





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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin

@dataclass
class  __A       (  a      ):
				__A								=			42




class  __A       (  a      ,   a      ):


				@register_to_config
				def __init__(						self				,	UpperCAmelCase_ = 32				,	UpperCAmelCase_ = 64				,	UpperCAmelCase_ = 20				,	UpperCAmelCase_ = 768				,	UpperCAmelCase_=77				,	UpperCAmelCase_=4				,	UpperCAmelCase_ = 0.0				,	UpperCAmelCase_ = "silu"				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = "linear"				,	UpperCAmelCase_ = "prd"				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	):
								super().__init__()
								lowerCamelCase			=num_attention_heads
								lowerCamelCase			=attention_head_dim
								lowerCamelCase			=num_attention_heads * attention_head_dim
								lowerCamelCase			=additional_embeddings

								lowerCamelCase			=time_embed_dim or inner_dim
								lowerCamelCase			=embedding_proj_dim or embedding_dim
								lowerCamelCase			=clip_embed_dim or embedding_dim

								lowerCamelCase			=Timesteps(UpperCAmelCase_				,	UpperCAmelCase_				,	0							)
								lowerCamelCase			=TimestepEmbedding(UpperCAmelCase_				,	UpperCAmelCase_				,	out_dim=UpperCAmelCase_				,	act_fn=UpperCAmelCase_							)

								lowerCamelCase			=nn.Linear(UpperCAmelCase_				,	UpperCAmelCase_							)

								if embedding_proj_norm_type is None:
												lowerCamelCase			=None
								elif embedding_proj_norm_type == "layer":
												lowerCamelCase			=nn.LayerNorm(UpperCAmelCase_							)
								else:
												raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""							)

								lowerCamelCase			=nn.Linear(UpperCAmelCase_				,	UpperCAmelCase_							)

								if encoder_hid_proj_type is None:
												lowerCamelCase			=None
								elif encoder_hid_proj_type == "linear":
												lowerCamelCase			=nn.Linear(UpperCAmelCase_				,	UpperCAmelCase_							)
								else:
												raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""							)

								lowerCamelCase			=nn.Parameter(torch.zeros(1				,	num_embeddings + additional_embeddings				,	UpperCAmelCase_							)							)

								if added_emb_type == "prd":
												lowerCamelCase			=nn.Parameter(torch.zeros(1				,	1				,	UpperCAmelCase_							)							)
								elif added_emb_type is None:
												lowerCamelCase			=None
								else:
												raise ValueError(
												    f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""							)

								lowerCamelCase			=nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	dropout=UpperCAmelCase_				,	activation_fn="""gelu"""				,	attention_bias=UpperCAmelCase_				,	)
								        for d in range(UpperCAmelCase_							)
								    ]							)

								if norm_in_type == "layer":
												lowerCamelCase			=nn.LayerNorm(UpperCAmelCase_							)
								elif norm_in_type is None:
												lowerCamelCase			=None
								else:
												raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""							)

								lowerCamelCase			=nn.LayerNorm(UpperCAmelCase_							)

								lowerCamelCase			=nn.Linear(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]				,	-10000.0							)
								causal_attention_mask.triu_(1							)
								lowerCamelCase			=causal_attention_mask[None, ...]
								self.register_buffer("""causal_attention_mask"""				,	UpperCAmelCase_				,	persistent=UpperCAmelCase_							)

								lowerCamelCase			=nn.Parameter(torch.zeros(1				,	UpperCAmelCase_							)							)
								lowerCamelCase			=nn.Parameter(torch.zeros(1				,	UpperCAmelCase_							)							)


				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def _snake_case    (						self							):
								lowerCamelCase			={}

								def fn_recursive_add_processors(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
												if hasattr(UpperCAmelCase_				,	"""set_processor"""							):
																lowerCamelCase			=module.processor

												for sub_name, child in module.named_children():
																fn_recursive_add_processors(f"""{name}.{sub_name}"""				,	UpperCAmelCase_				,	UpperCAmelCase_							)

												return processors

								for name, module in self.named_children():
												fn_recursive_add_processors(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)

								return processors


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=len(self.attn_processors.keys()							)

								if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) and len(UpperCAmelCase_							) != count:
												raise ValueError(
												    f"""A dict of processors was passed, but the number of processors {len(UpperCAmelCase_							)} does not match the"""
												    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

								def fn_recursive_attn_processor(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							):
												if hasattr(UpperCAmelCase_				,	"""set_processor"""							):
																if not isinstance(UpperCAmelCase_				,	UpperCAmelCase_							):
																				module.set_processor(UpperCAmelCase_							)
																else:
																				module.set_processor(processor.pop(f"""{name}.processor"""							)							)

												for sub_name, child in module.named_children():
																fn_recursive_attn_processor(f"""{name}.{sub_name}"""				,	UpperCAmelCase_				,	UpperCAmelCase_							)

								for name, module in self.named_children():
												fn_recursive_attn_processor(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							)


				def _snake_case    (						self							):
								self.set_attn_processor(AttnProcessor()							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):
								lowerCamelCase			=hidden_states.shape[0]

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

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								lowerCamelCase			=timesteps * torch.ones(UpperCAmelCase_				,	dtype=timesteps.dtype				,	device=timesteps.device							)

								lowerCamelCase			=self.time_proj(UpperCAmelCase_							)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might be fp16, so we need to cast here.
								lowerCamelCase			=timesteps_projected.to(dtype=self.dtype							)
								lowerCamelCase			=self.time_embedding(UpperCAmelCase_							)

								if self.embedding_proj_norm is not None:
												lowerCamelCase			=self.embedding_proj_norm(UpperCAmelCase_							)

								lowerCamelCase			=self.embedding_proj(UpperCAmelCase_							)
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
												lowerCamelCase			=self.encoder_hidden_states_proj(UpperCAmelCase_							)
								elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
												raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"""							)

								lowerCamelCase			=self.proj_in(UpperCAmelCase_							)

								lowerCamelCase			=self.positional_embedding.to(hidden_states.dtype							)

								lowerCamelCase			=[]
								lowerCamelCase			=0

								if encoder_hidden_states is not None:
												additional_embeds.append(UpperCAmelCase_							)
												additional_embeddings_len += encoder_hidden_states.shape[1]

								if len(proj_embeddings.shape							) == 2:
												lowerCamelCase			=proj_embeddings[:, None, :]

								if len(hidden_states.shape							) == 2:
												lowerCamelCase			=hidden_states[:, None, :]

								lowerCamelCase			=additional_embeds + [
								    proj_embeddings,
								    time_embeddings[:, None, :],
								    hidden_states,
								]

								if self.prd_embedding is not None:
												lowerCamelCase			=self.prd_embedding.to(hidden_states.dtype							).expand(UpperCAmelCase_				,	-1				,	-1							)
												additional_embeds.append(UpperCAmelCase_							)

								lowerCamelCase			=torch.cat(
								    UpperCAmelCase_				,	dim=1				,	)

								# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
								lowerCamelCase			=additional_embeddings_len + proj_embeddings.shape[1] + 1
								if positional_embeddings.shape[1] < hidden_states.shape[1]:
												lowerCamelCase			=F.pad(
												    UpperCAmelCase_				,	(
												        0,
												        0,
												        additional_embeddings_len,
												        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
												    )				,	value=0.0				,	)

								lowerCamelCase			=hidden_states + positional_embeddings

								if attention_mask is not None:
												lowerCamelCase			=(1 - attention_mask.to(hidden_states.dtype							)) * -10000.0
												lowerCamelCase			=F.pad(UpperCAmelCase_				,	(0, self.additional_embeddings)				,	value=0.0							)
												lowerCamelCase			=(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
												lowerCamelCase			=attention_mask.repeat_interleave(self.config.num_attention_heads				,	dim=0							)

								if self.norm_in is not None:
												lowerCamelCase			=self.norm_in(UpperCAmelCase_							)

								for block in self.transformer_blocks:
												lowerCamelCase			=block(UpperCAmelCase_				,	attention_mask=UpperCAmelCase_							)

								lowerCamelCase			=self.norm_out(UpperCAmelCase_							)

								if self.prd_embedding is not None:
												lowerCamelCase			=hidden_states[:, -1]
								else:
												lowerCamelCase			=hidden_states[:, additional_embeddings_len:]

								lowerCamelCase			=self.proj_to_clip_embeddings(UpperCAmelCase_							)

								if not return_dict:
												return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=UpperCAmelCase_							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=(prior_latents * self.clip_std) + self.clip_mean
								return prior_latents












import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			BioGptTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase			=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase			=dict(zip(UpperCAmelCase_				,	range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase			=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file				,	"""w"""							) as fp:
												fp.write(json.dumps(UpperCAmelCase_							)							)
								with open(self.merges_file				,	"""w"""							) as fp:
												fp.write("""\n""".join(UpperCAmelCase_							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""lower newer"""
								lowerCamelCase			="""lower newer"""
								return input_text, output_text


				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer(self.vocab_file				,	self.merges_file							)

								lowerCamelCase			="""lower"""
								lowerCamelCase			=["""low""", """er</w>"""]
								lowerCamelCase			=tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=tokens + ["""<unk>"""]
								lowerCamelCase			=[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

								lowerCamelCase			=tokenizer.encode("""sequence builders"""				,	add_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.encode("""multi-sequence build"""				,	add_special_tokens=UpperCAmelCase_							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,	UpperCAmelCase_							)

								self.assertTrue(encoded_sentence == [2] + text							)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)





