"""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     (    a	):







 """simple docstring"""


 a_       :	torch.FloatTensor

class SCREAMING_SNAKE_CASE     (    a   ,      a	):







 """simple docstring"""


 @register_to_config
 def __init__(   self		:      Dict					,   _snake_case		:      int = 16					,   _snake_case		:      int = 88					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      int = 1					,   _snake_case		:      float = 0.0					,   _snake_case		:      int = 32					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      bool = False					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      str = "geglu"					,   _snake_case		:      Optional[int] = None					,   _snake_case		:      bool = False					,   _snake_case		:      bool = False					,   _snake_case		:      bool = False					,   _snake_case		:      str = "layer_norm"					,   _snake_case		:      bool = True					,   )				->			Tuple:




    '''simple docstring'''






    super().__init__()
    a__     				=		use_linear_projection
    a__     				=		num_attention_heads
    a__     				=		attention_head_dim
    a__     				=		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
    a__     				=		(in_channels is not None) and (patch_size is None)
    a__     				=		num_vector_embeds is not None
    a__     				=		in_channels is not None and patch_size is not None

    if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
       a__     				=		(
           F'''The configuration file of this model: {self.__class__} is outdated.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'					,   _snake_case					,   standard_warn=_snake_case				)
       a__     				=		'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:
       a__     				=		in_channels

       a__     				=		torch.nn.GroupNorm(num_groups=_snake_case					,   num_channels=_snake_case					,   eps=1E-6					,   affine=_snake_case				)
       if use_linear_projection:
          a__     				=		nn.Linear(_snake_case					,   _snake_case				)
       else:
          a__     				=		nn.Convad(_snake_case					,   _snake_case					,   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"

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

       a__     				=		ImagePositionalEmbeddings(
           num_embed=_snake_case					,   embed_dim=_snake_case					,   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"

       a__     				=		sample_size
       a__     				=		sample_size

       a__     				=		patch_size
       a__     				=		PatchEmbed(
           height=_snake_case					,   width=_snake_case					,   patch_size=_snake_case					,   in_channels=_snake_case					,   embed_dim=_snake_case					,   )

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

    # 4. Define output layers
    a__     				=		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:
          a__     				=		nn.Linear(_snake_case					,   _snake_case				)
       else:
          a__     				=		nn.Convad(_snake_case					,   _snake_case					,   kernel_size=1					,   stride=1					,   padding=0				)
    elif self.is_input_vectorized:
       a__     				=		nn.LayerNorm(_snake_case				)
       a__     				=		nn.Linear(_snake_case					,   self.num_vector_embeds - 1				)
    elif self.is_input_patches:
       a__     				=		nn.LayerNorm(_snake_case					,   elementwise_affine=_snake_case					,   eps=1E-6				)
       a__     				=		nn.Linear(_snake_case					,   2 * inner_dim				)
       a__     				=		nn.Linear(_snake_case					,   patch_size * patch_size * self.out_channels				)



 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      torch.Tensor					,   _snake_case		:      Optional[torch.Tensor] = None					,   _snake_case		:      Optional[torch.LongTensor] = None					,   _snake_case		:      Optional[torch.LongTensor] = None					,   _snake_case		:      Dict[str, Any] = None					,   _snake_case		:      Optional[torch.Tensor] = None					,   _snake_case		:      Optional[torch.Tensor] = None					,   _snake_case		:      bool = True					,   )				->			Any:




    '''simple docstring'''






    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)
       a__     				=		(1 - attention_mask.to(hidden_states.dtype				)) * -1_0000.0
       a__     				=		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:
       a__     				=		(1 - encoder_attention_mask.to(hidden_states.dtype				)) * -1_0000.0
       a__     				=		encoder_attention_mask.unsqueeze(1				)

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

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

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

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

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

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

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



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


