


"""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 UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   42




class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							@register_to_config
							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Optional[Union[int, Tuple[int, int]]] = None		,			lowerCamelCase_			:  int = 3		,			lowerCamelCase_			:  int = 3		,			lowerCamelCase_			:  bool = False		,			lowerCamelCase_			:  str = "positional"		,			lowerCamelCase_			:  int = 0		,			lowerCamelCase_			:  bool = True		,			lowerCamelCase_			:  Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,			lowerCamelCase_			:  Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,			lowerCamelCase_			:  Tuple[int] = (224, 448, 672, 896)		,			lowerCamelCase_			:  int = 2		,			lowerCamelCase_			:  float = 1		,			lowerCamelCase_			:  int = 1		,			lowerCamelCase_			:  str = "conv"		,			lowerCamelCase_			:  str = "conv"		,			lowerCamelCase_			:  str = "silu"		,			lowerCamelCase_			:  Optional[int] = 8		,			lowerCamelCase_			:  int = 32		,			lowerCamelCase_			:  float = 1E-5		,			lowerCamelCase_			:  str = "default"		,			lowerCamelCase_			:  bool = True		,			lowerCamelCase_			:  Optional[str] = None		,			lowerCamelCase_			:  Optional[int] = None		,			):
													super().__init__()

													UpperCAmelCase__												=     sample_size
													UpperCAmelCase__												=     block_out_channels[0] * 4

													# Check inputs
													if len(lowerCamelCase_					) != len(lowerCamelCase_					):
																			raise ValueError(
																			    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""					)

													if len(lowerCamelCase_					) != len(lowerCamelCase_					):
																			raise ValueError(
																			    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""					)

													# input
													UpperCAmelCase__												=     nn.Convad(lowerCamelCase_		,			block_out_channels[0]		,			kernel_size=3		,			padding=(1, 1)					)

													# time
													if time_embedding_type == "fourier":
																			UpperCAmelCase__												=     GaussianFourierProjection(embedding_size=block_out_channels[0]		,			scale=16					)
																			UpperCAmelCase__												=     2 * block_out_channels[0]
													elif time_embedding_type == "positional":
																			UpperCAmelCase__												=     Timesteps(block_out_channels[0]		,			lowerCamelCase_		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     block_out_channels[0]

													UpperCAmelCase__												=     TimestepEmbedding(lowerCamelCase_		,			lowerCamelCase_					)

													# class embedding
													if class_embed_type is None and num_class_embeds is not None:
																			UpperCAmelCase__												=     nn.Embedding(lowerCamelCase_		,			lowerCamelCase_					)
													elif class_embed_type == "timestep":
																			UpperCAmelCase__												=     TimestepEmbedding(lowerCamelCase_		,			lowerCamelCase_					)
													elif class_embed_type == "identity":
																			UpperCAmelCase__												=     nn.Identity(lowerCamelCase_		,			lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     None

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

													# down
													UpperCAmelCase__												=     block_out_channels[0]
													for i, down_block_type in enumerate(lowerCamelCase_					):
																			UpperCAmelCase__												=     output_channel
																			UpperCAmelCase__												=     block_out_channels[i]
																			UpperCAmelCase__												=     i == len(lowerCamelCase_					) - 1

																			UpperCAmelCase__												=     get_down_block(
																			    lowerCamelCase_		,			num_layers=lowerCamelCase_		,			in_channels=lowerCamelCase_		,			out_channels=lowerCamelCase_		,			temb_channels=lowerCamelCase_		,			add_downsample=not is_final_block		,			resnet_eps=lowerCamelCase_		,			resnet_act_fn=lowerCamelCase_		,			resnet_groups=lowerCamelCase_		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,			downsample_padding=lowerCamelCase_		,			resnet_time_scale_shift=lowerCamelCase_		,			downsample_type=lowerCamelCase_		,			)
																			self.down_blocks.append(lowerCamelCase_					)

													# mid
													UpperCAmelCase__												=     UNetMidBlockaD(
													    in_channels=block_out_channels[-1]		,			temb_channels=lowerCamelCase_		,			resnet_eps=lowerCamelCase_		,			resnet_act_fn=lowerCamelCase_		,			output_scale_factor=lowerCamelCase_		,			resnet_time_scale_shift=lowerCamelCase_		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,			resnet_groups=lowerCamelCase_		,			add_attention=lowerCamelCase_		,			)

													# up
													UpperCAmelCase__												=     list(reversed(lowerCamelCase_					)					)
													UpperCAmelCase__												=     reversed_block_out_channels[0]
													for i, up_block_type in enumerate(lowerCamelCase_					):
																			UpperCAmelCase__												=     output_channel
																			UpperCAmelCase__												=     reversed_block_out_channels[i]
																			UpperCAmelCase__												=     reversed_block_out_channels[min(i + 1		,			len(lowerCamelCase_					) - 1					)]

																			UpperCAmelCase__												=     i == len(lowerCamelCase_					) - 1

																			UpperCAmelCase__												=     get_up_block(
																			    lowerCamelCase_		,			num_layers=layers_per_block + 1		,			in_channels=lowerCamelCase_		,			out_channels=lowerCamelCase_		,			prev_output_channel=lowerCamelCase_		,			temb_channels=lowerCamelCase_		,			add_upsample=not is_final_block		,			resnet_eps=lowerCamelCase_		,			resnet_act_fn=lowerCamelCase_		,			resnet_groups=lowerCamelCase_		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,			resnet_time_scale_shift=lowerCamelCase_		,			upsample_type=lowerCamelCase_		,			)
																			self.up_blocks.append(lowerCamelCase_					)
																			UpperCAmelCase__												=     output_channel

													# out
													UpperCAmelCase__												=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,			32					)
													UpperCAmelCase__												=     nn.GroupNorm(num_channels=block_out_channels[0]		,			num_groups=lowerCamelCase_		,			eps=lowerCamelCase_					)
													UpperCAmelCase__												=     nn.SiLU()
													UpperCAmelCase__												=     nn.Convad(block_out_channels[0]		,			lowerCamelCase_		,			kernel_size=3		,			padding=1					)






							def        lowerCAmelCase  (    self			:  List[Any]		,			lowerCamelCase_			:  torch.FloatTensor		,			lowerCamelCase_			:  Union[torch.Tensor, float, int]		,			lowerCamelCase_			:  Optional[torch.Tensor] = None		,			lowerCamelCase_			:  bool = True		,			):
													# 0. center input if necessary
													if self.config.center_input_sample:
																			UpperCAmelCase__												=     2 * sample - 1.0

													# 1. time
													UpperCAmelCase__												=     timestep
													if not torch.is_tensor(lowerCamelCase_					):
																			UpperCAmelCase__												=     torch.tensor([timesteps]		,			dtype=torch.long		,			device=sample.device					)
													elif torch.is_tensor(lowerCamelCase_					) and len(timesteps.shape					) == 0:
																			UpperCAmelCase__												=     timesteps[None].to(sample.device					)

													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													UpperCAmelCase__												=     timesteps * torch.ones(sample.shape[0]		,			dtype=timesteps.dtype		,			device=timesteps.device					)

													UpperCAmelCase__												=     self.time_proj(lowerCamelCase_					)

													# timesteps does not contain any weights and will always return f32 tensors
													# but time_embedding might actually be running in fp16. so we need to cast here.
													# there might be better ways to encapsulate this.
													UpperCAmelCase__												=     t_emb.to(dtype=self.dtype					)
													UpperCAmelCase__												=     self.time_embedding(lowerCamelCase_					)

													if self.class_embedding is not None:
																			if class_labels is None:
																									raise ValueError('''class_labels should be provided when doing class conditioning'''					)

																			if self.config.class_embed_type == "timestep":
																									UpperCAmelCase__												=     self.time_proj(lowerCamelCase_					)

																			UpperCAmelCase__												=     self.class_embedding(lowerCamelCase_					).to(dtype=self.dtype					)
																			UpperCAmelCase__												=     emb + class_emb

													# 2. pre-process
													UpperCAmelCase__												=     sample
													UpperCAmelCase__												=     self.conv_in(lowerCamelCase_					)

													# 3. down
													UpperCAmelCase__												=     (sample,)
													for downsample_block in self.down_blocks:
																			if hasattr(lowerCamelCase_		,			'''skip_conv'''					):
																									UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     downsample_block(
																									    hidden_states=lowerCamelCase_		,			temb=lowerCamelCase_		,			skip_sample=lowerCamelCase_					)
																			else:
																									UpperCAmelCase__						,					UpperCAmelCase__												=     downsample_block(hidden_states=lowerCamelCase_		,			temb=lowerCamelCase_					)

																			down_block_res_samples += res_samples

													# 4. mid
													UpperCAmelCase__												=     self.mid_block(lowerCamelCase_		,			lowerCamelCase_					)

													# 5. up
													UpperCAmelCase__												=     None
													for upsample_block in self.up_blocks:
																			UpperCAmelCase__												=     down_block_res_samples[-len(upsample_block.resnets					) :]
																			UpperCAmelCase__												=     down_block_res_samples[: -len(upsample_block.resnets					)]

																			if hasattr(lowerCamelCase_		,			'''skip_conv'''					):
																									UpperCAmelCase__						,					UpperCAmelCase__												=     upsample_block(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)
																			else:
																									UpperCAmelCase__												=     upsample_block(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

        # 6. post-process
													UpperCAmelCase__												=     self.conv_norm_out(lowerCamelCase_					)
													UpperCAmelCase__												=     self.conv_act(lowerCamelCase_					)
													UpperCAmelCase__												=     self.conv_out(lowerCamelCase_					)

													if skip_sample is not None:
																			sample += skip_sample

													if self.config.time_embedding_type == "fourier":
																			UpperCAmelCase__												=     timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]					)))					)
																			UpperCAmelCase__												=     sample / timesteps

													if not return_dict:
																			return (sample,)

													return UNetaDOutput(sample=lowerCamelCase_					)



"""simple docstring"""







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   SpeechaTextaTokenizer
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Dict					):
													super().setUp()

													UpperCAmelCase__												=     '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''					)
													UpperCAmelCase__												=     ['''he re</w> 123''', '''here a 1456''']
													UpperCAmelCase__												=     dict(zip(lowerCamelCase_		,			range(len(lowerCamelCase_					)					)					)					)

													UpperCAmelCase__												=     {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

													UpperCAmelCase__												=     tempfile.mkdtemp()
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''vocab_file''']					)
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''merges_file''']					)

													with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''					) as fp:
																			fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)

													with open(self.merges_file		,			'''w'''					) as fp:
																			fp.write('''\n'''.join(lowerCamelCase_					)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]		,			'''<s>'''					)
													self.assertEqual(vocab_keys[1]		,			'''<pad>'''					)
													self.assertEqual(vocab_keys[-1]		,			'''vocab'''					)
													self.assertEqual(len(lowerCamelCase_					)		,			14					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													self.assertEqual(self.get_tokenizer().vocab_size		,			14					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													# make sure @@ is correctly concatenated
													UpperCAmelCase__												=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
													UpperCAmelCase__												=     tokenizer.decode(lowerCamelCase_					)

													self.assertTrue(output_string == '''herecouple words ofthe'''					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			os.remove(os.path.join(lowerCamelCase_		,			'''merges.txt'''					)					)

																			# load tokenizer without merges file should not throw an error
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			# save tokenizer and load again
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													self.assertIsNotNone(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													if not self.test_slow_tokenizer:
																			return

													UpperCAmelCase__												=     inspect.signature(self.tokenizer_class.__init__					)
													UpperCAmelCase__												=     self.get_tokenizer()

													for parameter_name, parameter in signature.parameters.items():
																			if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																									self.assertIn(lowerCamelCase_		,			tokenizer.init_kwargs					)