

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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin






@dataclass
class 		__magic_name__ (				snake_case							):
     UpperCamelCase_     :torch.FloatTensor






class 		__magic_name__ (				snake_case       ,	snake_case							):






     @register_to_config
     def __init__(							self						,		_lowercase = 16						,		_lowercase = 88						,		_lowercase = None						,		_lowercase = None						,		_lowercase = 1						,		_lowercase = 0.0						,		_lowercase = 32						,		_lowercase = None						,		_lowercase = False						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = "geglu"						,		_lowercase = None						,		_lowercase = False						,		_lowercase = False						,		_lowercase = False						,		_lowercase = "layer_norm"						,		_lowercase = True						,		)->			Optional[int]:
         super().__init__()
         UpperCamelCase_          =		use_linear_projection
         UpperCamelCase_          =		num_attention_heads
         UpperCamelCase_          =		attention_head_dim
         UpperCamelCase_          =		num_attention_heads * attention_head_dim

         # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration
         UpperCamelCase_          =		(in_channels is not None) and (patch_size is None)
         UpperCamelCase_          =		num_vector_embeds is not None
         UpperCamelCase_          =		in_channels is not None and patch_size is not None

         if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
             UpperCamelCase_          =		(
                 F"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                 " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                 " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                 " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                 " would be very nice if you could open a Pull request for the `transformer/config.json` file"
             )
             deprecate("norm_type!=num_embeds_ada_norm"						,		"1.0.0"						,		_lowercase						,		standard_warn=_lowercase     )
             UpperCamelCase_          =		"ada_norm"

         if self.is_input_continuous and self.is_input_vectorized:
             raise ValueError(
                 F"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                 " sure that either `in_channels` or `num_vector_embeds` is None."     )
         elif self.is_input_vectorized and self.is_input_patches:
             raise ValueError(
                 F"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                 " sure that either `num_vector_embeds` or `num_patches` is None."     )
         elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
             raise ValueError(
                 F"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                 F" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."     )

         # 2. Define input layers
         if self.is_input_continuous:
             UpperCamelCase_          =		in_channels

             UpperCamelCase_          =		torch.nn.GroupNorm(num_groups=_lowercase						,		num_channels=_lowercase						,		eps=1e-6						,		affine=_lowercase     )
             if use_linear_projection:
                 UpperCamelCase_          =		nn.Linear(_lowercase						,		_lowercase     )
             else:
                 UpperCamelCase_          =		nn.Convad(_lowercase						,		_lowercase						,		kernel_size=1						,		stride=1						,		padding=0     )
         elif self.is_input_vectorized:
             assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
             assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

             UpperCamelCase_          =		sample_size
             UpperCamelCase_          =		sample_size
             UpperCamelCase_          =		num_vector_embeds
             UpperCamelCase_          =		self.height * self.width

             UpperCamelCase_          =		ImagePositionalEmbeddings(
                 num_embed=_lowercase						,		embed_dim=_lowercase						,		height=self.height						,		width=self.width     )
         elif self.is_input_patches:
             assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

             UpperCamelCase_          =		sample_size
             UpperCamelCase_          =		sample_size

             UpperCamelCase_          =		patch_size
             UpperCamelCase_          =		PatchEmbed(
                 height=_lowercase						,		width=_lowercase						,		patch_size=_lowercase						,		in_channels=_lowercase						,		embed_dim=_lowercase						,		)

         # 3. Define transformers blocks
         UpperCamelCase_          =		nn.ModuleList(
             [
                 BasicTransformerBlock(
                     _lowercase						,		_lowercase						,		_lowercase						,		dropout=_lowercase						,		cross_attention_dim=_lowercase						,		activation_fn=_lowercase						,		num_embeds_ada_norm=_lowercase						,		attention_bias=_lowercase						,		only_cross_attention=_lowercase						,		upcast_attention=_lowercase						,		norm_type=_lowercase						,		norm_elementwise_affine=_lowercase						,		)
                 for d in range(_lowercase     )
             ]     )

         # 4. Define output layers
         UpperCamelCase_          =		in_channels if out_channels is None else out_channels
         if self.is_input_continuous:
             # TODO: should use out_channels for continuous projections
             if use_linear_projection:
                 UpperCamelCase_          =		nn.Linear(_lowercase						,		_lowercase     )
             else:
                 UpperCamelCase_          =		nn.Convad(_lowercase						,		_lowercase						,		kernel_size=1						,		stride=1						,		padding=0     )
         elif self.is_input_vectorized:
             UpperCamelCase_          =		nn.LayerNorm(_lowercase     )
             UpperCamelCase_          =		nn.Linear(_lowercase						,		self.num_vector_embeds - 1     )
         elif self.is_input_patches:
             UpperCamelCase_          =		nn.LayerNorm(_lowercase						,		elementwise_affine=_lowercase						,		eps=1e-6     )
             UpperCamelCase_          =		nn.Linear(_lowercase						,		2 * inner_dim     )
             UpperCamelCase_          =		nn.Linear(_lowercase						,		patch_size * patch_size * self.out_channels     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = True						,		)->			Optional[int]:
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
         #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
         #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
         # expects mask of shape:
         #   [batch, key_tokens]
         # adds singleton query_tokens dimension:
         #   [batch,                    1, key_tokens]
         # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
         #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
         #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
         if attention_mask is not None and attention_mask.ndim == 2:
             # assume that mask is expressed as:
             #   (1 = keep,      0 = discard)
             # convert mask into a bias that can be added to attention scores:
             #       (keep = +0,     discard = -10000.0)
             UpperCamelCase_          =		(1 - attention_mask.to(hidden_states.dtype     )) * -10_000.0
             UpperCamelCase_          =		attention_mask.unsqueeze(1     )

         # convert encoder_attention_mask to a bias the same way we do for attention_mask
         if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
             UpperCamelCase_          =		(1 - encoder_attention_mask.to(hidden_states.dtype     )) * -10_000.0
             UpperCamelCase_          =		encoder_attention_mask.unsqueeze(1     )

         # 1. Input
         if self.is_input_continuous:
             UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		hidden_states.shape
             UpperCamelCase_          =		hidden_states

             UpperCamelCase_          =		self.norm(_lowercase     )
             if not self.use_linear_projection:
                 UpperCamelCase_          =		self.proj_in(_lowercase     )
                 UpperCamelCase_          =		hidden_states.shape[1]
                 UpperCamelCase_          =		hidden_states.permute(0						,		2						,		3						,		1     ).reshape(_lowercase						,		height * width						,		_lowercase     )
             else:
                 UpperCamelCase_          =		hidden_states.shape[1]
                 UpperCamelCase_          =		hidden_states.permute(0						,		2						,		3						,		1     ).reshape(_lowercase						,		height * width						,		_lowercase     )
                 UpperCamelCase_          =		self.proj_in(_lowercase     )
         elif self.is_input_vectorized:
             UpperCamelCase_          =		self.latent_image_embedding(_lowercase     )
         elif self.is_input_patches:
             UpperCamelCase_          =		self.pos_embed(_lowercase     )

         # 2. Blocks
         for block in self.transformer_blocks:
             UpperCamelCase_          =		block(
                 _lowercase						,		attention_mask=_lowercase						,		encoder_hidden_states=_lowercase						,		encoder_attention_mask=_lowercase						,		timestep=_lowercase						,		cross_attention_kwargs=_lowercase						,		class_labels=_lowercase						,		)

         # 3. Output
         if self.is_input_continuous:
             if not self.use_linear_projection:
                 UpperCamelCase_          =		hidden_states.reshape(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     ).permute(0						,		3						,		1						,		2     ).contiguous()
                 UpperCamelCase_          =		self.proj_out(_lowercase     )
             else:
                 UpperCamelCase_          =		self.proj_out(_lowercase     )
                 UpperCamelCase_          =		hidden_states.reshape(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     ).permute(0						,		3						,		1						,		2     ).contiguous()

             UpperCamelCase_          =		hidden_states + residual
         elif self.is_input_vectorized:
             UpperCamelCase_          =		self.norm_out(_lowercase     )
             UpperCamelCase_          =		self.out(_lowercase     )
             # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
             UpperCamelCase_          =		logits.permute(0						,		2						,		1     )

             # log(p(x_0))
             UpperCamelCase_          =		F.log_softmax(logits.double()						,		dim=1     ).float()
         elif self.is_input_patches:
             # TODO: cleanup!
             UpperCamelCase_          =		self.transformer_blocks[0].norma.emb(
                 _lowercase						,		_lowercase						,		hidden_dtype=hidden_states.dtype     )
             UpperCamelCase_   ,    UpperCamelCase_          =		self.proj_out_a(F.silu(_lowercase     )     ).chunk(2						,		dim=1     )
             UpperCamelCase_          =		self.norm_out(_lowercase     ) * (1 + scale[:, None]) + shift[:, None]
             UpperCamelCase_          =		self.proj_out_a(_lowercase     )

             # unpatchify
             UpperCamelCase_          =		UpperCamelCase_          =		int(hidden_states.shape[1] ** 0.5     )
             UpperCamelCase_          =		hidden_states.reshape(
                 shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)     )
             UpperCamelCase_          =		torch.einsum("nhwpqc->nchpwq"						,		_lowercase     )
             UpperCamelCase_          =		hidden_states.reshape(
                 shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)     )

         if not return_dict:
             return (output,)

         return TransformeraDModelOutput(sample=_lowercase     )



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_weight.chunk(3       ,		dim=0       )
    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		pt_attn_layer.in_proj_bias.chunk(3       ,		dim=0       )

    UpperCamelCase_          =		pt_attn_layer.out_proj.weight
    UpperCamelCase_          =		pt_attn_layer.out_proj.bias

    UpperCamelCase_          =		q_proj
    UpperCamelCase_          =		q_proj_bias

    UpperCamelCase_          =		k_proj
    UpperCamelCase_          =		k_proj_bias

    UpperCamelCase_          =		v_proj
    UpperCamelCase_          =		v_proj_bias

    UpperCamelCase_          =		out_proj_weights
    UpperCamelCase_          =		out_proj_bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    copy_linear(hf_mlp.fca       ,		pt_mlp.c_fc       )
    copy_linear(hf_mlp.fca       ,		pt_mlp.c_proj       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_linear.weight
    UpperCamelCase_          =		pt_linear.bias




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )
    copy_linear(hf_layer.layer_norma       ,		pt_layer.ln_a       )

    # copy MLP
    copy_mlp(hf_layer.mlp       ,		pt_layer.mlp       )

    # copy attn
    copy_attn_layer(hf_layer.self_attn       ,		pt_layer.attn       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        copy_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[Any]:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.token_embedding.weight
    UpperCamelCase_          =		pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm       ,		pt_model.ln_final       )

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers       ,		pt_model.transformer.resblocks       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    UpperCamelCase_          =		pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm       ,		pt_model.visual.ln_pre       )
    copy_linear(hf_model.vision_model.post_layernorm       ,		pt_model.visual.ln_post       )

    # copy embeds
    UpperCamelCase_          =		pt_model.visual.conva.weight.data
    UpperCamelCase_          =		pt_model.visual.class_embedding
    UpperCamelCase_          =		pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers       ,		pt_model.visual.transformer.resblocks       )




@torch.no_grad()
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_          =		CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
    else:
        UpperCamelCase_          =		CLIPConfig(projection_dim=5_1_2       ,		text_config={}       ,		vision_config={}       )

    UpperCamelCase_          =		CLIPModel(SCREAMING_SNAKE_CASE_       ).eval()

    UpperCamelCase_   ,    UpperCamelCase_          =		load(SCREAMING_SNAKE_CASE_       ,		device="cpu"       ,		jit=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.eval()

    copy_text_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    copy_vison_model_and_projection(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		pt_model.logit_scale

    UpperCamelCase_          =		torch.arange(0       ,		7_7       ).unsqueeze(0       )
    UpperCamelCase_          =		torch.randn(1       ,		3       ,		2_2_4       ,		2_2_4       )

    UpperCamelCase_          =		hf_model(input_ids=SCREAMING_SNAKE_CASE_       ,		pixel_values=SCREAMING_SNAKE_CASE_       ,		return_dict=SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		hf_outputs.logits_per_image
    UpperCamelCase_          =		hf_outputs.logits_per_text
    UpperCamelCase_   ,    UpperCamelCase_          =		pt_model(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )
    assert torch.allclose(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		atol=1E-3       )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Any      =			argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 SCREAMING_SNAKE_CASE						:Dict      =			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
