





"""simple docstring"""


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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:torch.FloatTensor





class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase      ):







  '''simple docstring'''


  @register_to_config
  def __init__(						self      :				List[str]    ,      __a      :				int = 16    ,      __a      :				int = 88    ,      __a      :				Optional[int] = None    ,      __a      :				Optional[int] = None    ,      __a      :				int = 1    ,      __a      :				float = 0.0    ,      __a      :				int = 32    ,      __a      :				Optional[int] = None    ,      __a      :				bool = False    ,      __a      :				Optional[int] = None    ,      __a      :				Optional[int] = None    ,      __a      :				Optional[int] = None    ,      __a      :				str = "geglu"    ,      __a      :				Optional[int] = None    ,      __a      :				bool = False    ,      __a      :				bool = False    ,      __a      :				bool = False    ,      __a      :				str = "layer_norm"    ,      __a      :				bool = True    ,      )						-> int:
        super().__init__()
        _UpperCamelCase					:		Dict  =				use_linear_projection
        _UpperCamelCase					:		Optional[Any]  =				num_attention_heads
        _UpperCamelCase					:		Optional[int]  =				attention_head_dim
        _UpperCamelCase					:		List[str]  =				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					:		Tuple  =				(in_channels is not None) and (patch_size is None)
        _UpperCamelCase					:		Union[str, Any]  =				num_vector_embeds is not None
        _UpperCamelCase					:		Any  =				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					:		Any  =				(
                  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"    ,      __a    ,      standard_warn=__a		)
              _UpperCamelCase					:		Union[str, Any]  =				"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					:		Union[str, Any]  =				in_channels

              _UpperCamelCase					:		Union[str, Any]  =				torch.nn.GroupNorm(num_groups=__a    ,      num_channels=__a    ,      eps=1e-6    ,      affine=__a		)
              if use_linear_projection:
                    _UpperCamelCase					:		List[str]  =				nn.Linear(__a    ,      __a		)
              else:
                    _UpperCamelCase					:		Any  =				nn.Convad(__a    ,      __a    ,      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					:		List[Any]  =				sample_size
              _UpperCamelCase					:		List[Any]  =				sample_size
              _UpperCamelCase					:		Dict  =				num_vector_embeds
              _UpperCamelCase					:		Dict  =				self.height * self.width

              _UpperCamelCase					:		Optional[int]  =				ImagePositionalEmbeddings(
                  num_embed=__a    ,      embed_dim=__a    ,      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					:		Tuple  =				sample_size
              _UpperCamelCase					:		Dict  =				sample_size

              _UpperCamelCase					:		Optional[Any]  =				patch_size
              _UpperCamelCase					:		Dict  =				PatchEmbed(
                  height=__a    ,      width=__a    ,      patch_size=__a    ,      in_channels=__a    ,      embed_dim=__a    ,      )

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

        # 4. Define output layers
        _UpperCamelCase					:		int  =				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					:		Any  =				nn.Linear(__a    ,      __a		)
              else:
                    _UpperCamelCase					:		Dict  =				nn.Convad(__a    ,      __a    ,      kernel_size=1    ,      stride=1    ,      padding=0		)
        elif self.is_input_vectorized:
              _UpperCamelCase					:		str  =				nn.LayerNorm(__a		)
              _UpperCamelCase					:		Union[str, Any]  =				nn.Linear(__a    ,      self.num_vector_embeds - 1		)
        elif self.is_input_patches:
              _UpperCamelCase					:		Optional[int]  =				nn.LayerNorm(__a    ,      elementwise_affine=__a    ,      eps=1e-6		)
              _UpperCamelCase					:		List[Any]  =				nn.Linear(__a    ,      2 * inner_dim		)
              _UpperCamelCase					:		Optional[int]  =				nn.Linear(__a    ,      patch_size * patch_size * self.out_channels		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				torch.Tensor    ,      __a      :				Optional[torch.Tensor] = None    ,      __a      :				Optional[torch.LongTensor] = None    ,      __a      :				Optional[torch.LongTensor] = None    ,      __a      :				Dict[str, Any] = None    ,      __a      :				Optional[torch.Tensor] = None    ,      __a      :				Optional[torch.Tensor] = None    ,      __a      :				bool = True    ,      )						-> str:
        # 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					:		str  =				(1 - attention_mask.to(hidden_states.dtype		)) * -1_00_00.0
              _UpperCamelCase					:		int  =				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					:		Optional[Any]  =				(1 - encoder_attention_mask.to(hidden_states.dtype		)) * -1_00_00.0
              _UpperCamelCase					:		Optional[int]  =				encoder_attention_mask.unsqueeze(1		)

        # 1. Input
        if self.is_input_continuous:
              _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				hidden_states.shape
              _UpperCamelCase					:		Union[str, Any]  =				hidden_states

              _UpperCamelCase					:		str  =				self.norm(__a		)
              if not self.use_linear_projection:
                    _UpperCamelCase					:		Optional[int]  =				self.proj_in(__a		)
                    _UpperCamelCase					:		str  =				hidden_states.shape[1]
                    _UpperCamelCase					:		Optional[Any]  =				hidden_states.permute(0    ,      2    ,      3    ,      1		).reshape(__a    ,      height * width    ,      __a		)
              else:
                    _UpperCamelCase					:		Dict  =				hidden_states.shape[1]
                    _UpperCamelCase					:		int  =				hidden_states.permute(0    ,      2    ,      3    ,      1		).reshape(__a    ,      height * width    ,      __a		)
                    _UpperCamelCase					:		int  =				self.proj_in(__a		)
        elif self.is_input_vectorized:
              _UpperCamelCase					:		Tuple  =				self.latent_image_embedding(__a		)
        elif self.is_input_patches:
              _UpperCamelCase					:		Any  =				self.pos_embed(__a		)

        # 2. Blocks
        for block in self.transformer_blocks:
              _UpperCamelCase					:		List[Any]  =				block(
                  __a    ,      attention_mask=__a    ,      encoder_hidden_states=__a    ,      encoder_attention_mask=__a    ,      timestep=__a    ,      cross_attention_kwargs=__a    ,      class_labels=__a    ,      )

        # 3. Output
        if self.is_input_continuous:
              if not self.use_linear_projection:
                    _UpperCamelCase					:		Any  =				hidden_states.reshape(__a    ,      __a    ,      __a    ,      __a		).permute(0    ,      3    ,      1    ,      2		).contiguous()
                    _UpperCamelCase					:		Tuple  =				self.proj_out(__a		)
              else:
                    _UpperCamelCase					:		int  =				self.proj_out(__a		)
                    _UpperCamelCase					:		Dict  =				hidden_states.reshape(__a    ,      __a    ,      __a    ,      __a		).permute(0    ,      3    ,      1    ,      2		).contiguous()

              _UpperCamelCase					:		Tuple  =				hidden_states + residual
        elif self.is_input_vectorized:
              _UpperCamelCase					:		Optional[int]  =				self.norm_out(__a		)
              _UpperCamelCase					:		Optional[int]  =				self.out(__a		)
              # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
              _UpperCamelCase					:		Optional[Any]  =				logits.permute(0    ,      2    ,      1		)

              # log(p(x_0))
              _UpperCamelCase					:		Optional[int]  =				F.log_softmax(logits.double()    ,      dim=1		).float()
        elif self.is_input_patches:
              # TODO: cleanup!
              _UpperCamelCase					:		List[str]  =				self.transformer_blocks[0].norma.emb(
                  __a    ,      __a    ,      hidden_dtype=hidden_states.dtype		)
              _UpperCamelCase,     _UpperCamelCase					:		Optional[int]  =				self.proj_out_a(F.silu(__a		)		).chunk(2    ,      dim=1		)
              _UpperCamelCase					:		Tuple  =				self.norm_out(__a		) * (1 + scale[:, None]) + shift[:, None]
              _UpperCamelCase					:		Union[str, Any]  =				self.proj_out_a(__a		)

              # unpatchify
              _UpperCamelCase					:		str  =				int(hidden_states.shape[1] ** 0.5		)
              _UpperCamelCase					:		Any  =				hidden_states.reshape(
                  shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)		)
              _UpperCamelCase					:		List[Any]  =				torch.einsum("nhwpqc->nchpwq"    ,      __a		)
              _UpperCamelCase					:		int  =				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=__a		)







"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
