


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_      : Tuple  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       str = None     ,							SCREAMING_SNAKE_CASE__				:       uuid.UUID = None     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       int=None     )		->			List[Any]:
             if not conversation_id:
                    a_					:		Any							=  uuid.uuida()
             if past_user_inputs is None:
                    a_					:		int							=  []
             if generated_responses is None:
                    a_					:		int							=  []

             a_					:		uuid.UUID							=  conversation_id
             a_					:		List[str]							=  past_user_inputs
             a_					:		List[str]							=  generated_responses
             a_					:		Optional[str]							=  text
      def __eq__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Tuple:
             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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       bool = 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}\"."""     )
                           a_					:		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:
                    a_					:		Optional[Any]							=  text
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			List[str]:
             if self.new_user_input:
                    self.past_user_inputs.append(self.new_user_input     )
             a_					:		List[Any]							=  None
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Dict:
             self.generated_responses.append(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Optional[Any]:
             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     )		->			Dict:
             a_					:		str							=  F"""Conversation id: {self.uuid} \n"""
             for is_user, text in self.iter_texts():
                    a_					:		List[str]							=  '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  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      def __init__(       self				:       List[Any]     ,							*SCREAMING_SNAKE_CASE__				:       Any     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Optional[Any]:
             super().__init__(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
             if self.tokenizer.pad_token_id is None:
                    a_					:		Dict							=  self.tokenizer.eos_token
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Union[str, Any]:
             a_					:		Optional[int]							=  {}
             a_					:		Optional[Any]							=  {}
             a_					:		Dict							=  {}

             if min_length_for_response is not None:
                    a_					:		Optional[int]							=  min_length_for_response
             if minimum_tokens is not None:
                    a_					:		Union[str, Any]							=  minimum_tokens

             if "max_length" in generate_kwargs:
                    a_					:		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:
                    a_					:		List[Any]							=  clean_up_tokenization_spaces

             if generate_kwargs:
                    forward_params.update(SCREAMING_SNAKE_CASE__     )
             return preprocess_params, forward_params, postprocess_params
      def __call__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[Conversation, List[Conversation]]     ,							SCREAMING_SNAKE_CASE__				:       Any=0     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     )		->			List[str]:
             a_					:		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        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Conversation     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_2     )		->			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'     ):
                    a_					:		str							=  self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE__     )
             else:
                    # If the tokenizer cannot handle conversations, we default to only the old version
                    a_					:		Any							=  self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE__     )

             if self.framework == "pt":
                    a_					:		Any							=  torch.LongTensor([input_ids]     )
             elif self.framework == "tf":
                    a_					:		Dict							=  tf.constant([input_ids]     )
             return {"input_ids": input_ids, "conversation": conversation}
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_0     ,							**SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Union[str, Any]							=  generate_kwargs.get('max_length'     ,							self.model.config.max_length     )

             a_					:		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})"""     )
                    a_					:		Any							=  max_length - minimum_tokens
                    a_					:		Tuple							=  model_inputs['input_ids'][:, -trim:]
                    if "attention_mask" in model_inputs:
                           a_					:		Tuple							=  model_inputs['attention_mask'][:, -trim:]
             a_					:		Dict							=  model_inputs.pop('conversation'     )
             a_					:		List[Any]							=  max_length
             a_					:		Optional[int]							=  self.model.generate(**SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
             if self.model.config.is_encoder_decoder:
                    a_					:		Optional[int]							=  1
             else:
                    a_					:		List[str]							=  n
             return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     )		->			Optional[Any]:
             a_					:		Optional[int]							=  model_outputs['output_ids']
             a_					:		List[Any]							=  self.tokenizer.decode(
                 output_ids[0]     ,							skip_special_tokens=SCREAMING_SNAKE_CASE__     ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Optional[int]							=  model_outputs['conversation']
             conversation.mark_processed()
             conversation.append_response(SCREAMING_SNAKE_CASE__     )
             return conversation

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Conversation     )		->			Dict:
             a_					:		Optional[int]							=  self.tokenizer.eos_token_id
             a_					:		List[Any]							=  []
             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:
                    a_					:		str							=  input_ids[-self.tokenizer.model_max_length :]
             return input_ids







"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_   						=   logging.get_logger()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

		with torch.no_grad():
				if hidden_sizes == 1_28:
						if name[-1] == "S":
								lowercase__   :    str				=			timm.create_model('''levit_128s'''						,     pretrained=__lowerCamelCase	)
						else:
								lowercase__   :    Tuple				=			timm.create_model('''levit_128'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 1_92:
						lowercase__   :    Union[str, Any]				=			timm.create_model('''levit_192'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 2_56:
						lowercase__   :    str				=			timm.create_model('''levit_256'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 3_84:
						lowercase__   :    str				=			timm.create_model('''levit_384'''						,     pretrained=__lowerCamelCase	)

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

				lowercase__   :    int				=			from_model.state_dict()
				lowercase__   :    Dict				=			list(from_model.state_dict().keys()	)
				lowercase__   :    Any				=			list(our_model.state_dict().keys()	)
				print(len(__lowerCamelCase	)						,     len(__lowerCamelCase	)	)
				for i in range(len(__lowerCamelCase	)	):
						lowercase__   :    str				=			weights[og_keys[i]]
				our_model.load_state_dict(__lowerCamelCase	)

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

		assert torch.allclose(__lowerCamelCase						,     __lowerCamelCase	), "The model logits don't match the original one."

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name	)
				lowercase__   :    int				=			LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name	)

				print(f"""Pushed {checkpoint_name}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

		lowercase__   :    List[str]				=			'''huggingface/label-files'''
		lowercase__   :    str				=			num_labels
		lowercase__   :    List[Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
		lowercase__   :    Union[str, Any]				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Union[str, Any]				=			idalabel
		lowercase__   :    Optional[int]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		lowercase__   :    Tuple				=			{
		    '''levit-128S''': 1_28,
		    '''levit-128''': 1_28,
		    '''levit-192''': 1_92,
		    '''levit-256''': 2_56,
		    '''levit-384''': 3_84,
		}

		lowercase__   :    Any				=			{
		    '''levit-128S''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 6, 8]						,     depths=[2, 3, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-128''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 8, 12]						,     depths=[4, 4, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-192''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_92, 2_88, 3_84]						,     num_attention_heads=[3, 5, 6]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-256''': ImageNetPreTrainedConfig(
		        hidden_sizes=[2_56, 3_84, 5_12]						,     num_attention_heads=[4, 6, 8]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-384''': ImageNetPreTrainedConfig(
		        hidden_sizes=[3_84, 5_12, 7_68]						,     num_attention_heads=[6, 9, 12]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0.1						,     ),
		}

		if model_name:
				convert_weight_and_push(
				    names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     names_to_config[model_name]						,     __lowerCamelCase						,     __lowerCamelCase	)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							lowerCAmelCase_   						=   parser.parse_args()
							lowerCAmelCase_   						=   args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
