


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  _a							:




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

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




	def __eq__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Optional[int]:
			if not isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
					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						:		Dict		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		bool = False		)->	Tuple:
			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__       :      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__       :      Union[str, Any]   					= text




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Any:
			if self.new_user_input:
					self.past_user_inputs.append(self.new_user_input		)
			lowerCAmelCase__       :      Optional[int]   					= None




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	Optional[int]:
			self.generated_responses.append(_SCREAMING_SNAKE_CASE		)




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





@add_end_docstrings(
    _lowercase					,						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  _a							(    _lowercase):




	def __init__( self						:		List[str]		, *_SCREAMING_SNAKE_CASE						:		Dict		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	Tuple:
			super().__init__(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			if self.tokenizer.pad_token_id is None:
					lowerCAmelCase__       :      Optional[int]   					= self.tokenizer.eos_token




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

			if min_length_for_response is not None:
					lowerCAmelCase__       :      int   					= min_length_for_response
			if minimum_tokens is not None:
					lowerCAmelCase__       :      List[str]   					= minimum_tokens

			if "max_length" in generate_kwargs:
					lowerCAmelCase__       :      Optional[int]   					= 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__       :      Dict   					= clean_up_tokenization_spaces

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




	def __call__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Union[Conversation, List[Conversation]]		, _SCREAMING_SNAKE_CASE						:		Any=0		, **_SCREAMING_SNAKE_CASE						:		Any		)->	Dict:
			lowerCAmelCase__       :      Dict   					= super().__call__(_SCREAMING_SNAKE_CASE		, num_workers=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) and len(_SCREAMING_SNAKE_CASE		) == 1:
					return outputs[0]
			return outputs




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

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




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str=10		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	List[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= generate_kwargs.get('''max_length'''		, self.model.config.max_length		)

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




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any=True		)->	List[Any]:
			lowerCAmelCase__       :      List[Any]   					= model_outputs['''output_ids''']
			lowerCAmelCase__       :      List[str]   					= self.tokenizer.decode(
			    output_ids[0]		, skip_special_tokens=_SCREAMING_SNAKE_CASE		, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		, )
			lowerCAmelCase__       :      Union[str, Any]   					= model_outputs['''conversation''']
			conversation.mark_processed()
			conversation.append_response(_SCREAMING_SNAKE_CASE		)
			return conversation




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

			if len(_SCREAMING_SNAKE_CASE		) > self.tokenizer.model_max_length:
					lowerCAmelCase__       :      Union[str, Any]   					= input_ids[-self.tokenizer.model_max_length :]
			return input_ids








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 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		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 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      Tuple   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= 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[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

    # load original model from timm
		lowerCAmelCase__       :      Tuple   					= timm.create_model(_a     ,       pretrained=_a	)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase__       :      Any   					= timm_model.state_dict()
		if base_model:
				remove_classification_head_(_a	)
		lowerCAmelCase__       :      List[Any]   					= 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":
				lowerCAmelCase__       :      List[str]   					= ViTModel(_a	).eval()
		else:
				lowerCAmelCase__       :      Any   					= ViTForImageClassification(_a	).eval()
		model.load_state_dict(_a	)

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

		if base_model:
				lowerCAmelCase__       :      Optional[Any]   					= 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:
				lowerCAmelCase__       :      Union[str, Any]   					= 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__":
		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)




