



'''simple docstring'''

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.generation import DisjunctiveConstraint

@require_torch
class __snake_case( unittest.TestCase   ):


						'''simple docstring'''



						def 							__snake_case       (   self )      ->      Tuple:
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							lowerCAmelCase       =      [[1, 2, 4], [1, 2, 3, 4]]
							lowerCAmelCase       =      DisjunctiveConstraint(_UpperCamelCase )
							self.assertTrue(isinstance(dc.token_ids      ,			_UpperCamelCase ) )

							with self.assertRaises(_UpperCamelCase ):
								DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

							with self.assertRaises(_UpperCamelCase ):
								DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )


						def 							__snake_case       (   self )      ->      int:
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							lowerCAmelCase       =      [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(_UpperCamelCase ):
								DisjunctiveConstraint(_UpperCamelCase )  # fails here


						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      [[1, 2, 3], [1, 2, 4]]

							lowerCAmelCase       =      DisjunctiveConstraint(_UpperCamelCase )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(1 )
							lowerCAmelCase       =      stepped is True and completed is False and reset is False
							self.assertTrue(_UpperCamelCase )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.current_seq == [1] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(2 )
							lowerCAmelCase       =      stepped is True and completed is False and reset is False
							self.assertTrue(_UpperCamelCase )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.current_seq == [1, 2] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(3 )
							lowerCAmelCase       =      stepped is True and completed is True and reset is False
							self.assertTrue(_UpperCamelCase )
							self.assertTrue(dc.completed )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3] )




						def 							__snake_case       (   self )      ->      Tuple:
							lowerCAmelCase       =      [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							lowerCAmelCase       =      DisjunctiveConstraint(_UpperCamelCase )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(1 )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.current_seq == [1] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(2 )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.current_seq == [1, 2] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(4 )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.current_seq == [1, 2, 4] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(5 )
							self.assertTrue(dc.completed )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

							dc.reset()

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(1 )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.remaining() == 3 )
							self.assertTrue(dc.current_seq == [1] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(2 )
							self.assertTrue(not dc.completed )
							self.assertTrue(dc.remaining() == 2 )
							self.assertTrue(dc.current_seq == [1, 2] )

							lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase       =      dc.update(5 )
							self.assertTrue(dc.completed )  # Completed!
							self.assertTrue(dc.remaining() == 0 )
							self.assertTrue(dc.current_seq == [1, 2, 5] )



'''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					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      int:
            _A				=							[]
            _A				=							[]

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

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

                 _A				=							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   )

            _A				=							resnets
            _A				=							attentions

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






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            _A				=							()

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

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

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

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

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

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






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

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

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

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      str:
            _A				=							[]
            _A				=							[]

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

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

                 _A				=							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   )

            _A				=							resnets
            _A				=							attentions

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






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

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

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

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

            _A				=							resnets

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






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            for resnet in self.resnets:
                 # pop res hidden states
                 _A				=							res_hidden_states_tuple[-1]
                 _A				=							res_hidden_states_tuple[:-1]
                 _A				=							jnp.concatenate((hidden_states, res_hidden_states)			,     axis=-1   )

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Dict:
            # there is always at least one resnet
            _A				=							[
                FlaxResnetBlockaD(
                    in_channels=self.in_channels			,     out_channels=self.in_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
            ]

            _A				=							[]

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

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

            _A				=							resnets
            _A				=							attentions






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      List[Any]:
            _A				=							self.resnets[0](_UpperCamelCase			,     _UpperCamelCase   )
            for attn, resnet in zip(self.attentions			,     self.resnets[1:]   ):
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            return hidden_states





