


"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					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 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

       snake_case					:   Optional[Any]         =					tokenizer.tokenize("UNwant\u00E9d,running"		)
       self.assertListEqual(UpperCamelCase__		,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[7, 4, 5, 10, 8, 9]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass









"""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 _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[]
       snake_case					:   Tuple         =					[]

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

           snake_case					:   Optional[int]         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   Dict         =					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(UpperCamelCase__		)

       snake_case					:   List[Any]         =					resnets
       snake_case					:   List[Any]         =					attentions

       if self.add_downsample:
           snake_case					:   Optional[Any]         =					FlaxDownsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet, attn in zip(self.resnets		,			self.attentions		):
           snake_case					:   str         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   int         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Any         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]

       for i in range(self.num_layers		):
           snake_case					:   List[str]         =					self.in_channels if i == 0 else self.out_channels

           snake_case					:   str         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)
       snake_case					:   str         =					resnets

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







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet in self.resnets:
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Tuple         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







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

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       snake_case					:   Union[str, Any]         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Optional[Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Union[str, Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Optional[Any]         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   int         =					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(UpperCamelCase__		)

       snake_case					:   str         =					resnets
       snake_case					:   List[Any]         =					attentions

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







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Optional[int]:

       '''simple docstring'''




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

           snake_case					:   List[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Any         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   int         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







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

       '''simple docstring'''




       snake_case					:   str         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Optional[Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Dict         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Optional[int]         =					resnets

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







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	int:

       '''simple docstring'''




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

           snake_case					:   Dict         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   Optional[int]         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Dict         =					[
           FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
       ]

       snake_case					:   Any         =					[]

       for _ in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					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(UpperCamelCase__		)

           snake_case					:   List[Any]         =					FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					resnets
       snake_case					:   str         =					attentions







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.resnets[0](UpperCamelCase__		,			UpperCamelCase__		)
       for attn, resnet in zip(self.attentions		,			self.resnets[1:]		):
           snake_case					:   Union[str, Any]         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       return hidden_states





