



'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 	SCREAMING_SNAKE_CASE	(      a__    ,   a__  ):




       @register_to_config
       def __init__(					self      : Any	,    A__      : List[str] = 128	,    A__      : List[Any] = 256	,    A__      : Dict = 2000.0	,    A__      : Tuple = 768	,    A__      : int = 12	,    A__      : Optional[Any] = 12	,    A__      : Any = 64	,    A__      : str = 2048	,    A__      : Optional[int] = 0.1	,    ):


            """simple docstring"""







            super().__init__()

            __lowerCamelCase						:       int				   =				nn.Sequential(
                nn.Linear(_A	,    d_model * 4	,    bias=_A					)	,    nn.SiLU()	,    nn.Linear(d_model * 4	,    d_model * 4	,    bias=_A					)	,    nn.SiLU()	,    )

            __lowerCamelCase						:       Any				   =				nn.Embedding(_A	,    _A					)
            __lowerCamelCase						:       Tuple				   =				False

            __lowerCamelCase						:       Union[str, Any]				   =				nn.Linear(_A	,    _A	,    bias=_A					)

            __lowerCamelCase						:       int				   =				nn.Dropout(p=_A					)

            __lowerCamelCase						:       int				   =				nn.ModuleList()
            for lyr_num in range(_A					):
                 # FiLM conditional T5 decoder
                 __lowerCamelCase						:       Any				   =				DecoderLayer(d_model=_A	,    d_kv=_A	,    num_heads=_A	,    d_ff=_A	,    dropout_rate=_A					)
                 self.decoders.append(_A					)

            __lowerCamelCase						:       Optional[Any]				   =				TaLayerNorm(_A					)

            __lowerCamelCase						:       List[str]				   =				nn.Dropout(p=_A					)
            __lowerCamelCase						:       Optional[Any]				   =				nn.Linear(_A	,    _A	,    bias=_A					)




       def 					a_					(					self      : List[Any]	,    A__      : Union[str, Any]	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				torch.mul(query_input.unsqueeze(-1					)	,    key_input.unsqueeze(-2					)					)
            return mask.unsqueeze(-3					)




       def 					a_					(					self      : List[str]	,    A__      : List[str]	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				decoder_input_tokens.shape
            assert decoder_noise_time.shape == (batch,)

            # decoder_noise_time is in [0, 1), so rescale to expected timing range.
            __lowerCamelCase						:       Any				   =				get_timestep_embedding(
                decoder_noise_time * self.config.max_decoder_noise_time	,    embedding_dim=self.config.d_model	,    max_period=self.config.max_decoder_noise_time	,    ).to(dtype=self.dtype					)

            __lowerCamelCase						:       Union[str, Any]				   =				self.conditioning_emb(_A					).unsqueeze(1					)

            assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

            __lowerCamelCase						:       str				   =				decoder_input_tokens.shape[1]

            # If we want to use relative positions for audio context, we can just offset
            # this sequence by the length of encodings_and_masks.
            __lowerCamelCase						:       Union[str, Any]				   =				torch.broadcast_to(
                torch.arange(_A	,    device=decoder_input_tokens.device					)	,    (batch, seq_length)	,    )

            __lowerCamelCase						:       Any				   =				self.position_encoding(_A					)

            __lowerCamelCase						:       str				   =				self.continuous_inputs_projection(_A					)
            inputs += position_encodings
            __lowerCamelCase						:       int				   =				self.dropout(_A					)

            # decoder: No padding present.
            __lowerCamelCase						:       Union[str, Any]				   =				torch.ones(
                decoder_input_tokens.shape[:2]	,    device=decoder_input_tokens.device	,    dtype=inputs.dtype					)

            # Translate encoding masks to encoder-decoder masks.
            __lowerCamelCase						:       Optional[Any]				   =				[(x, self.encoder_decoder_mask(_A	,    _A					)) for x, y in encodings_and_masks]

            # cross attend style: concat encodings
            __lowerCamelCase						:       Dict				   =				torch.cat([x[0] for x in encodings_and_encdec_masks]	,    dim=1					)
            __lowerCamelCase						:       Tuple				   =				torch.cat([x[1] for x in encodings_and_encdec_masks]	,    dim=-1					)

            for lyr in self.decoders:
                 __lowerCamelCase						:       Tuple				   =				lyr(
                     _A	,    conditioning_emb=_A	,    encoder_hidden_states=_A	,    encoder_attention_mask=_A	,    )[0]

            __lowerCamelCase						:       Any				   =				self.decoder_norm(_A					)
            __lowerCamelCase						:       List[Any]				   =				self.post_dropout(_A					)

            __lowerCamelCase						:       int				   =				self.spec_out(_A					)
            return spec_out







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : int	,    A__      : Optional[int]	,    A__      : List[Any]	,    A__      : List[Any]	,    A__      : Dict	,    A__      : str	,    A__      : Optional[int]=1e-6					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Optional[Any]				   =				nn.ModuleList()

            # cond self attention: layer 0
            self.layer.append(
                TaLayerSelfAttentionCond(d_model=_A	,    d_kv=_A	,    num_heads=_A	,    dropout_rate=_A					)					)

            # cross attention: layer 1
            self.layer.append(
                TaLayerCrossAttention(
                    d_model=_A	,    d_kv=_A	,    num_heads=_A	,    dropout_rate=_A	,    layer_norm_epsilon=_A	,    )					)

            # Film Cond MLP + dropout: last layer
            self.layer.append(
                TaLayerFFCond(d_model=_A	,    d_ff=_A	,    dropout_rate=_A	,    layer_norm_epsilon=_A					)					)




       def 					a_					(					self      : Union[str, Any]	,    A__      : Dict	,    A__      : Optional[int]=None	,    A__      : List[str]=None	,    A__      : Optional[Any]=None	,    A__      : Any=None	,    A__      : int=None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.layer[0](
                _A	,    conditioning_emb=_A	,    attention_mask=_A	,    )

            if encoder_hidden_states is not None:
                 __lowerCamelCase						:       Any				   =				torch.where(encoder_attention_mask > 0	,    0	,    -1e1_0					).to(
                     encoder_hidden_states.dtype					)

                 __lowerCamelCase						:       str				   =				self.layer[1](
                     _A	,    key_value_states=_A	,    attention_mask=_A	,    )

            # Apply Film Conditional Feed Forward layer
            __lowerCamelCase						:       Optional[Any]				   =				self.layer[-1](_A	,    _A					)

            return (hidden_states,)







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : str	,    A__      : List[Any]	,    A__      : int	,    A__      : str	,    A__      : str					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Union[str, Any]				   =				TaLayerNorm(_A					)
            __lowerCamelCase						:       Any				   =				TaFiLMLayer(in_features=d_model * 4	,    out_features=_A					)
            __lowerCamelCase						:       Dict				   =				Attention(query_dim=_A	,    heads=_A	,    dim_head=_A	,    out_bias=_A	,    scale_qk=_A					)
            __lowerCamelCase						:       Tuple				   =				nn.Dropout(_A					)




       def 					a_					(					self      : Union[str, Any]	,    A__      : Optional[Any]	,    A__      : Optional[int]=None	,    A__      : int=None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.layer_norm(_A					)

            if conditioning_emb is not None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.FiLMLayer(_A	,    _A					)

            # Self-attention block
            __lowerCamelCase						:       Union[str, Any]				   =				self.attention(_A					)

            __lowerCamelCase						:       Optional[Any]				   =				hidden_states + self.dropout(_A					)

            return hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Optional[Any]	,    A__      : List[str]	,    A__      : Optional[Any]	,    A__      : Optional[int]	,    A__      : Optional[Any]	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       List[str]				   =				Attention(query_dim=_A	,    heads=_A	,    dim_head=_A	,    out_bias=_A	,    scale_qk=_A					)
            __lowerCamelCase						:       Optional[int]				   =				TaLayerNorm(_A	,    eps=_A					)
            __lowerCamelCase						:       Tuple				   =				nn.Dropout(_A					)




       def 					a_					(					self      : str	,    A__      : Optional[Any]	,    A__      : Any=None	,    A__      : str=None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.layer_norm(_A					)
            __lowerCamelCase						:       str				   =				self.attention(
                _A	,    encoder_hidden_states=_A	,    attention_mask=attention_mask.squeeze(1					)	,    )
            __lowerCamelCase						:       Any				   =				hidden_states + self.dropout(_A					)
            return layer_output







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : str	,    A__      : Tuple	,    A__      : Any	,    A__      : Optional[Any]	,    A__      : str					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Optional[int]				   =				TaDenseGatedActDense(d_model=_A	,    d_ff=_A	,    dropout_rate=_A					)
            __lowerCamelCase						:       Tuple				   =				TaFiLMLayer(in_features=d_model * 4	,    out_features=_A					)
            __lowerCamelCase						:       Any				   =				TaLayerNorm(_A	,    eps=_A					)
            __lowerCamelCase						:       Union[str, Any]				   =				nn.Dropout(_A					)




       def 					a_					(					self      : Dict	,    A__      : str	,    A__      : int=None					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.layer_norm(_A					)
            if conditioning_emb is not None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.film(_A	,    _A					)

            __lowerCamelCase						:       str				   =				self.DenseReluDense(_A					)
            __lowerCamelCase						:       Tuple				   =				hidden_states + self.dropout(_A					)
            return hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Union[str, Any]	,    A__      : Tuple	,    A__      : Any	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Union[str, Any]				   =				nn.Linear(_A	,    _A	,    bias=_A					)
            __lowerCamelCase						:       Any				   =				nn.Linear(_A	,    _A	,    bias=_A					)
            __lowerCamelCase						:       Union[str, Any]				   =				nn.Linear(_A	,    _A	,    bias=_A					)
            __lowerCamelCase						:       Union[str, Any]				   =				nn.Dropout(_A					)
            __lowerCamelCase						:       int				   =				NewGELUActivation()




       def 					a_					(					self      : str	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.act(self.wi_a(_A					)					)
            __lowerCamelCase						:       Optional[int]				   =				self.wi_a(_A					)
            __lowerCamelCase						:       Union[str, Any]				   =				hidden_gelu * hidden_linear
            __lowerCamelCase						:       Dict				   =				self.dropout(_A					)

            __lowerCamelCase						:       Dict				   =				self.wo(_A					)
            return hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Optional[Any]	,    A__      : Optional[int]	,    A__      : Union[str, Any]=1e-6					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       Union[str, Any]				   =				nn.Parameter(torch.ones(_A					)					)
            __lowerCamelCase						:       Optional[int]				   =				eps




       def 					a_					(					self      : Any	,    A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				hidden_states.to(torch.floataa					).pow(2					).mean(-1	,    keepdim=_A					)
            __lowerCamelCase						:       List[Any]				   =				hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

            # convert into half-precision if necessary
            if self.weight.dtype in [torch.floataa, torch.bfloataa]:
                 __lowerCamelCase						:       Optional[int]				   =				hidden_states.to(self.weight.dtype					)

            return self.weight * hidden_states







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def 					a_					(					self      : Optional[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_4715 * torch.pow(_A	,    3.0					))					))







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : Optional[Any]	,    A__      : Optional[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       List[str]				   =				nn.Linear(_A	,    out_features * 2	,    bias=_A					)




       def 					a_					(					self      : List[Any]	,    A__      : str	,    A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.scale_bias(_A					)
            __lowerCamelCase						:       List[Any]				   =				torch.chunk(_A	,    2	,    -1					)
            __lowerCamelCase						:       List[Any]				   =				x * (1 + scale) + shift
            return x











'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			RobertaTokenizer
       snake_case__    :     Optional[int]   		=			RobertaTokenizerFast
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     Tuple   		=			{'cls_token': '<s>'}




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __lowerCamelCase						:       Tuple				   =				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            __lowerCamelCase						:       Tuple				   =				dict(zip(A__	,    range(len(A__					)					)					)					)
            __lowerCamelCase						:       List[str]				   =				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __lowerCamelCase						:       Tuple				   =				{"""unk_token""": """<unk>"""}

            __lowerCamelCase						:       Optional[int]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]					)
            __lowerCamelCase						:       Union[str, Any]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""merges_file"""]					)
            with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write(json.dumps(A__					) + """\n"""					)
            with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write("""\n""".join(A__					)					)




       def 					a_					(					self      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : str	,    **A__      : List[Any]					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : Tuple	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""lower newer"""
            __lowerCamelCase						:       int				   =				"""lower newer"""
            return input_text, output_text




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.tokenizer_class(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map					)
            __lowerCamelCase						:       Any				   =				"""lower newer"""
            __lowerCamelCase						:       Union[str, Any]				   =				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.tokenize(A__					)  # , add_prefix_space=True)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       List[str]				   =				tokens + [tokenizer.unk_token]
            __lowerCamelCase						:       str				   =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,    A__					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,    )




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode("""sequence builders"""	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				tokenizer.encode("""multi-sequence build"""	,    add_special_tokens=A__					)

            __lowerCamelCase						:       Dict				   =				tokenizer.encode(
                """sequence builders"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.encode(
                """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.build_inputs_with_special_tokens(A__					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.build_inputs_with_special_tokens(A__	,    A__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_tokenizer()

            __lowerCamelCase						:       str				   =				"""Encode this sequence."""
            __lowerCamelCase						:       str				   =				tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

            # Testing encoder arguments
            __lowerCamelCase						:       Union[str, Any]				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(A__	,    A__					)

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(A__	,    A__					)

            # Testing spaces after special tokens
            __lowerCamelCase						:       str				   =				"""<mask>"""
            tokenizer.add_special_tokens(
                {"""mask_token""": AddedToken(A__	,    lstrip=A__	,    rstrip=A__					)}					)  # mask token has a left space
            __lowerCamelCase						:       str				   =				tokenizer.convert_tokens_to_ids(A__					)

            __lowerCamelCase						:       str				   =				"""Encode <mask> sequence"""
            __lowerCamelCase						:       Optional[int]				   =				"""Encode <mask>sequence"""

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				encoded.index(A__					)
            __lowerCamelCase						:       Any				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				encoded.index(A__					)
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(A__	,    A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            pass




       def 					a_					(					self      : str					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				"""A, <mask> AllenNLP sentence."""
                      __lowerCamelCase						:       int				   =				tokenizer_r.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]					)	,    sum(tokens_p["""token_type_ids"""]					)					)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)	,    sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)	,    )

                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                      __lowerCamelCase						:       Any				   =				tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2					):
                 __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowerCamelCase						:       Dict				   =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]	,    A__					)

                 self.assertEqual(post_processor_state["""add_prefix_space"""]	,    A__					)
                 self.assertEqual(post_processor_state["""trim_offsets"""]	,    A__					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowerCamelCase						:       Optional[Any]				   =				f"{text_of_1_token} {text_of_1_token}"

                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       int				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Tuple				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       str				   =				f" {text}"

                      # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                      # )
                      # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                      # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                      # self.assertEqual(
                      #     encoding.offset_mapping[1],
                      #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                      # )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (1, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					) + 1, 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Dict				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Dict				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )



