from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase  							=						{
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''BridgeTowerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BridgeTowerForContrastiveLearning''',
          '''BridgeTowerForImageAndTextRetrieval''',
          '''BridgeTowerForMaskedLM''',
          '''BridgeTowerModel''',
          '''BridgeTowerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_bridgetower import (
          BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BridgeTowerConfig,
          BridgeTowerTextConfig,
          BridgeTowerVisionConfig,
      )
      from .processing_bridgetower import BridgeTowerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_bridgetower import BridgeTowerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bridgetower import (
                BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
                BridgeTowerForContrastiveLearning,
                BridgeTowerForImageAndTextRetrieval,
                BridgeTowerForMaskedLM,
                BridgeTowerModel,
                BridgeTowerPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)








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


