



"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
   import tensorflow as tf

if is_torch_available():
   import torch


_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	:



  def __init__(						self :					Any       ,		lowerCAmelCase :					str = None       ,		lowerCAmelCase :					uuid.UUID = None       ,		lowerCAmelCase :					List[str]=None       ,		lowerCAmelCase :					Any=None		)     ->					Optional[Any]:


     '''simple docstring'''




     if not conversation_id:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=uuid.uuida()
     if past_user_inputs is None:
        SCREAMING_SNAKE_CASE_: Tuple     			=[]
     if generated_responses is None:
        SCREAMING_SNAKE_CASE_: Any     			=[]

     SCREAMING_SNAKE_CASE_: uuid.UUID     			=conversation_id
     SCREAMING_SNAKE_CASE_: List[str]     			=past_user_inputs
     SCREAMING_SNAKE_CASE_: List[str]     			=generated_responses
     SCREAMING_SNAKE_CASE_: Optional[str]     			=text



  def __eq__(						self :					Optional[int]       ,		lowerCAmelCase :					List[str]		)     ->					Union[str, Any]:


     '''simple docstring'''




     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        return False
     if self.uuid == other.uuid:
        return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					bool = False		)     ->					Union[str, Any]:


     '''simple docstring'''




     if self.new_user_input:
        if overwrite:
           logger.warning(
               f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
               f'''with: "{text}".'''		)
           SCREAMING_SNAKE_CASE_: Optional[int]     			=text
        else:
           logger.warning(
               f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
               f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''		)
     else:
        SCREAMING_SNAKE_CASE_: int     			=text



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Any:


     '''simple docstring'''




     if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=None



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					str		)     ->					Any:


     '''simple docstring'''




     self.generated_responses.append(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[int]:


     '''simple docstring'''




     for user_input, generated_response in zip(self.past_user_inputs       ,		self.generated_responses		):
        yield True, user_input
        yield False, generated_response
     if self.new_user_input:
        yield True, self.new_user_input





  def __repr__(						self :					Optional[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=f'''Conversation id: {self.uuid} \n'''
     for is_user, text in self.iter_texts():
        SCREAMING_SNAKE_CASE_: List[Any]     			="""user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
     return output







@add_end_docstrings(
    UpperCAmelCase__	,      R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,      )
class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Tuple       ,		*lowerCAmelCase :					str       ,		**lowerCAmelCase :					str		)     ->					Any:


     '''simple docstring'''




     super().__init__(*lowerCAmelCase       ,		**lowerCAmelCase		)
     if self.tokenizer.pad_token_id is None:
        SCREAMING_SNAKE_CASE_: Tuple     			=self.tokenizer.eos_token



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]=None       ,		lowerCAmelCase :					List[Any]=None       ,		lowerCAmelCase :					Any=None       ,		**lowerCAmelCase :					Dict		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			={}
     SCREAMING_SNAKE_CASE_: str     			={}
     SCREAMING_SNAKE_CASE_: str     			={}

     if min_length_for_response is not None:
        SCREAMING_SNAKE_CASE_: List[Any]     			=min_length_for_response
     if minimum_tokens is not None:
        SCREAMING_SNAKE_CASE_: str     			=minimum_tokens

     if "max_length" in generate_kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
        SCREAMING_SNAKE_CASE_: Dict     			=clean_up_tokenization_spaces

     if generate_kwargs:
        forward_params.update(lowerCAmelCase		)
     return preprocess_params, forward_params, postprocess_params



  def __call__(						self :					Optional[int]       ,		lowerCAmelCase :					Union[Conversation, List[Conversation]]       ,		lowerCAmelCase :					Any=0       ,		**lowerCAmelCase :					Optional[int]		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=super().__call__(lowerCAmelCase       ,		num_workers=lowerCAmelCase       ,		**lowerCAmelCase		)
     if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) and len(lowerCAmelCase		) == 1:
        return outputs[0]
     return outputs



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Conversation       ,		lowerCAmelCase :					List[str]=32		)     ->					Dict[str, Any]:


     '''simple docstring'''




     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""		)
     if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid		)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""		)
     if hasattr(self.tokenizer       ,		"""_build_conversation_input_ids"""		):
        SCREAMING_SNAKE_CASE_: List[Any]     			=self.tokenizer._build_conversation_input_ids(lowerCAmelCase		)
     else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        SCREAMING_SNAKE_CASE_: List[Any]     			=self._legacy_parse_and_tokenize(lowerCAmelCase		)

     if self.framework == "pt":
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.LongTensor([input_ids]		)
     elif self.framework == "tf":
        SCREAMING_SNAKE_CASE_: str     			=tf.constant([input_ids]		)
     return {"input_ids": input_ids, "conversation": conversation}



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					str=10       ,		**lowerCAmelCase :					int		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=generate_kwargs.get("""max_length"""       ,		self.model.config.max_length		)

     SCREAMING_SNAKE_CASE_: str     			=model_inputs["""input_ids"""].shape[1]
     if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''		)
        SCREAMING_SNAKE_CASE_: Tuple     			=max_length - minimum_tokens
        SCREAMING_SNAKE_CASE_: int     			=model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=model_inputs["""attention_mask"""][:, -trim:]
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model_inputs.pop("""conversation"""		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=max_length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model.generate(**lowerCAmelCase       ,		**lowerCAmelCase		)
     if self.model.config.is_encoder_decoder:
        SCREAMING_SNAKE_CASE_: List[str]     			=1
     else:
        SCREAMING_SNAKE_CASE_: Tuple     			=n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Union[str, Any]=True		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=model_outputs["""output_ids"""]
     SCREAMING_SNAKE_CASE_: List[str]     			=self.tokenizer.decode(
         output_ids[0]       ,		skip_special_tokens=lowerCAmelCase       ,		clean_up_tokenization_spaces=lowerCAmelCase       ,		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=model_outputs["""conversation"""]
     conversation.mark_processed()
     conversation.append_response(lowerCAmelCase		)
     return conversation





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Conversation		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.tokenizer.eos_token_id
     SCREAMING_SNAKE_CASE_: List[Any]     			=[]
     for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
           input_ids.extend(self.tokenizer.encode(lowerCAmelCase       ,		add_special_tokens=lowerCAmelCase		) + [eos_token_id]		)
        else:
           input_ids.extend(self.tokenizer.encode(lowerCAmelCase       ,		add_special_tokens=lowerCAmelCase		)		)

     if len(lowerCAmelCase		) > self.tokenizer.model_max_length:
        SCREAMING_SNAKE_CASE_: List[Any]     			=input_ids[-self.tokenizer.model_max_length :]
     return input_ids








"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      __magic_name__				(							lowercase ):
   if "cls_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""cls_token"""							,			"""vit.embeddings.cls_token""" )
   if "mask_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""mask_token"""							,			"""decoder.mask_token""" )
   if "decoder_pos_embed" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""decoder_pos_embed"""							,			"""decoder.decoder_pos_embed""" )
   if "pos_embed" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""pos_embed"""							,			"""vit.embeddings.position_embeddings""" )
   if "patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""patch_embed.proj"""							,			"""vit.embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""patch_embed.norm"""							,			"""vit.embeddings.norm""" )
   if "decoder_blocks" in name:
      SCREAMING_SNAKE_CASE_: List[Any]     			=name.replace("""decoder_blocks"""							,			"""decoder.decoder_layers""" )
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""blocks"""							,			"""vit.encoder.layer""" )
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: Dict     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )
   if "decoder_embed" in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""decoder_embed"""							,			"""decoder.decoder_embed""" )
   if "decoder_norm" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""decoder_norm"""							,			"""decoder.decoder_norm""" )
   if "decoder_pred" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""decoder_pred"""							,			"""decoder.decoder_pred""" )
   if "norm.weight" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.weight"""							,			"""vit.layernorm.weight""" )
   if "norm.bias" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.bias"""							,			"""vit.layernorm.bias""" )

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Optional[int]     			=orig_state_dict.pop(lowercase )

      if "qkv" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.split(""".""" )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=int(key_split[1] )
         if "decoder_blocks" in key:
            SCREAMING_SNAKE_CASE_: int     			=config.decoder_hidden_size
            SCREAMING_SNAKE_CASE_: Optional[int]     			="""decoder.decoder_layers."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Dict     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: str     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[-dim:]
         else:
            SCREAMING_SNAKE_CASE_: Any     			=config.hidden_size
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""vit.encoder.layer."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: Dict     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Any     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[-dim:]

      else:
         SCREAMING_SNAKE_CASE_: Tuple     			=val

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=ViTMAEConfig()
   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: List[Any]     			=1024
      SCREAMING_SNAKE_CASE_: Dict     			=4096
      SCREAMING_SNAKE_CASE_: Tuple     			=24
      SCREAMING_SNAKE_CASE_: int     			=16
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=14
      SCREAMING_SNAKE_CASE_: Any     			=1280
      SCREAMING_SNAKE_CASE_: Dict     			=5120
      SCREAMING_SNAKE_CASE_: Optional[int]     			=32
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=16

   SCREAMING_SNAKE_CASE_: Tuple     			=ViTMAEForPreTraining(lowercase )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=ViTMAEImageProcessor(size=config.image_size )

   SCREAMING_SNAKE_CASE_: str     			=convert_state_dict(lowercase							,			lowercase )

   model.load_state_dict(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			="""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: int     			=ViTMAEImageProcessor(size=config.image_size )
   SCREAMING_SNAKE_CASE_: int     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   # forward pass
   torch.manual_seed(2 )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=outputs.logits

   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Dict     			=torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

   # verify logits
   assert torch.allclose(logits[0, :3, :3]							,			lowercase							,			atol=1e-4 )

   print(f'''Saving model to {pytorch_dump_folder_path}''' )
   model.save_pretrained(lowercase )

   print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
   image_processor.save_pretrained(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
       type=str,
       help="""URL of the checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



