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				):






    '''simple docstring'''






    a__			:		int
    a__			:		int
    a__			:		float      			=   0.0
    a__			:		int      			=   1
    a__			:		int      			=   1
    a__			:		bool      			=   True
    a__			:		bool      			=   False
    a__			:		bool      			=   False
    a__			:		bool      			=   False
    a__			:		jnp.dtype      			=   jnp.floataa







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :int    						=	[]
        __UpperCamelCase  :Any    						=	[]

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

            __UpperCamelCase  :Any    						=	FlaxResnetBlockaD(
                in_channels=__lowercase				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
            resnets.append(__lowercase)

            __UpperCamelCase  :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(__lowercase)

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

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







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

        for resnet, attn in zip(self.resnets				,				self.attentions):
            __UpperCamelCase  :Any    						=	resnet(__lowercase				,				__lowercase				,				deterministic=__lowercase)
            __UpperCamelCase  :Union[str, Any]    						=	attn(__lowercase				,				__lowercase				,				deterministic=__lowercase)
            output_states += (hidden_states,)

        if self.add_downsample:
            __UpperCamelCase  :Optional[int]    						=	self.downsamplers_a(__lowercase)
            output_states += (hidden_states,)

        return hidden_states, output_states


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    a__			:		int
    a__			:		int
    a__			:		float      			=   0.0
    a__			:		int      			=   1
    a__			:		bool      			=   True
    a__			:		jnp.dtype      			=   jnp.floataa







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :List[str]    						=	[]

        for i in range(self.num_layers):
            __UpperCamelCase  :Optional[Any]    						=	self.in_channels if i == 0 else self.out_channels

            __UpperCamelCase  :Optional[int]    						=	FlaxResnetBlockaD(
                in_channels=__lowercase				,				out_channels=self.out_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
            resnets.append(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	resnets

        if self.add_downsample:
            __UpperCamelCase  :List[str]    						=	FlaxDownsampleaD(self.out_channels				,				dtype=self.dtype)







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

        for resnet in self.resnets:
            __UpperCamelCase  :Union[str, Any]    						=	resnet(__lowercase				,				__lowercase				,				deterministic=__lowercase)
            output_states += (hidden_states,)

        if self.add_downsample:
            __UpperCamelCase  :int    						=	self.downsamplers_a(__lowercase)
            output_states += (hidden_states,)

        return hidden_states, output_states


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    a__			:		int
    a__			:		int
    a__			:		int
    a__			:		float      			=   0.0
    a__			:		int      			=   1
    a__			:		int      			=   1
    a__			:		bool      			=   True
    a__			:		bool      			=   False
    a__			:		bool      			=   False
    a__			:		bool      			=   False
    a__			:		jnp.dtype      			=   jnp.floataa







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :str    						=	[]
        __UpperCamelCase  :int    						=	[]

        for i in range(self.num_layers):
            __UpperCamelCase  :List[str]    						=	self.in_channels if (i == self.num_layers - 1) else self.out_channels
            __UpperCamelCase  :List[str]    						=	self.prev_output_channel if i == 0 else self.out_channels

            __UpperCamelCase  :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(__lowercase)

            __UpperCamelCase  :Any    						=	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(__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	resnets
        __UpperCamelCase  :List[str]    						=	attentions

        if self.add_upsample:
            __UpperCamelCase  :Optional[Any]    						=	FlaxUpsampleaD(self.out_channels				,				dtype=self.dtype)







    def __call__(			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase=True) ->			Tuple:

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

            __UpperCamelCase  :Any    						=	resnet(__lowercase				,				__lowercase				,				deterministic=__lowercase)
            __UpperCamelCase  :Optional[Any]    						=	attn(__lowercase				,				__lowercase				,				deterministic=__lowercase)

        if self.add_upsample:
            __UpperCamelCase  :str    						=	self.upsamplers_a(__lowercase)

        return hidden_states


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    a__			:		int
    a__			:		int
    a__			:		int
    a__			:		float      			=   0.0
    a__			:		int      			=   1
    a__			:		bool      			=   True
    a__			:		jnp.dtype      			=   jnp.floataa







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :int    						=	[]

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

            __UpperCamelCase  :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(__lowercase)

        __UpperCamelCase  :Tuple    						=	resnets

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







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

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

            __UpperCamelCase  :Optional[Any]    						=	resnet(__lowercase				,				__lowercase				,				deterministic=__lowercase)

        if self.add_upsample:
            __UpperCamelCase  :Any    						=	self.upsamplers_a(__lowercase)

        return hidden_states


class 			lowerCamelCase_  (     nn.Module				):






    '''simple docstring'''






    a__			:		int
    a__			:		float      			=   0.0
    a__			:		int      			=   1
    a__			:		int      			=   1
    a__			:		bool      			=   False
    a__			:		bool      			=   False
    a__			:		jnp.dtype      			=   jnp.floataa







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :str    						=	[
            FlaxResnetBlockaD(
                in_channels=self.in_channels				,				out_channels=self.in_channels				,				dropout_prob=self.dropout				,				dtype=self.dtype				,				)
        ]

        __UpperCamelCase  :str    						=	[]

        for _ in range(self.num_layers):
            __UpperCamelCase  :List[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(__lowercase)

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

        __UpperCamelCase  :str    						=	resnets
        __UpperCamelCase  :str    						=	attentions







    def __call__(			self				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase=True) ->			int:
        __UpperCamelCase  :int    						=	self.resnets[0](__lowercase				,				__lowercase)
        for attn, resnet in zip(self.attentions				,				self.resnets[1:]):
            __UpperCamelCase  :Tuple    						=	attn(__lowercase				,				__lowercase				,				deterministic=__lowercase)
            __UpperCamelCase  :List[str]    						=	resnet(__lowercase				,				__lowercase				,				deterministic=__lowercase)

        return hidden_states







import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   CpmAntTokenizer
    a__			:		Optional[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Any:
        super().setUp()

        __UpperCamelCase  :Optional[int]    						=	[
            '''<d>''',
            '''</d>''',
            '''<s>''',
            '''</s>''',
            '''</_>''',
            '''<unk>''',
            '''<pad>''',
            '''</n>''',
            '''我''',
            '''是''',
            '''C''',
            '''P''',
            '''M''',
            '''A''',
            '''n''',
            '''t''',
        ]
        __UpperCamelCase  :Optional[int]    						=	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]))







    @tooslow
    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
        __UpperCamelCase  :Dict    						=	'''今天天气真好！'''
        __UpperCamelCase  :Tuple    						=	['''今天''', '''天气''', '''真''', '''好''', '''！''']
        __UpperCamelCase  :Optional[Any]    						=	tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)
        __UpperCamelCase  :int    						=	'''今天天气真好！'''
        __UpperCamelCase  :List[str]    						=	[tokenizer.bos_token] + tokens

        __UpperCamelCase  :List[str]    						=	[6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.decode(__lowercase)
        self.assertEqual(__lowercase				,				__lowercase)





