

import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:   str										=       logging.get_logger(__name__)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    SwinConfig.from_pretrained(
							    'microsoft/swin-tiny-patch4-window7-224'   ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
							snake_case_							=    MaskFormerConfig(backbone_config=UpperCamelCase__      )

							snake_case_							=    'huggingface/label-files'
							if "ade20k-full" in model_name:
														# this should be ok
														snake_case_							=    847
														snake_case_							=    'maskformer-ade20k-full-id2label.json'
							elif "ade" in model_name:
														# this should be ok
														snake_case_							=    150
														snake_case_							=    'ade20k-id2label.json'
							elif "coco-stuff" in model_name:
														# this should be ok
														snake_case_							=    171
														snake_case_							=    'maskformer-coco-stuff-id2label.json'
							elif "coco" in model_name:
														# TODO
														snake_case_							=    133
														snake_case_							=    'coco-panoptic-id2label.json'
							elif "cityscapes" in model_name:
														# this should be ok
														snake_case_							=    19
														snake_case_							=    'cityscapes-id2label.json'
							elif "vistas" in model_name:
														# this should be ok
														snake_case_							=    65
														snake_case_							=    'mapillary-vistas-id2label.json'

							snake_case_							=    json.load(open(hf_hub_download(UpperCamelCase__   ,							UpperCamelCase__   ,							repo_type='dataset'      )   ,							'r'      )      )
							snake_case_							=    {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

							return config





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							# stem
							# fmt: off
							rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')      )
							rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')      )
							rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')      )
							rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')      )
							# stages
							for i in range(len(config.backbone_config.depths      )      ):
														for j in range(config.backbone_config.depths[i]      ):
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')      )

														if i < 3:
																					rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')      )
																					rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')      )
														rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')      )
														rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')      )

							# FPN
							rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')      )
							rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')      )
							rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')      )
							for source_index, target_index in zip(range(3   ,							0   ,							-1      )   ,							range(0   ,							3      )      ):
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')      )
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')      )
														rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')      )
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')      )
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')      )
														rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')      )
							rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')      )
							rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')      )

							# Transformer decoder
							for idx in range(config.decoder_config.decoder_layers      ):
														# self-attention out projection
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')      )
														# cross-attention out projection
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')      )
														# MLP 1
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')      )
														# MLP 2
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')      )
														# layernorm 1 (self-attention layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')      )
														# layernorm 2 (cross-attention layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')      )
														# layernorm 3 (final layernorm)
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')      )

							rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')      )
							rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')      )

							# heads on top
							rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')      )

							rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')      )
							rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')      )

							rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')      )
							rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')      )

							for i in range(3      ):
														rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')      )
														rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')      )
							# fmt: on

							return rename_keys





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    dct.pop(UpperCamelCase__      )
							snake_case_							=    val





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
							for i in range(len(backbone_config.depths      )      ):
														snake_case_							=    num_features[i]
														for j in range(backbone_config.depths[i]      ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					snake_case_							=    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
																					snake_case_							=    state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''      )
																					# next, add query, keys and values (in that order) to the state dict
																					snake_case_							=    in_proj_weight[:dim, :]
																					snake_case_							=    in_proj_bias[: dim]
																					snake_case_							=    in_proj_weight[
																					    dim : dim * 2, :
																					]
																					snake_case_							=    in_proj_bias[
																					    dim : dim * 2
																					]
																					snake_case_							=    in_proj_weight[
																					    -dim :, :
																					]
																					snake_case_							=    in_proj_bias[-dim :]
																					# fmt: on





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    config.decoder_config.hidden_size
							for idx in range(config.decoder_config.decoder_layers      ):
														# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
														snake_case_							=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''      )
														snake_case_							=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''      )
														# next, add query, keys and values (in that order) to the state dict
														snake_case_							=    in_proj_weight[: hidden_size, :]
														snake_case_							=    in_proj_bias[:config.hidden_size]
														snake_case_							=    in_proj_weight[hidden_size : hidden_size * 2, :]
														snake_case_							=    in_proj_bias[hidden_size : hidden_size * 2]
														snake_case_							=    in_proj_weight[-hidden_size :, :]
														snake_case_							=    in_proj_bias[-hidden_size :]
														# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
														snake_case_							=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''      )
														snake_case_							=    state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''      )
														# next, add query, keys and values (in that order) to the state dict
														snake_case_							=    in_proj_weight[: hidden_size, :]
														snake_case_							=    in_proj_bias[:config.hidden_size]
														snake_case_							=    in_proj_weight[hidden_size : hidden_size * 2, :]
														snake_case_							=    in_proj_bias[hidden_size : hidden_size * 2]
														snake_case_							=    in_proj_weight[-hidden_size :, :]
														snake_case_							=    in_proj_bias[-hidden_size :]
							# fmt: on





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_							=    Image.open(requests.get(UpperCamelCase__   ,							stream=UpperCamelCase__      ).raw      )
							return im





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = False      ):







							'''simple docstring'''






							snake_case_							=    get_maskformer_config(UpperCamelCase__      )

							# load original state_dict
							with open(UpperCamelCase__   ,							'rb'      ) as f:
														snake_case_							=    pickle.load(UpperCamelCase__      )
							snake_case_							=    data['model']

							# for name, param in state_dict.items():
							#     print(name, param.shape)

							# rename keys
							snake_case_							=    create_rename_keys(UpperCamelCase__      )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							read_in_swin_q_k_v(UpperCamelCase__   ,							config.backbone_config      )
							read_in_decoder_q_k_v(UpperCamelCase__   ,							UpperCamelCase__      )

							# update to torch tensors
							for key, value in state_dict.items():
														snake_case_							=    torch.from_numpy(UpperCamelCase__      )

							# load 🤗 model
							snake_case_							=    MaskFormerForInstanceSegmentation(UpperCamelCase__      )
							model.eval()

							for name, param in model.named_parameters():
														print(UpperCamelCase__   ,							param.shape      )

							snake_case_				,    snake_case_							=    model.load_state_dict(UpperCamelCase__   ,							strict=UpperCamelCase__      )
							assert missing_keys == [
							    "model.pixel_level_module.encoder.model.layernorm.weight",
							    "model.pixel_level_module.encoder.model.layernorm.bias",
							]
							assert len(UpperCamelCase__      ) == 0, F'''Unexpected keys: {unexpected_keys}'''

							# verify results
							snake_case_							=    prepare_img()
							if "vistas" in model_name:
														snake_case_							=    65
							elif "cityscapes" in model_name:
														snake_case_							=    65535
							else:
														snake_case_							=    255
							snake_case_							=    True if 'ade' in model_name else False
							snake_case_							=    MaskFormerImageProcessor(ignore_index=UpperCamelCase__   ,							reduce_labels=UpperCamelCase__      )

							snake_case_							=    image_processor(UpperCamelCase__   ,							return_tensors='pt'      )

							snake_case_							=    model(**UpperCamelCase__      )

							print('Logits:'   ,							outputs.class_queries_logits[0, :3, :3]      )

							if model_name == "maskformer-swin-tiny-ade":
														snake_case_							=    torch.tensor(
														    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]      )
							assert torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,							UpperCamelCase__   ,							atol=1E-4      )
							print('Looks ok!'      )

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''      )
														Path(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
														model.save_pretrained(UpperCamelCase__      )
														image_processor.save_pretrained(UpperCamelCase__      )

							if push_to_hub:
														print('Pushing model and image processor to the hub...'      )
														model.push_to_hub(F'''nielsr/{model_name}'''      )
														image_processor.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
					_UpperCAmelCase		:   List[Any]										=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""maskformer-swin-tiny-ade""",
					    type=str,
					    help=("""Name of the MaskFormer model you'd like to convert""",),
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
					    type=str,
					    help="""Path to the original state dict (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					_UpperCAmelCase		:   int										=       parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)







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		:   Dict										=       logging.get_logger(__name__)







class  lowercase				:




				def __init__(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   snake_case=None       ):
											if not conversation_id:
																		snake_case_							=    uuid.uuida()
											if past_user_inputs is None:
																		snake_case_							=    []
											if generated_responses is None:
																		snake_case_							=    []

											snake_case_							=    conversation_id
											snake_case_							=    past_user_inputs
											snake_case_							=    generated_responses
											snake_case_							=    text




				def __eq__(					self      ,   snake_case       ):
											if not isinstance(snake_case      ,   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     a							(					self      ,   snake_case      ,   snake_case = False       ):
											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}".'''       )
																									snake_case_							=    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:
																		snake_case_							=    text




				def     a							(					self       ):
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											snake_case_							=    None




				def     a							(					self      ,   snake_case       ):
											self.generated_responses.append(snake_case       )




				def     a							(					self       ):
											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       ):
											snake_case_							=    F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		snake_case_							=    '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  lowercase				(       lowercase_			):




				def __init__(					self      ,   *snake_case      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )
											if self.tokenizer.pad_token_id is None:
																		snake_case_							=    self.tokenizer.eos_token




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    {}
											snake_case_							=    {}

											if min_length_for_response is not None:
																		snake_case_							=    min_length_for_response
											if minimum_tokens is not None:
																		snake_case_							=    minimum_tokens

											if "max_length" in generate_kwargs:
																		snake_case_							=    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:
																		snake_case_							=    clean_up_tokenization_spaces

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




				def __call__(					self      ,   snake_case      ,   snake_case=0      ,   **snake_case       ):
											snake_case_							=    super().__call__(snake_case      ,   num_workers=snake_case      ,   **snake_case       )
											if isinstance(snake_case      ,   snake_case       ) and len(snake_case       ) == 1:
																		return outputs[0]
											return outputs




				def     a							(					self      ,   snake_case      ,   snake_case=32       ):
											if not isinstance(snake_case      ,   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'       ):
																		snake_case_							=    self.tokenizer._build_conversation_input_ids(snake_case       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		snake_case_							=    self._legacy_parse_and_tokenize(snake_case       )

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




				def     a							(					self      ,   snake_case      ,   snake_case=10      ,   **snake_case       ):
											snake_case_							=    generate_kwargs.get('max_length'      ,   self.model.config.max_length       )

											snake_case_							=    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})'''       )
																		snake_case_							=    max_length - minimum_tokens
																		snake_case_							=    model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									snake_case_							=    model_inputs['attention_mask'][:, -trim:]
											snake_case_							=    model_inputs.pop('conversation'       )
											snake_case_							=    max_length
											snake_case_							=    self.model.generate(**snake_case      ,   **snake_case       )
											if self.model.config.is_encoder_decoder:
																		snake_case_							=    1
											else:
																		snake_case_							=    n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=True       ):
											snake_case_							=    model_outputs['output_ids']
											snake_case_							=    self.tokenizer.decode(
											    output_ids[0]      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   )
											snake_case_							=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(snake_case       )
											return conversation




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.tokenizer.eos_token_id
											snake_case_							=    []
											for is_user, text in conversation.iter_texts():
																		if eos_token_id is not None:
																									input_ids.extend(self.tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       ) + [eos_token_id]       )
																		else:
																									input_ids.extend(self.tokenizer.encode(snake_case      ,   add_special_tokens=snake_case       )       )

											if len(snake_case       ) > self.tokenizer.model_max_length:
																		snake_case_							=    input_ids[-self.tokenizer.model_max_length :]
											return input_ids




