





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




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):
 __SCREAMING_SNAKE_CASE							       =	42
 __SCREAMING_SNAKE_CASE							       =	jnp.floataa



 def 				UpperCamelCase			(							self ):
        A__											=						nn.Conv(
            self.out_channels,kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)




 def __call__(							self,__lowerCamelCase ):
        A__											=						hidden_states.shape
        A__											=						jax.image.resize(
            __SCREAMING_SNAKE_CASE,shape=(batch, height * 2, width * 2, channels),method='''nearest''',)
        A__											=						self.conv(__SCREAMING_SNAKE_CASE )
        return hidden_states




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):
 __SCREAMING_SNAKE_CASE							       =	42
 __SCREAMING_SNAKE_CASE							       =	jnp.floataa



 def 				UpperCamelCase			(							self ):
        A__											=						nn.Conv(
            self.out_channels,kernel_size=(3, 3),strides=(2, 2),padding=((1, 1), (1, 1)),dtype=self.dtype,)




 def __call__(							self,__lowerCamelCase ):
        A__											=						self.conv(__SCREAMING_SNAKE_CASE )
        return hidden_states




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):
 __SCREAMING_SNAKE_CASE							       =	42
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	0.0
 __SCREAMING_SNAKE_CASE							       =	None
 __SCREAMING_SNAKE_CASE							       =	jnp.floataa



 def 				UpperCamelCase			(							self ):
        A__											=						self.in_channels if self.out_channels is None else self.out_channels

        A__											=						nn.GroupNorm(num_groups=32,epsilon=1E-5 )
        A__											=						nn.Conv(
            __SCREAMING_SNAKE_CASE,kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)

        A__											=						nn.Dense(__SCREAMING_SNAKE_CASE,dtype=self.dtype )

        A__											=						nn.GroupNorm(num_groups=32,epsilon=1E-5 )
        A__											=						nn.Dropout(self.dropout_prob )
        A__											=						nn.Conv(
            __SCREAMING_SNAKE_CASE,kernel_size=(3, 3),strides=(1, 1),padding=((1, 1), (1, 1)),dtype=self.dtype,)

        A__											=						self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        A__											=						None
        if use_nin_shortcut:
               A__											=						nn.Conv(
                   __SCREAMING_SNAKE_CASE,kernel_size=(1, 1),strides=(1, 1),padding='''VALID''',dtype=self.dtype,)




 def __call__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=True ):
        A__											=						hidden_states
        A__											=						self.norma(__SCREAMING_SNAKE_CASE )
        A__											=						nn.swish(__SCREAMING_SNAKE_CASE )
        A__											=						self.conva(__SCREAMING_SNAKE_CASE )

        A__											=						self.time_emb_proj(nn.swish(__SCREAMING_SNAKE_CASE ) )
        A__											=						jnp.expand_dims(jnp.expand_dims(__SCREAMING_SNAKE_CASE,1 ),1 )
        A__											=						hidden_states + temb

        A__											=						self.norma(__SCREAMING_SNAKE_CASE )
        A__											=						nn.swish(__SCREAMING_SNAKE_CASE )
        A__											=						self.dropout(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
        A__											=						self.conva(__SCREAMING_SNAKE_CASE )

        if self.conv_shortcut is not None:
               A__											=						self.conv_shortcut(__SCREAMING_SNAKE_CASE )

        return hidden_states + residual







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



