import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class     lowercase     (  nn.Module  ):
							lowercase__    :							List[Any]										=				42
							lowercase__    :							int										=				42
							lowercase__    :							Any										=				0.0
							lowercase__    :							Optional[Any]										=				1
							lowercase__    :							Any										=				1
							lowercase__    :							Tuple										=				True
							lowercase__    :							Union[str, Any]										=				False
							lowercase__    :							Union[str, Any]										=				False
							lowercase__    :							str										=				False
							lowercase__    :							str										=				jnp.floataa




							def 						__snake_case(    self :       Optional[int]   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_SNAKE_CASE           =							[]

												for i in range(self.num_layers   ):
																	SCREAMING_SNAKE_CASE           =							self.in_channels if i == 0 else self.out_channels

																	SCREAMING_SNAKE_CASE           =							FlaxResnetBlockaD(
																	    in_channels=lowerCAmelCase_						,				out_channels=self.out_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
																	resnets.append(lowerCAmelCase_   )

																	SCREAMING_SNAKE_CASE           =							FlaxTransformeraDModel(
																	    in_channels=self.out_channels						,				n_heads=self.num_attention_heads						,				d_head=self.out_channels // self.num_attention_heads						,				depth=1						,				use_linear_projection=self.use_linear_projection						,				only_cross_attention=self.only_cross_attention						,				use_memory_efficient_attention=self.use_memory_efficient_attention						,				dtype=self.dtype						,				)
																	attentions.append(lowerCAmelCase_   )

												SCREAMING_SNAKE_CASE           =							resnets
												SCREAMING_SNAKE_CASE           =							attentions

												if self.add_downsample:
																	SCREAMING_SNAKE_CASE           =							FlaxDownsampleaD(self.out_channels						,				dtype=self.dtype   )





							def __call__(    self :       Optional[int]						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       str						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       str=True   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							()

												for resnet, attn in zip(self.resnets						,				self.attentions   ):
																	SCREAMING_SNAKE_CASE           =							resnet(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )
																	SCREAMING_SNAKE_CASE           =							attn(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )
																	output_states += (hidden_states,)

												if self.add_downsample:
																	SCREAMING_SNAKE_CASE           =							self.downsamplers_a(lowerCAmelCase_   )
																	output_states += (hidden_states,)

												return hidden_states, output_states



class     lowercase     (  nn.Module  ):
							lowercase__    :							str										=				42
							lowercase__    :							Optional[int]										=				42
							lowercase__    :							Optional[Any]										=				0.0
							lowercase__    :							List[Any]										=				1
							lowercase__    :							int										=				True
							lowercase__    :							List[Any]										=				jnp.floataa




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]

												for i in range(self.num_layers   ):
																	SCREAMING_SNAKE_CASE           =							self.in_channels if i == 0 else self.out_channels

																	SCREAMING_SNAKE_CASE           =							FlaxResnetBlockaD(
																	    in_channels=lowerCAmelCase_						,				out_channels=self.out_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
																	resnets.append(lowerCAmelCase_   )
												SCREAMING_SNAKE_CASE           =							resnets

												if self.add_downsample:
																	SCREAMING_SNAKE_CASE           =							FlaxDownsampleaD(self.out_channels						,				dtype=self.dtype   )





							def __call__(    self :       Tuple						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       List[str]=True   )       ->					Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							()

												for resnet in self.resnets:
																	SCREAMING_SNAKE_CASE           =							resnet(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )
																	output_states += (hidden_states,)

												if self.add_downsample:
																	SCREAMING_SNAKE_CASE           =							self.downsamplers_a(lowerCAmelCase_   )
																	output_states += (hidden_states,)

												return hidden_states, output_states



class     lowercase     (  nn.Module  ):
							lowercase__    :							List[Any]										=				42
							lowercase__    :							Any										=				42
							lowercase__    :							Optional[Any]										=				42
							lowercase__    :							List[Any]										=				0.0
							lowercase__    :							List[str]										=				1
							lowercase__    :							Any										=				1
							lowercase__    :							int										=				True
							lowercase__    :							Tuple										=				False
							lowercase__    :							Dict										=				False
							lowercase__    :							Any										=				False
							lowercase__    :							Any										=				jnp.floataa




							def 						__snake_case(    self :       Optional[int]   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_SNAKE_CASE           =							[]

												for i in range(self.num_layers   ):
																	SCREAMING_SNAKE_CASE           =							self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	SCREAMING_SNAKE_CASE           =							self.prev_output_channel if i == 0 else self.out_channels

																	SCREAMING_SNAKE_CASE           =							FlaxResnetBlockaD(
																	    in_channels=resnet_in_channels + res_skip_channels						,				out_channels=self.out_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
																	resnets.append(lowerCAmelCase_   )

																	SCREAMING_SNAKE_CASE           =							FlaxTransformeraDModel(
																	    in_channels=self.out_channels						,				n_heads=self.num_attention_heads						,				d_head=self.out_channels // self.num_attention_heads						,				depth=1						,				use_linear_projection=self.use_linear_projection						,				only_cross_attention=self.only_cross_attention						,				use_memory_efficient_attention=self.use_memory_efficient_attention						,				dtype=self.dtype						,				)
																	attentions.append(lowerCAmelCase_   )

												SCREAMING_SNAKE_CASE           =							resnets
												SCREAMING_SNAKE_CASE           =							attentions

												if self.add_upsample:
																	SCREAMING_SNAKE_CASE           =							FlaxUpsampleaD(self.out_channels						,				dtype=self.dtype   )





							def __call__(    self :       Optional[Any]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       Optional[int]=True   )       ->					int:



												'''simple docstring'''

												for resnet, attn in zip(self.resnets						,				self.attentions   ):
																	# pop res hidden states
																	SCREAMING_SNAKE_CASE           =							res_hidden_states_tuple[-1]
																	SCREAMING_SNAKE_CASE           =							res_hidden_states_tuple[:-1]
																	SCREAMING_SNAKE_CASE           =							jnp.concatenate((hidden_states, res_hidden_states)						,				axis=-1   )

																	SCREAMING_SNAKE_CASE           =							resnet(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )
																	SCREAMING_SNAKE_CASE           =							attn(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )

												if self.add_upsample:
																	SCREAMING_SNAKE_CASE           =							self.upsamplers_a(lowerCAmelCase_   )

												return hidden_states



class     lowercase     (  nn.Module  ):
							lowercase__    :							Dict										=				42
							lowercase__    :							Union[str, Any]										=				42
							lowercase__    :							Optional[Any]										=				42
							lowercase__    :							Tuple										=				0.0
							lowercase__    :							Union[str, Any]										=				1
							lowercase__    :							str										=				True
							lowercase__    :							int										=				jnp.floataa




							def 						__snake_case(    self :       Optional[int]   )       ->					int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]

												for i in range(self.num_layers   ):
																	SCREAMING_SNAKE_CASE           =							self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	SCREAMING_SNAKE_CASE           =							self.prev_output_channel if i == 0 else self.out_channels

																	SCREAMING_SNAKE_CASE           =							FlaxResnetBlockaD(
																	    in_channels=resnet_in_channels + res_skip_channels						,				out_channels=self.out_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
																	resnets.append(lowerCAmelCase_   )

												SCREAMING_SNAKE_CASE           =							resnets

												if self.add_upsample:
																	SCREAMING_SNAKE_CASE           =							FlaxUpsampleaD(self.out_channels						,				dtype=self.dtype   )





							def __call__(    self :       Any						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       str						,				_UpperCamelCase :       Optional[int]=True   )       ->					Optional[Any]:



												'''simple docstring'''

												for resnet in self.resnets:
																	# pop res hidden states
																	SCREAMING_SNAKE_CASE           =							res_hidden_states_tuple[-1]
																	SCREAMING_SNAKE_CASE           =							res_hidden_states_tuple[:-1]
																	SCREAMING_SNAKE_CASE           =							jnp.concatenate((hidden_states, res_hidden_states)						,				axis=-1   )

																	SCREAMING_SNAKE_CASE           =							resnet(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )

												if self.add_upsample:
																	SCREAMING_SNAKE_CASE           =							self.upsamplers_a(lowerCAmelCase_   )

												return hidden_states



class     lowercase     (  nn.Module  ):
							lowercase__    :							Dict										=				42
							lowercase__    :							int										=				0.0
							lowercase__    :							Union[str, Any]										=				1
							lowercase__    :							Union[str, Any]										=				1
							lowercase__    :							int										=				False
							lowercase__    :							Optional[Any]										=				False
							lowercase__    :							Dict										=				jnp.floataa




							def 						__snake_case(    self :       Any   )       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[
												    FlaxResnetBlockaD(
												        in_channels=self.in_channels						,				out_channels=self.in_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
												]

												SCREAMING_SNAKE_CASE           =							[]

												for _ in range(self.num_layers   ):
																	SCREAMING_SNAKE_CASE           =							FlaxTransformeraDModel(
																	    in_channels=self.in_channels						,				n_heads=self.num_attention_heads						,				d_head=self.in_channels // self.num_attention_heads						,				depth=1						,				use_linear_projection=self.use_linear_projection						,				use_memory_efficient_attention=self.use_memory_efficient_attention						,				dtype=self.dtype						,				)
																	attentions.append(lowerCAmelCase_   )

																	SCREAMING_SNAKE_CASE           =							FlaxResnetBlockaD(
																	    in_channels=self.in_channels						,				out_channels=self.in_channels						,				dropout_prob=self.dropout						,				dtype=self.dtype						,				)
																	resnets.append(lowerCAmelCase_   )

												SCREAMING_SNAKE_CASE           =							resnets
												SCREAMING_SNAKE_CASE           =							attentions





							def __call__(    self :       List[str]						,				_UpperCamelCase :       str						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Optional[int]=True   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.resnets[0](lowerCAmelCase_						,				lowerCAmelCase_   )
												for attn, resnet in zip(self.attentions						,				self.resnets[1:]   ):
																	SCREAMING_SNAKE_CASE           =							attn(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )
																	SCREAMING_SNAKE_CASE           =							resnet(lowerCAmelCase_						,				lowerCAmelCase_						,				deterministic=lowerCAmelCase_   )

												return hidden_states










'''simple docstring'''


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


_snake_case : Tuple  				=						logging.get_logger(__name__)

_snake_case : Optional[int]  				=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case : List[Any]  				=						{
    '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'
        ),
    },
}

_snake_case : Union[str, Any]  				=						{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


_snake_case : Tuple  				=						{
    '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       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_INIT_CONFIGURATION
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							     =							SqueezeBertTokenizer







							def __init__(    self			:    str				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    List[str]="[UNK]"				,     lowerCAmelCase_			:    Union[str, Any]="[SEP]"				,     lowerCAmelCase_			:    Optional[Any]="[PAD]"				,     lowerCAmelCase_			:    Any="[CLS]"				,     lowerCAmelCase_			:    List[str]="[MASK]"				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    List[Any]=None				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				int:


														"""simple docstring"""


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

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

														_a      	=							do_lower_case







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Optional[Any]=None   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							[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   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[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   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														_a      	=							self._tokenizer.model.save(lowerCAmelCase_				,     name=lowerCAmelCase_   )
														return tuple(lowerCAmelCase_   )

