



'''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()
_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=False			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					[]
    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"
        lowerCAmelCase__ :  Tuple							      =					[(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  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=False			):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase							,    UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					dct.pop(UpperCamelCase			)
    lowerCAmelCase__ :  int							      =					val
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__ :  List[Any]							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			)
    return im
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					ViTConfig()
    lowerCAmelCase__ :  Any							      =					False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :  int							      =					True
        lowerCAmelCase__ :  Optional[int]							      =					int(vit_name[-12:-10]			)
        lowerCAmelCase__ :  Dict							      =					int(vit_name[-9:-6]			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					1000
        lowerCAmelCase__ :  Any							      =					"""huggingface/label-files"""
        lowerCAmelCase__ :  List[Any]							      =					"""imagenet-1k-id2label.json"""
        lowerCAmelCase__ :  Any							      =					json.load(open(hf_hub_download(UpperCamelCase							,    UpperCamelCase							,    repo_type="""dataset"""			)							,    """r"""			)			)
        lowerCAmelCase__ :  Optional[int]							      =					{int(UpperCamelCase			): v for k, v in idalabel.items()}
        lowerCAmelCase__ :  int							      =					idalabel
        lowerCAmelCase__ :  Optional[Any]							      =					{v: k for k, v in idalabel.items()}
        lowerCAmelCase__ :  Optional[Any]							      =					int(vit_name[-6:-4]			)
        lowerCAmelCase__ :  Union[str, Any]							      =					int(vit_name[-3:]			)
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("""tiny"""			):
            lowerCAmelCase__ :  List[Any]							      =					192
            lowerCAmelCase__ :  Tuple							      =					768
            lowerCAmelCase__ :  List[Any]							      =					12
            lowerCAmelCase__ :  str							      =					3
        elif vit_name[9:].startswith("""small"""			):
            lowerCAmelCase__ :  List[str]							      =					384
            lowerCAmelCase__ :  List[Any]							      =					1536
            lowerCAmelCase__ :  Any							      =					12
            lowerCAmelCase__ :  List[str]							      =					6
        else:
            pass
    else:
        if vit_name[4:].startswith("""small"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					768
            lowerCAmelCase__ :  Any							      =					2304
            lowerCAmelCase__ :  Tuple							      =					8
            lowerCAmelCase__ :  str							      =					8
        elif vit_name[4:].startswith("""base"""			):
            pass
        elif vit_name[4:].startswith("""large"""			):
            lowerCAmelCase__ :  Any							      =					1024
            lowerCAmelCase__ :  List[str]							      =					4096
            lowerCAmelCase__ :  Optional[Any]							      =					24
            lowerCAmelCase__ :  Optional[int]							      =					16
        elif vit_name[4:].startswith("""huge"""			):
            lowerCAmelCase__ :  List[str]							      =					1280
            lowerCAmelCase__ :  int							      =					5120
            lowerCAmelCase__ :  Optional[int]							      =					32
            lowerCAmelCase__ :  List[Any]							      =					16

    # load original model from timm
    lowerCAmelCase__ :  Optional[Any]							      =					timm.create_model(UpperCamelCase							,    pretrained=UpperCamelCase			)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    lowerCAmelCase__ :  Union[str, Any]							      =					timm_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					create_rename_keys(UpperCamelCase							,    UpperCamelCase			)
    for src, dest in rename_keys:
        rename_key(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
    read_in_q_k_v(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :  Union[str, Any]							      =					ViTModel(UpperCamelCase			).eval()
    else:
        lowerCAmelCase__ :  Any							      =					ViTForImageClassification(UpperCamelCase			).eval()
    model.load_state_dict(UpperCamelCase			)

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        lowerCAmelCase__ :  int							      =					DeiTImageProcessor(size=config.image_size			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					ViTImageProcessor(size=config.image_size			)
    lowerCAmelCase__ :  Optional[Any]							      =					image_processor(images=prepare_img()							,    return_tensors="""pt"""			)
    lowerCAmelCase__ :  Tuple							      =					encoding["""pixel_values"""]
    lowerCAmelCase__ :  str							      =					model(UpperCamelCase			)

    if base_model:
        lowerCAmelCase__ :  int							      =					timm_model.forward_features(UpperCamelCase			)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(UpperCamelCase							,    outputs.pooler_output							,    atol=1e-3			)
    else:
        lowerCAmelCase__ :  Optional[Any]							      =					timm_model(UpperCamelCase			)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(UpperCamelCase							,    outputs.logits							,    atol=1e-3			)

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


if __name__ == "__main__":
 _lowerCAmelCase      						=	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.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''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


_lowerCAmelCase      						=	logging.get_logger(__name__)






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None      )    ->		str:
      if not conversation_id:
          lowerCAmelCase__ :  List[str]							      =					uuid.uuida()
      if past_user_inputs is None:
          lowerCAmelCase__ :  List[Any]							      =					[]
      if generated_responses is None:
          lowerCAmelCase__ :  str							      =					[]

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




  def __eq__(    self  ,__UpperCAmelCase      )    ->		Dict:
      if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Optional[Any]:
      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}\"."""      )
              lowerCAmelCase__ :  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:
          lowerCAmelCase__ :  Optional[Any]							      =					text




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      if self.new_user_input:
          self.past_user_inputs.append(self.new_user_input      )
      lowerCAmelCase__ :  Union[str, Any]							      =					None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      self.generated_responses.append(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      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      )    ->		Tuple:
      lowerCAmelCase__ :  Tuple							      =					F"""Conversation id: {self.uuid} \n"""
      for is_user, text in self.iter_texts():
          lowerCAmelCase__ :  Any							      =					"""user""" if is_user else """bot"""
          output += F"""{name} >> {text} \n"""
      return output







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




  def __init__(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		Tuple:
      super().__init__(*__UpperCAmelCase  ,**__UpperCAmelCase      )
      if self.tokenizer.pad_token_id is None:
          lowerCAmelCase__ :  Tuple							      =					self.tokenizer.eos_token




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[Any]							      =					{}
      lowerCAmelCase__ :  Optional[int]							      =					{}
      lowerCAmelCase__ :  List[str]							      =					{}

      if min_length_for_response is not None:
          lowerCAmelCase__ :  Any							      =					min_length_for_response
      if minimum_tokens is not None:
          lowerCAmelCase__ :  Optional[int]							      =					minimum_tokens

      if "max_length" in generate_kwargs:
          lowerCAmelCase__ :  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:
          lowerCAmelCase__ :  int							      =					clean_up_tokenization_spaces

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




  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=0  ,**__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					super().__call__(__UpperCAmelCase  ,num_workers=__UpperCAmelCase  ,**__UpperCAmelCase      )
      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) and len(__UpperCAmelCase      ) == 1:
          return outputs[0]
      return outputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=32      )    ->		Dict[str, Any]:
      if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          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"""      ):
          lowerCAmelCase__ :  str							      =					self.tokenizer._build_conversation_input_ids(__UpperCAmelCase      )
      else:
          # If the tokenizer cannot handle conversations, we default to only the old version
          lowerCAmelCase__ :  List[Any]							      =					self._legacy_parse_and_tokenize(__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=10  ,**__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Optional[Any]							      =					generate_kwargs.get("""max_length"""  ,self.model.config.max_length      )

      lowerCAmelCase__ :  Optional[Any]							      =					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})"""      )
          lowerCAmelCase__ :  str							      =					max_length - minimum_tokens
          lowerCAmelCase__ :  Union[str, Any]							      =					model_inputs["""input_ids"""][:, -trim:]
          if "attention_mask" in model_inputs:
              lowerCAmelCase__ :  Tuple							      =					model_inputs["""attention_mask"""][:, -trim:]
      lowerCAmelCase__ :  str							      =					model_inputs.pop("""conversation"""      )
      lowerCAmelCase__ :  Union[str, Any]							      =					max_length
      lowerCAmelCase__ :  Any							      =					self.model.generate(**__UpperCAmelCase  ,**__UpperCAmelCase      )
      if self.model.config.is_encoder_decoder:
          lowerCAmelCase__ :  int							      =					1
      else:
          lowerCAmelCase__ :  int							      =					n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True      )    ->		List[str]:
      lowerCAmelCase__ :  Optional[int]							      =					model_outputs["""output_ids"""]
      lowerCAmelCase__ :  Tuple							      =					self.tokenizer.decode(
          output_ids[0]  ,skip_special_tokens=__UpperCAmelCase  ,clean_up_tokenization_spaces=__UpperCAmelCase  ,)
      lowerCAmelCase__ :  Union[str, Any]							      =					model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__UpperCAmelCase      )
      return conversation






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Dict:
      lowerCAmelCase__ :  Dict							      =					self.tokenizer.eos_token_id
      lowerCAmelCase__ :  int							      =					[]
      for is_user, text in conversation.iter_texts():
          if eos_token_id is not None:
              input_ids.extend(self.tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ) + [eos_token_id]      )
          else:
              input_ids.extend(self.tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      )      )

      if len(__UpperCAmelCase      ) > self.tokenizer.model_max_length:
          lowerCAmelCase__ :  Optional[Any]							      =					input_ids[-self.tokenizer.model_max_length :]
      return input_ids


