




import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a													=			{
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Union[str, Any]:
						_UpperCAmelCase      =			{}
						state_dict.pop("""pixel_mean"""			,     snake_case       )
						state_dict.pop("""pixel_std"""			,     snake_case       )

						_UpperCAmelCase      =			R""".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"""

						for key, value in state_dict.items():
												for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																		if key_to_modify in key:
																								_UpperCAmelCase      =			key.replace(snake_case			,     snake_case       )

												if re.match(snake_case			,     snake_case       ):
																		_UpperCAmelCase      =			int(re.match(snake_case			,     snake_case       ).group(2       )       )
																		if layer_nb == 0:
																								_UpperCAmelCase      =			key.replace("""layers.0"""			,     """proj_in"""       )
																		elif layer_nb == 1:
																								_UpperCAmelCase      =			key.replace("""layers.1"""			,     """layers.0"""       )
																		elif layer_nb == 2:
																								_UpperCAmelCase      =			key.replace("""layers.2"""			,     """proj_out"""       )

												_UpperCAmelCase      =			value

						_UpperCAmelCase      =			model_state_dict[
						    """prompt_encoder.shared_embedding.positional_embedding"""
						]

						return model_state_dict




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case="ybelkada/segment-anything"       )						->				Dict:
						_UpperCAmelCase      =			hf_hub_download(snake_case			,     f"checkpoints/{model_name}.pth"       )

						if "sam_vit_b" in model_name:
												_UpperCAmelCase      =			SamConfig()
						elif "sam_vit_l" in model_name:
												_UpperCAmelCase      =			SamVisionConfig(
												    hidden_size=1_0_2_4			,     num_hidden_layers=2_4			,     num_attention_heads=1_6			,     global_attn_indexes=[5, 1_1, 1_7, 2_3]			,     )

												_UpperCAmelCase      =			SamConfig(
												    vision_config=snake_case			,     )
						elif "sam_vit_h" in model_name:
												_UpperCAmelCase      =			SamVisionConfig(
												    hidden_size=1_2_8_0			,     num_hidden_layers=3_2			,     num_attention_heads=1_6			,     global_attn_indexes=[7, 1_5, 2_3, 3_1]			,     )

												_UpperCAmelCase      =			SamConfig(
												    vision_config=snake_case			,     )

						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )
						_UpperCAmelCase      =			replace_keys(snake_case       )

						_UpperCAmelCase      =			SamImageProcessor()

						_UpperCAmelCase      =			SamProcessor(image_processor=snake_case       )
						_UpperCAmelCase      =			SamModel(snake_case       )

						hf_model.load_state_dict(snake_case       )
						_UpperCAmelCase      =			hf_model.to("""cuda"""       )

						_UpperCAmelCase      =			"""https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"""
						_UpperCAmelCase      =			Image.open(requests.get(snake_case			,     stream=snake_case       ).raw       ).convert("""RGB"""       )

						_UpperCAmelCase      =			[[[4_0_0, 6_5_0]]]
						_UpperCAmelCase      =			[[1]]

						_UpperCAmelCase      =			processor(images=np.array(snake_case       )			,     return_tensors="""pt"""       ).to("""cuda"""       )

						with torch.no_grad():
												_UpperCAmelCase      =			hf_model(**snake_case       )
						_UpperCAmelCase      =			output.iou_scores.squeeze()

						if model_name == "sam_vit_h_4b8939":
												assert scores[-1].item() == 0.579890251159668

												_UpperCAmelCase      =			processor(
												    images=np.array(snake_case       )			,     input_points=snake_case			,     input_labels=snake_case			,     return_tensors="""pt"""       ).to("""cuda"""       )

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9712603092193604

												_UpperCAmelCase      =			((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

												_UpperCAmelCase      =			processor(images=np.array(snake_case       )			,     input_boxes=snake_case			,     return_tensors="""pt"""       ).to("""cuda"""       )

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.8686015605926514

												# Test with 2 points and 1 image.
												_UpperCAmelCase      =			[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
												_UpperCAmelCase      =			[[1, 1]]

												_UpperCAmelCase      =			processor(
												    images=np.array(snake_case       )			,     input_points=snake_case			,     input_labels=snake_case			,     return_tensors="""pt"""       ).to("""cuda"""       )

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				a													=			["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
				parser.add_argument(
				    "--model_name",
				    default="sam_vit_h_4b8939",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)
				parser.add_argument(
				    "--model_hub_id",
				    default="ybelkada/segment-anything",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)

				a													=			parser.parse_args()

				convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)





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


a													=			logging.get_logger(__name__)




class       _A				:






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

								_UpperCAmelCase      =			conversation_id
								_UpperCAmelCase      =			past_user_inputs
								_UpperCAmelCase      =			generated_responses
								_UpperCAmelCase      =			text






		def __eq__(    self						,		_SCREAMING_SNAKE_CASE			):
								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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_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}\"."			)
																				_UpperCAmelCase      =			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:
														_UpperCAmelCase      =			text






		def 				UpperCAmelCase (    self			):
								if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input			)
								_UpperCAmelCase      =			None






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								self.generated_responses.append(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    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			):
								_UpperCAmelCase      =			F"Conversation id: {self.uuid} \n"
								for is_user, text in self.iter_texts():
														_UpperCAmelCase      =			"""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						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if self.tokenizer.pad_token_id is None:
														_UpperCAmelCase      =			self.tokenizer.eos_token






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}

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

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

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






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=32			):
								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"""			):
														_UpperCAmelCase      =			self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE			)
								else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														_UpperCAmelCase      =			self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=10						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			generate_kwargs.get("""max_length"""						,		self.model.config.max_length			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=True			):
								_UpperCAmelCase      =			model_outputs["""output_ids"""]
								_UpperCAmelCase      =			self.tokenizer.decode(
								    output_ids[0]						,		skip_special_tokens=_SCREAMING_SNAKE_CASE						,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(_SCREAMING_SNAKE_CASE			)
								return conversation






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.tokenizer.eos_token_id
								_UpperCAmelCase      =			[]
								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:
														_UpperCAmelCase      =			input_ids[-self.tokenizer.model_max_length :]
								return input_ids