



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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: torch.FloatTensor






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @register_to_config
    def __init__(       self,		lowerCamelCase__ = 32,		lowerCamelCase__ = 64,		lowerCamelCase__ = 20,		lowerCamelCase__ = 768,		lowerCamelCase__=77,		lowerCamelCase__=4,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = "silu",		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "linear",		lowerCamelCase__ = "prd",		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       int          =   num_attention_heads
        A :       Tuple          =   attention_head_dim
        A :       Optional[int]          =   num_attention_heads * attention_head_dim
        A :       List[str]          =   additional_embeddings

        A :       int          =   time_embed_dim or inner_dim
        A :       Tuple          =   embedding_proj_dim or embedding_dim
        A :       Dict          =   clip_embed_dim or embedding_dim

        A :       List[str]          =   Timesteps(lowerCamelCase__,		lowerCamelCase__,		0		)
        A :       Any          =   TimestepEmbedding(lowerCamelCase__,		lowerCamelCase__,		out_dim=lowerCamelCase__,		act_fn=lowerCamelCase__		)

        A :       List[Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)

        if embedding_proj_norm_type is None:
            A :       Union[str, Any]          =   None
        elif embedding_proj_norm_type == "layer":
            A :       str          =   nn.LayerNorm(lowerCamelCase__		)
        else:
            raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''		)

        A :       Optional[Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)

        if encoder_hid_proj_type is None:
            A :       Dict          =   None
        elif encoder_hid_proj_type == "linear":
            A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)
        else:
            raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''		)

        A :       List[str]          =   nn.Parameter(torch.zeros(1,		num_embeddings + additional_embeddings,		lowerCamelCase__		)		)

        if added_emb_type == "prd":
            A :       Union[str, Any]          =   nn.Parameter(torch.zeros(1,		1,		lowerCamelCase__		)		)
        elif added_emb_type is None:
            A :       int          =   None
        else:
            raise ValueError(
                f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''		)

        A :       int          =   nn.ModuleList(
            [
                BasicTransformerBlock(
                    lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		dropout=lowerCamelCase__,		activation_fn="""gelu""",		attention_bias=lowerCamelCase__,		)
                for d in range(lowerCamelCase__		)
            ]		)

        if norm_in_type == "layer":
            A :       int          =   nn.LayerNorm(lowerCamelCase__		)
        elif norm_in_type is None:
            A :       Tuple          =   None
        else:
            raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''		)

        A :       Optional[Any]          =   nn.LayerNorm(lowerCamelCase__		)

        A :       str          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)

        A :       Union[str, Any]          =   torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings],		-1_0000.0		)
        causal_attention_mask.triu_(1		)
        A :       Optional[Any]          =   causal_attention_mask[None, ...]
        self.register_buffer("""causal_attention_mask""",		lowerCamelCase__,		persistent=lowerCamelCase__		)

        A :       Dict          =   nn.Parameter(torch.zeros(1,		lowerCamelCase__		)		)
        A :       Tuple          =   nn.Parameter(torch.zeros(1,		lowerCamelCase__		)		)






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   {}

        def fn_recursive_add_processors(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                A :       Tuple          =   module.processor

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

            return processors

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

        return processors






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[Any]          =   len(self.attn_processors.keys()		)

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

        def fn_recursive_attn_processor(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
            if hasattr(lowerCamelCase__,		"""set_processor"""		):
                if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                    module.set_processor(lowerCamelCase__		)
                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}''',		lowerCamelCase__,		lowerCamelCase__		)

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






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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		):
        A :       Optional[int]          =   hidden_states.shape[0]

        A :       Tuple          =   timestep
        if not torch.is_tensor(lowerCamelCase__		):
            A :       List[Any]          =   torch.tensor([timesteps],		dtype=torch.long,		device=hidden_states.device		)
        elif torch.is_tensor(lowerCamelCase__		) and len(timesteps.shape		) == 0:
            A :       Optional[int]          =   timesteps[None].to(hidden_states.device		)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        A :       Optional[Any]          =   timesteps * torch.ones(lowerCamelCase__,		dtype=timesteps.dtype,		device=timesteps.device		)

        A :       str          =   self.time_proj(lowerCamelCase__		)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        A :       Union[str, Any]          =   timesteps_projected.to(dtype=self.dtype		)
        A :       Tuple          =   self.time_embedding(lowerCamelCase__		)

        if self.embedding_proj_norm is not None:
            A :       Optional[Any]          =   self.embedding_proj_norm(lowerCamelCase__		)

        A :       Any          =   self.embedding_proj(lowerCamelCase__		)
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
            A :       List[str]          =   self.encoder_hidden_states_proj(lowerCamelCase__		)
        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"""		)

        A :       Tuple          =   self.proj_in(lowerCamelCase__		)

        A :       Dict          =   self.positional_embedding.to(hidden_states.dtype		)

        A :       List[str]          =   []
        A :       Optional[Any]          =   0

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

        if len(proj_embeddings.shape		) == 2:
            A :       Optional[Any]          =   proj_embeddings[:, None, :]

        if len(hidden_states.shape		) == 2:
            A :       List[str]          =   hidden_states[:, None, :]

        A :       Dict          =   additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
            A :       List[Any]          =   self.prd_embedding.to(hidden_states.dtype		).expand(lowerCamelCase__,		-1,		-1		)
            additional_embeds.append(lowerCamelCase__		)

        A :       Union[str, Any]          =   torch.cat(
            lowerCamelCase__,		dim=1,		)

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

        A :       Union[str, Any]          =   hidden_states + positional_embeddings

        if attention_mask is not None:
            A :       List[Any]          =   (1 - attention_mask.to(hidden_states.dtype		)) * -1_0000.0
            A :       Tuple          =   F.pad(lowerCamelCase__,		(0, self.additional_embeddings),		value=0.0		)
            A :       List[Any]          =   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
            A :       Any          =   attention_mask.repeat_interleave(self.config.num_attention_heads,		dim=0		)

        if self.norm_in is not None:
            A :       str          =   self.norm_in(lowerCamelCase__		)

        for block in self.transformer_blocks:
            A :       Optional[Any]          =   block(lowerCamelCase__,		attention_mask=lowerCamelCase__		)

        A :       Optional[int]          =   self.norm_out(lowerCamelCase__		)

        if self.prd_embedding is not None:
            A :       Dict          =   hidden_states[:, -1]
        else:
            A :       Optional[int]          =   hidden_states[:, additional_embeddings_len:]

        A :       Optional[Any]          =   self.proj_to_clip_embeddings(lowerCamelCase__		)

        if not return_dict:
            return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       str          =   (prior_latents * self.clip_std) + self.clip_mean
        return prior_latents







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam






class SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        with tf.name_scope(self.name or """WarmUp"""		) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   self.initial_learning_rate * tf.math.pow(lowerCamelCase__,		self.power		)
            return tf.cond(
                global_step_float < warmup_steps_float,		lambda: warmup_learning_rate,		lambda: self.decay_schedule_fn(step - self.warmup_steps		),		name=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # We return the optimizer and the LR scheduler in order to better track the
    # evolution of the LR independently of the optimizer.
    return optimizer, lr_schedule






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   self._do_use_weight_decay(var.name		)
        if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""],		use_locking=self._use_locking,		)
        return tf.no_op()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if self.weight_decay_rate == 0:
            return False

        if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return True

        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return False
        return True






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   tf.Variable(
                tf.constant(0,		dtype=tf.intaa		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)

        return self._accum_steps.value()






    @property
    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






    def __call__(       self,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(lowerCamelCase__		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]		)
        if len(lowerCamelCase__		) != len(self._gradients		):
            raise ValueError(f'''Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase__		)}'''		)

        for accum_gradient, gradient in zip(self._gradients,		lowerCamelCase__		):
            if accum_gradient is not None and gradient is not None:
                accum_gradient.assign_add(lowerCamelCase__		)

        self._accum_steps.assign_add(1		)






    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            return
        self._accum_steps.assign(0		)
        for gradient in self._gradients:
            if gradient is not None:
                gradient.assign(tf.zeros_like(lowerCamelCase__		)		)


