"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase		:		List[str]														=							logging.get_logger(__name__)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=False):
     __snake_case				=							[]
     for i in range(config.num_hidden_layers):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
          rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
          rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight"))
          rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
          rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
          rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
          rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
          rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
          rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
          rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''cls_token''', '''vit.embeddings.cls_token'''),
             ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
         ])

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
                  ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                  ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
              ])

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          __snake_case				=							[(pair[0], pair[1][4:]) if pair[1].startswith('''vit''') else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''norm.weight''', '''vit.layernorm.weight'''),
                  ('''norm.bias''', '''vit.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ])

     return rename_keys





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case=False):




     for i in range(config.num_hidden_layers):
          if base_model:
               __snake_case				=							''''''
          else:
               __snake_case				=							'''vit.'''
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __snake_case				=							state_dict.pop(f"blocks.{i}.attn.qkv.weight")
          __snake_case				=							state_dict.pop(f"blocks.{i}.attn.qkv.bias")
          # next, add query, keys and values (in that order) to the state dict
          __snake_case				=							in_proj_weight[
              : config.hidden_size, :
          ]
          __snake_case				=							in_proj_bias[: config.hidden_size]
          __snake_case				=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __snake_case				=							in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __snake_case				=							in_proj_weight[
              -config.hidden_size :, :
          ]
          __snake_case				=							in_proj_bias[-config.hidden_size :]





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(__A,      __A)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							dct.pop(__A)
     __snake_case				=							val





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case				=							Image.open(requests.get(__A,      stream=__A).raw)
     return im





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							ViTConfig()
     __snake_case				=							False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __snake_case				=							True
          __snake_case				=							int(vit_name[-12:-10])
          __snake_case				=							int(vit_name[-9:-6])
     else:
          __snake_case				=							10_00
          __snake_case				=							'''huggingface/label-files'''
          __snake_case				=							'''imagenet-1k-id2label.json'''
          __snake_case				=							json.load(open(hf_hub_download(__A,      __A,      repo_type='''dataset'''),      '''r'''))
          __snake_case				=							{int(__A): v for k, v in idalabel.items()}
          __snake_case				=							idalabel
          __snake_case				=							{v: k for k, v in idalabel.items()}
          __snake_case				=							int(vit_name[-6:-4])
          __snake_case				=							int(vit_name[-3:])
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('''tiny'''):
               __snake_case				=							1_92
               __snake_case				=							7_68
               __snake_case				=							12
               __snake_case				=							3
          elif vit_name[9:].startswith('''small'''):
               __snake_case				=							3_84
               __snake_case				=							15_36
               __snake_case				=							12
               __snake_case				=							6
          else:
               pass
     else:
          if vit_name[4:].startswith('''small'''):
               __snake_case				=							7_68
               __snake_case				=							23_04
               __snake_case				=							8
               __snake_case				=							8
          elif vit_name[4:].startswith('''base'''):
               pass
          elif vit_name[4:].startswith('''large'''):
               __snake_case				=							10_24
               __snake_case				=							40_96
               __snake_case				=							24
               __snake_case				=							16
          elif vit_name[4:].startswith('''huge'''):
               __snake_case				=							12_80
               __snake_case				=							51_20
               __snake_case				=							32
               __snake_case				=							16

                # load original model from timm
     __snake_case				=							timm.create_model(__A,      pretrained=__A)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     __snake_case				=							timm_model.state_dict()
     if base_model:
          remove_classification_head_(__A)
     __snake_case				=							create_rename_keys(__A,      __A)
     for src, dest in rename_keys:
          rename_key(__A,      __A,      __A)
     read_in_q_k_v(__A,      __A,      __A)

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          __snake_case				=							ViTModel(__A).eval()
     else:
          __snake_case				=							ViTForImageClassification(__A).eval()
     model.load_state_dict(__A)

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          __snake_case				=							DeiTImageProcessor(size=config.image_size)
     else:
          __snake_case				=							ViTImageProcessor(size=config.image_size)
     __snake_case				=							image_processor(images=prepare_img(),      return_tensors='''pt''')
     __snake_case				=							encoding['''pixel_values''']
     __snake_case				=							model(__A)

     if base_model:
          __snake_case				=							timm_model.forward_features(__A)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__A,      outputs.pooler_output,      atol=1E-3)
     else:
          __snake_case				=							timm_model(__A)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(__A,      outputs.logits,      atol=1E-3)

     Path(__A).mkdir(exist_ok=__A)
     print(f"Saving model {vit_name} to {pytorch_dump_folder_path}")
     model.save_pretrained(__A)
     print(f"Saving image processor to {pytorch_dump_folder_path}")
     image_processor.save_pretrained(__A)


if __name__ == "__main__":
       __lowercase		:		Dict														=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--vit_name",
           default="vit_base_patch16_224",
           type=str,
           help="Name of the ViT timm model you\'d like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __lowercase		:		Tuple														=							parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__lowercase		:		int														=							logging.get_logger(__name__)


__lowercase		:		Tuple														=							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowercase		:		List[Any]														=							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowercase		:		List[str]														=							{"facebook/blenderbot-3B": 128}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					int							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Tuple							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					int							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					str							=					BlenderbotTokenizer
    def __init__(		self							:     Union[str, Any]	,      A_							:     Any=None	,      A_							:     Optional[int]=None	,      A_							:     Optional[Any]=None	,      A_							:     Union[str, Any]="replace"	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     List[Any]="<s>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     Any="<pad>"	,      A_							:     List[str]="<mask>"	,      A_							:     int=False	,      A_							:     Tuple=True	,      **A_							:     Optional[Any]	,      )    ->       int:
         super().__init__(
             A_	,      A_	,      tokenizer_file=A_	,      errors=A_	,      bos_token=A_	,      eos_token=A_	,      sep_token=A_	,      cls_token=A_	,      unk_token=A_	,      pad_token=A_	,      mask_token=A_	,      add_prefix_space=A_	,      trim_offsets=A_	,      **A_	,      )

         __snake_case				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
              __snake_case				=							getattr(A_	,      pre_tok_state.pop('''type'''    )    )
              __snake_case				=							add_prefix_space
              __snake_case				=							pre_tok_class(**A_    )

         __snake_case				=							add_prefix_space

         __snake_case				=							'''post_processor'''
         __snake_case				=							getattr(self.backend_tokenizer	,      A_	,      A_    )
         if tokenizer_component_instance:
              __snake_case				=							json.loads(tokenizer_component_instance.__getstate__()    )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   __snake_case				=							tuple(state['''sep''']    )
              if "cls" in state:
                   __snake_case				=							tuple(state['''cls''']    )

              __snake_case				=							False

              if state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
                   __snake_case				=							add_prefix_space
                   __snake_case				=							True

              if state.get('''trim_offsets'''	,      A_    ) != trim_offsets:
                   __snake_case				=							trim_offsets
                   __snake_case				=							True

              if changes_to_apply:
                   __snake_case				=							getattr(A_	,      state.pop('''type'''    )    )
                   __snake_case				=							component_class(**A_    )
                   setattr(self.backend_tokenizer	,      A_	,      A_    )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''    )
              return None
         return str(self._mask_token    )
    @mask_token.setter
    def  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else value
         __snake_case				=							value
    def  lowercase					(		self							:     Any	,      *A_							:     List[str]	,      **A_							:     Optional[int]    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      *A_							:     Tuple	,      **A_							:     str    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							self._tokenizer.model.save(A_	,      name=A_    )
         return tuple(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep    ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       Any:
         return token_ids_a + [self.eos_token_id]







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     "Conversation"    )    ->       List[int]:
         __snake_case				=							[]
         for is_user, text in conversation.iter_texts():
              if is_user:
                   # We need to space prefix as it's being done within blenderbot
                   inputs.append(''' ''' + text    )
              else:
                   # Generated responses should contain them already.
                   inputs.append(A_    )

         __snake_case				=							'''  '''.join(A_    )
         __snake_case				=							self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __snake_case				=							input_ids[-self.model_max_length :]
              logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."    )
         return input_ids