


import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   CLIPSegTextConfig()
					_lowerCAmelCase      =   CLIPSegVisionConfig(patch_size=16      )

					_lowerCAmelCase      =   True if """refined""" in model_name else False
					_lowerCAmelCase      =   16 if """rd16""" in model_name else 64

					_lowerCAmelCase      =   CLIPSegConfig.from_text_vision_configs(
					    _A     ,						_A     ,						use_complex_transposed_convolution=_A     ,						reduce_dim=_A     ,						)
					return config


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if "clip_model" in name:
										_lowerCAmelCase      =   name.replace("""clip_model"""     ,						"""clip"""      )
					if "transformer" in name:
										if "visual" in name:
															_lowerCAmelCase      =   name.replace("""visual.transformer"""     ,						"""vision_model"""      )
										else:
															_lowerCAmelCase      =   name.replace("""transformer"""     ,						"""text_model"""      )
					if "resblocks" in name:
										_lowerCAmelCase      =   name.replace("""resblocks"""     ,						"""encoder.layers"""      )
					if "ln_1" in name:
										_lowerCAmelCase      =   name.replace("""ln_1"""     ,						"""layer_norm1"""      )
					if "ln_2" in name:
										_lowerCAmelCase      =   name.replace("""ln_2"""     ,						"""layer_norm2"""      )
					if "c_fc" in name:
										_lowerCAmelCase      =   name.replace("""c_fc"""     ,						"""fc1"""      )
					if "c_proj" in name:
										_lowerCAmelCase      =   name.replace("""c_proj"""     ,						"""fc2"""      )
					if "attn" in name and "self" not in name:
										_lowerCAmelCase      =   name.replace("""attn"""     ,						"""self_attn"""      )
					# text encoder
					if "token_embedding" in name:
										_lowerCAmelCase      =   name.replace("""token_embedding"""     ,						"""text_model.embeddings.token_embedding"""      )
					if "positional_embedding" in name and "visual" not in name:
										_lowerCAmelCase      =   name.replace("""positional_embedding"""     ,						"""text_model.embeddings.position_embedding.weight"""      )
					if "ln_final" in name:
										_lowerCAmelCase      =   name.replace("""ln_final"""     ,						"""text_model.final_layer_norm"""      )
					# vision encoder
					if "visual.class_embedding" in name:
										_lowerCAmelCase      =   name.replace("""visual.class_embedding"""     ,						"""vision_model.embeddings.class_embedding"""      )
					if "visual.conv1" in name:
										_lowerCAmelCase      =   name.replace("""visual.conv1"""     ,						"""vision_model.embeddings.patch_embedding"""      )
					if "visual.positional_embedding" in name:
										_lowerCAmelCase      =   name.replace("""visual.positional_embedding"""     ,						"""vision_model.embeddings.position_embedding.weight"""      )
					if "visual.ln_pre" in name:
										_lowerCAmelCase      =   name.replace("""visual.ln_pre"""     ,						"""vision_model.pre_layrnorm"""      )
					if "visual.ln_post" in name:
										_lowerCAmelCase      =   name.replace("""visual.ln_post"""     ,						"""vision_model.post_layernorm"""      )
					# projection layers
					if "visual.proj" in name:
										_lowerCAmelCase      =   name.replace("""visual.proj"""     ,						"""visual_projection.weight"""      )
					if "text_projection" in name:
										_lowerCAmelCase      =   name.replace("""text_projection"""     ,						"""text_projection.weight"""      )
					# decoder
					if "trans_conv" in name:
										_lowerCAmelCase      =   name.replace("""trans_conv"""     ,						"""transposed_convolution"""      )
					if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
										_lowerCAmelCase      =   """decoder.""" + name
					if "blocks" in name:
										_lowerCAmelCase      =   name.replace("""blocks"""     ,						"""decoder.layers"""      )
					if "linear1" in name:
										_lowerCAmelCase      =   name.replace("""linear1"""     ,						"""mlp.fc1"""      )
					if "linear2" in name:
										_lowerCAmelCase      =   name.replace("""linear2"""     ,						"""mlp.fc2"""      )
					if "norm1" in name and "layer_" not in name:
										_lowerCAmelCase      =   name.replace("""norm1"""     ,						"""layer_norm1"""      )
					if "norm2" in name and "layer_" not in name:
										_lowerCAmelCase      =   name.replace("""norm2"""     ,						"""layer_norm2"""      )

					return name


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					for key in orig_state_dict.copy().keys():
										_lowerCAmelCase      =   orig_state_dict.pop(_A      )

										if key.startswith("""clip_model"""      ) and "attn.in_proj" in key:
															_lowerCAmelCase      =   key.split("""."""      )
															if "visual" in key:
																				_lowerCAmelCase      =   int(key_split[4]      )
																				_lowerCAmelCase      =   config.vision_config.hidden_size
																				_lowerCAmelCase      =   """vision_model"""
															else:
																				_lowerCAmelCase      =   int(key_split[3]      )
																				_lowerCAmelCase      =   config.text_config.hidden_size
																				_lowerCAmelCase      =   """text_model"""

															if "weight" in key:
																				_lowerCAmelCase      =   val[:dim, :]
																				_lowerCAmelCase      =   val[
																				    dim : dim * 2, :
																				]
																				_lowerCAmelCase      =   val[-dim:, :]
															else:
																				_lowerCAmelCase      =   val[:dim]
																				_lowerCAmelCase      =   val[dim : dim * 2]
																				_lowerCAmelCase      =   val[-dim:]
										elif "self_attn" in key and "out_proj" not in key:
															_lowerCAmelCase      =   key.split("""."""      )
															_lowerCAmelCase      =   int(key_split[1]      )
															_lowerCAmelCase      =   config.reduce_dim
															if "weight" in key:
																				_lowerCAmelCase      =   val[:dim, :]
																				_lowerCAmelCase      =   val[dim : dim * 2, :]
																				_lowerCAmelCase      =   val[-dim:, :]
															else:
																				_lowerCAmelCase      =   val[:dim]
																				_lowerCAmelCase      =   val[dim : dim * 2]
																				_lowerCAmelCase      =   val[-dim:]
										else:
															_lowerCAmelCase      =   rename_key(_A      )
															if "visual_projection" in new_name or "text_projection" in new_name:
																				_lowerCAmelCase      =   val.T
															_lowerCAmelCase      =   val

					return orig_state_dict


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
					_lowerCAmelCase      =   Image.open(requests.get(_A     ,						stream=_A      ).raw      )
					return image


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   get_clipseg_config(_A      )
					_lowerCAmelCase      =   CLIPSegForImageSegmentation(_A      )
					model.eval()

					_lowerCAmelCase      =   torch.load(_A     ,						map_location="""cpu"""      )

					# remove some keys
					for key in state_dict.copy().keys():
										if key.startswith("""model"""      ):
															state_dict.pop(_A     ,						_A      )

    # rename some keys
					_lowerCAmelCase      =   convert_state_dict(_A     ,						_A      )
					_lowerCAmelCase     ,     _lowerCAmelCase      =   model.load_state_dict(_A     ,						strict=_A      )

					if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
										raise ValueError("""Missing keys that are not expected: {}""".format(_A      )      )
					if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
										raise ValueError(F'Unexpected keys: {unexpected_keys}'      )

					_lowerCAmelCase      =   ViTImageProcessor(size=3_52      )
					_lowerCAmelCase      =   CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""      )
					_lowerCAmelCase      =   CLIPSegProcessor(image_processor=_A     ,						tokenizer=_A      )

					_lowerCAmelCase      =   prepare_img()
					_lowerCAmelCase      =   ["""a glass""", """something to fill""", """wood""", """a jar"""]

					_lowerCAmelCase      =   processor(text=_A     ,						images=[image] * len(_A      )     ,						padding="""max_length"""     ,						return_tensors="""pt"""      )

					with torch.no_grad():
										_lowerCAmelCase      =   model(**_A      )

					# verify values
					_lowerCAmelCase      =   torch.tensor([0.1_110, -0.1_882, 0.1_645]      )
					_lowerCAmelCase      =   torch.tensor([0.2_692, -0.7_197, -0.1_328]      )
					if model_name == "clipseg-rd64-refined":
										_lowerCAmelCase      =   torch.tensor(
										    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]      )
					elif model_name == "clipseg-rd64":
										_lowerCAmelCase      =   torch.tensor(
										    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]      )
					elif model_name == "clipseg-rd16":
										_lowerCAmelCase      =   torch.tensor(
										    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]      )
					else:
										raise ValueError(F'Model name {model_name} not supported.'      )

					assert torch.allclose(outputs.logits[0, :3, :3]     ,						_A     ,						atol=1E-3      )
					assert torch.allclose(outputs.conditional_embeddings[0, :3]     ,						_A     ,						atol=1E-3      )
					assert torch.allclose(outputs.pooled_output[0, :3]     ,						_A     ,						atol=1E-3      )
					print("""Looks ok!"""      )

					if pytorch_dump_folder_path is not None:
										print(F'Saving model and processor to {pytorch_dump_folder_path}'      )
										model.save_pretrained(_A      )
										processor.save_pretrained(_A      )

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


if __name__ == "__main__":
	A__     				=	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""clipseg-rd64""",
	    type=str,
	    choices=["""clipseg-rd16""", """clipseg-rd64""", """clipseg-rd64-refined"""],
	    help=(
	        """Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"""
	        """ reduce dimension)"""
	    ),
	)
	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth""",
	    type=str,
	    help=(
	        """Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"""
	        """ the decoder weights."""
	    ),
	)
	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."""
	)

	A__     				=	parser.parse_args()
	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A__     				=	logging.get_logger(__name__)





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    ['''input_values''', '''attention_mask''']




				def __init__(							self						,    _snake_case=1						,    _snake_case=16000						,    _snake_case=128						,    _snake_case=1024						,    _snake_case=0.0						,    _snake_case=True						,    _snake_case=-4.267_7393						,    _snake_case=4.568_9974						,    _snake_case=False						,    **_snake_case						,    ):



									"""simple docstring"""





									super().__init__(feature_size=_snake_case						,    sampling_rate=_snake_case						,    padding_value=_snake_case						,    **_snake_case		)
									_lowerCAmelCase      =   num_mel_bins
									_lowerCAmelCase      =   max_length
									_lowerCAmelCase      =   do_normalize
									_lowerCAmelCase      =   mean
									_lowerCAmelCase      =   std
									_lowerCAmelCase      =   return_attention_mask




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   torch.from_numpy(_snake_case		).unsqueeze(0		)
									_lowerCAmelCase      =   ta_kaldi.fbank(
									    _snake_case						,    htk_compat=_snake_case						,    sample_frequency=self.sampling_rate						,    use_energy=_snake_case						,    window_type="""hanning"""						,    num_mel_bins=self.num_mel_bins						,    dither=0.0						,    frame_shift=10						,    )

									_lowerCAmelCase      =   fbank.shape[0]
									_lowerCAmelCase      =   max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
														_lowerCAmelCase      =   torch.nn.ZeroPadad((0, 0, 0, difference)		)
														_lowerCAmelCase      =   pad_module(_snake_case		)
									elif difference < 0:
														_lowerCAmelCase      =   fbank[0:max_length, :]

									_lowerCAmelCase      =   fbank.numpy()

									return fbank




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									return (input_values - (self.mean)) / (self.std * 2)




				def __call__(							self						,    _snake_case						,    _snake_case = None						,    _snake_case = None						,    **_snake_case						,    ):



									"""simple docstring"""





									if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																			    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																			    F' {self.sampling_rate} and not {sampling_rate}.'		)
									else:
														logger.warning(
														    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
														    """Failing to do so can result in silent errors that might be hard to debug."""		)

									_lowerCAmelCase      =   isinstance(_snake_case						,    np.ndarray		) and len(raw_speech.shape		) > 1
									if is_batched_numpy and len(raw_speech.shape		) > 2:
														raise ValueError(F'Only mono-channel audio is supported for input to {self}'		)
									_lowerCAmelCase      =   is_batched_numpy or (
									    isinstance(_snake_case						,    (list, tuple)		) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)		))
									)

									if is_batched:
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for speech in raw_speech]
									elif not is_batched and not isinstance(_snake_case						,    np.ndarray		):
														_lowerCAmelCase      =   np.asarray(_snake_case						,    dtype=np.floataa		)
									elif isinstance(_snake_case						,    np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
														_lowerCAmelCase      =   raw_speech.astype(np.floataa		)

									# always return batch
									if not is_batched:
														_lowerCAmelCase      =   [raw_speech]

									# extract fbank features and pad/truncate to max_length
									_lowerCAmelCase      =   [self._extract_fbank_features(_snake_case						,    max_length=self.max_length		) for waveform in raw_speech]

									# convert into BatchFeature
									_lowerCAmelCase      =   BatchFeature({"""input_values""": features}		)

									# make sure list is in array format
									_lowerCAmelCase      =   padded_inputs.get("""input_values"""		)
									if isinstance(input_values[0]						,    _snake_case		):
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for feature in input_values]

									# normalization
									if self.do_normalize:
														_lowerCAmelCase      =   [self.normalize(_snake_case		) for feature in input_values]

									if return_tensors is not None:
														_lowerCAmelCase      =   padded_inputs.convert_to_tensors(_snake_case		)

									return padded_inputs



