





'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class 				__lowerCAmelCase     (    UpperCAmelCase__ ):






       """simple docstring"""
       _snake_case	:     List[str]										=    4_2

class 				__lowerCAmelCase     (    UpperCAmelCase__						,						UpperCAmelCase__ ):






       """simple docstring"""





       @register_to_config
       def __init__(							self   :		List[str]       , lowerCAmelCase__   :		Optional[int] = 65536       , lowerCAmelCase__   :		Optional[Any] = None       , lowerCAmelCase__   :		Dict = 2       , lowerCAmelCase__   :		Optional[Any] = 2       , lowerCAmelCase__   :		List[Any] = 0       , lowerCAmelCase__   :		str = "fourier"       , lowerCAmelCase__   :		str = True       , lowerCAmelCase__   :		str = False       , lowerCAmelCase__   :		List[Any] = 0.0       , lowerCAmelCase__   :		Union[str, Any] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       , lowerCAmelCase__   :		str = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       , lowerCAmelCase__   :		int = "UNetMidBlock1D"       , lowerCAmelCase__   :		Optional[Any] = None       , lowerCAmelCase__   :		List[Any] = (32, 32, 64)       , lowerCAmelCase__   :		List[Any] = None       , lowerCAmelCase__   :		Union[str, Any] = 8       , lowerCAmelCase__   :		Optional[Any] = 1       , lowerCAmelCase__   :		List[str] = False       , )    ->     List[str]:

            '''simple docstring'''




            super().__init__()
            _UpperCamelCase				    =       sample_size

            # time
            if time_embedding_type == "fourier":
                 _UpperCamelCase				    =       GaussianFourierProjection(
                     embedding_size=8       , set_W_to_weight=_a       , log=_a       , flip_sin_to_cos=_a     )
                 _UpperCamelCase				    =       2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                 _UpperCamelCase				    =       Timesteps(
                     block_out_channels[0]       , flip_sin_to_cos=_a       , downscale_freq_shift=_a     )
                 _UpperCamelCase				    =       block_out_channels[0]

            if use_timestep_embedding:
                 _UpperCamelCase				    =       block_out_channels[0] * 4
                 _UpperCamelCase				    =       TimestepEmbedding(
                     in_channels=_a       , time_embed_dim=_a       , act_fn=_a       , out_dim=block_out_channels[0]       , )

            _UpperCamelCase				    =       nn.ModuleList([]     )
            _UpperCamelCase				    =       None
            _UpperCamelCase				    =       nn.ModuleList([]     )
            _UpperCamelCase				    =       None

            # down
            _UpperCamelCase				    =       in_channels
            for i, down_block_type in enumerate(_a     ):
                 _UpperCamelCase				    =       output_channel
                 _UpperCamelCase				    =       block_out_channels[i]

                 if i == 0:
                      input_channel += extra_in_channels

                 _UpperCamelCase				    =       i == len(_a     ) - 1

                 _UpperCamelCase				    =       get_down_block(
                     _a       , num_layers=_a       , in_channels=_a       , out_channels=_a       , temb_channels=block_out_channels[0]       , add_downsample=not is_final_block or downsample_each_block       , )
                 self.down_blocks.append(_a     )

            # mid
            _UpperCamelCase				    =       get_mid_block(
                _a       , in_channels=block_out_channels[-1]       , mid_channels=block_out_channels[-1]       , out_channels=block_out_channels[-1]       , embed_dim=block_out_channels[0]       , num_layers=_a       , add_downsample=_a       , )

            # up
            _UpperCamelCase				    =       list(reversed(_a     )     )
            _UpperCamelCase				    =       reversed_block_out_channels[0]
            if out_block_type is None:
                 _UpperCamelCase				    =       out_channels
            else:
                 _UpperCamelCase				    =       block_out_channels[0]

            for i, up_block_type in enumerate(_a     ):
                 _UpperCamelCase				    =       output_channel
                 _UpperCamelCase				    =       (
                     reversed_block_out_channels[i + 1] if i < len(_a     ) - 1 else final_upsample_channels
                 )

                 _UpperCamelCase				    =       i == len(_a     ) - 1

                 _UpperCamelCase				    =       get_up_block(
                     _a       , num_layers=_a       , in_channels=_a       , out_channels=_a       , temb_channels=block_out_channels[0]       , add_upsample=not is_final_block       , )
                 self.up_blocks.append(_a     )
                 _UpperCamelCase				    =       output_channel

            # out
            _UpperCamelCase				    =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       , 32     )
            _UpperCamelCase				    =       get_out_block(
                out_block_type=_a       , num_groups_out=_a       , embed_dim=block_out_channels[0]       , out_channels=_a       , act_fn=_a       , fc_dim=block_out_channels[-1] // 4       , )







       def snake_case__						(							self   :		Optional[int]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		Tuple = True       , )    ->     Tuple:

            '''simple docstring'''




            _UpperCamelCase				    =       timestep
            if not torch.is_tensor(_a     ):
                 _UpperCamelCase				    =       torch.tensor([timesteps]       , dtype=torch.long       , device=sample.device     )
            elif torch.is_tensor(_a     ) and len(timesteps.shape     ) == 0:
                 _UpperCamelCase				    =       timesteps[None].to(sample.device     )

            _UpperCamelCase				    =       self.time_proj(_a     )
            if self.config.use_timestep_embedding:
                 _UpperCamelCase				    =       self.time_mlp(_a     )
            else:
                 _UpperCamelCase				    =       timestep_embed[..., None]
                 _UpperCamelCase				    =       timestep_embed.repeat([1, 1, sample.shape[2]]     ).to(sample.dtype     )
                 _UpperCamelCase				    =       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])     )

            # 2. down
            _UpperCamelCase				    =       ()
            for downsample_block in self.down_blocks:
                 _UpperCamelCase    ,				_UpperCamelCase				    =       downsample_block(hidden_states=_a       , temb=_a     )
                 down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                 _UpperCamelCase				    =       self.mid_block(_a       , _a     )

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks     ):
                 _UpperCamelCase				    =       down_block_res_samples[-1:]
                 _UpperCamelCase				    =       down_block_res_samples[:-1]
                 _UpperCamelCase				    =       upsample_block(_a       , res_hidden_states_tuple=_a       , temb=_a     )

            # 5. post-process
            if self.out_block:
                 _UpperCamelCase				    =       self.out_block(_a       , _a     )

            if not return_dict:
                 return (sample,)

            return UNetaDOutput(sample=_a     )









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_attention_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_choices





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

							lowerCamelCase			      =					None
							if self.use_attention_mask:
										lowerCamelCase			      =					random_attention_mask([self.batch_size, self.seq_length] )

							lowerCamelCase			      =					None
							if self.use_token_type_ids:
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size )

							lowerCamelCase			      =					RoFormerConfig(
							    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, is_decoder=_a		, initializer_range=self.initializer_range		, )

							return config, input_ids, token_type_ids, attention_mask





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






@require_flax
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						True

				__UpperCamelCase			  =						(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerModelTester(self )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase			      =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		, from_pt=_a )
										lowerCamelCase			      =					model(np.ones((1, 1) ) )
										self.assertIsNotNone(_a )






@require_flax
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					jnp.array([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					(1, 6, vocab_size)
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					jnp.array(
							    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )



