
                """side""",
                """@-@""",
                """scrolled""",
                """[""",
                """and""",
                """]""",
                """Henry""",
                """'s""",
                """$""",
                """5""",
                """@,@""",
                """000""",
                """with""",
                """3""",
                """@.@""",
                """34""",
                """m""",
                """.""",
                """What""",
                """'s""",
                """up""",
                """!""",
                """?""",
            ]

            self.assertListEqual(tokenizer.tokenize(__lowerCAmelCase		)						,			__lowerCAmelCase		)

            self.assertEqual(tokenizer.convert_tokens_to_string(__lowerCAmelCase		)						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizer()
            lowercase								=      len(__lowerCAmelCase		)

            tokenizer.add_tokens(["""new1""", """new2"""]		)
            tokenizer.move_added_token("""new1"""						,			1		)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(__lowerCAmelCase		)						,			original_len + 2		)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode("""new1"""		)						,			[1]		)
            self.assertEqual(tokenizer.decode([1]		)						,			"""new1"""		)



