




"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


a      =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a      =							{
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

a      =							{
    'unc-nlp/lxmert-base-uncased': 5_1_2,
}

a      =							{
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class       SCREAMING_SNAKE_CASE__					(					UpperCAmelCase__					):
						_a    						=						VOCAB_FILES_NAMES
						_a    						=						PRETRAINED_VOCAB_FILES_MAP
						_a    						=						PRETRAINED_INIT_CONFIGURATION
						_a    						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a    						=						LxmertTokenizer


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

										lowerCAmelCase  =				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
										):
														lowerCAmelCase  =				getattr(lowerCAmelCase						,						normalizer_state.pop("""type"""					)					)
														lowerCAmelCase  =				do_lower_case
														lowerCAmelCase  =				strip_accents
														lowerCAmelCase  =				tokenize_chinese_chars
														lowerCAmelCase  =				normalizer_class(**lowerCAmelCase					)

										lowerCAmelCase  =				do_lower_case


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]=None					):
										lowerCAmelCase  =				[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				:			Dict						,						lowerCAmelCase				:			List[int]						,						lowerCAmelCase				:			Optional[List[int]] = None					):
										lowerCAmelCase  =				[self.sep_token_id]
										lowerCAmelCase  =				[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				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[str] = None					):
										lowerCAmelCase  =				self._tokenizer.model.save(lowerCAmelCase						,						name=lowerCAmelCase					)
										return tuple(lowerCAmelCase					)











"""simple docstring"""







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

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






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

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

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

														lowerCAmelCase  =				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					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

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




						def __call__(							self				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			int=True					):
										lowerCAmelCase  =				()

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

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

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				[]

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

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

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




						def __call__(							self				:			int						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			List[str]=True					):
										lowerCAmelCase  =				()

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

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

										return hidden_states, output_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						True
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

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

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

														lowerCAmelCase  =				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					)

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions

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




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Dict=True					):
										for resnet, attn in zip(self.resnets						,						self.attentions					):
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						42
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						True
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				[]

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

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

										lowerCAmelCase  =				resnets

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




						def __call__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Dict=True					):
										for resnet in self.resnets:
														# pop res hidden states
														lowerCAmelCase  =				res_hidden_states_tuple[-1]
														lowerCAmelCase  =				res_hidden_states_tuple[:-1]
														lowerCAmelCase  =				jnp.concatenate((hidden_states, res_hidden_states)						,						axis=-1					)

														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										if self.add_upsample:
														lowerCAmelCase  =				self.upsamplers_a(lowerCAmelCase					)

										return hidden_states






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):
						_a    						=						42
						_a    						=						0.0
						_a    						=						1
						_a    						=						1
						_a    						=						False
						_a    						=						False
						_a    						=						jnp.floataa


						def   __lowercase (							self				:			List[Any]					):
										# there is always at least one resnet
										lowerCAmelCase  =				[
										    FlaxResnetBlockaD(
										        in_channels=self.in_channels						,						out_channels=self.in_channels						,						dropout_prob=self.dropout						,						dtype=self.dtype						,						)
										]

										lowerCAmelCase  =				[]

										for _ in range(self.num_layers					):
														lowerCAmelCase  =				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					)

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

										lowerCAmelCase  =				resnets
										lowerCAmelCase  =				attentions




						def __call__(							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]=True					):
										lowerCAmelCase  =				self.resnets[0](lowerCAmelCase						,						lowerCAmelCase					)
										for attn, resnet in zip(self.attentions						,						self.resnets[1:]					):
														lowerCAmelCase  =				attn(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)
														lowerCAmelCase  =				resnet(lowerCAmelCase						,						lowerCAmelCase						,						deterministic=lowerCAmelCase					)

										return hidden_states





