




import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_lowerCamelCase							:						Tuple							     =  logging.get_logger(__name__)

_lowerCamelCase							:						Tuple							     =  {
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_lowerCamelCase							:						str							     =  [
    'lm_head',
]






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for attribute in key.split("."  ):
				_snake_case     	=	getattr(lowercase_						,							lowercase_  )

		if weight_type is not None:
				_snake_case     	=	getattr(lowercase_						,							lowercase_  ).shape
		else:
				_snake_case     	=	hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''  )

		if weight_type == "weight":
				_snake_case     	=	value
		elif weight_type == "weight_g":
				_snake_case     	=	value
		elif weight_type == "weight_v":
				_snake_case     	=	value
		elif weight_type == "bias":
				_snake_case     	=	value
		else:
				_snake_case     	=	value

		logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]
		_snake_case     	=	fairseq_model.state_dict()

		if not is_headless:
				_snake_case     	=	hf_model.dataavec_audio.feature_extractor
				_snake_case     	=	hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				_snake_case     	=	hf_model.feature_extractor
				_snake_case     	=	hf_model.encoder.pos_conv_embed

		for name, value in fairseq_dict.items():
				_snake_case     	=	False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							)
						_snake_case     	=	True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							)
						_snake_case     	=	True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										_snake_case     	=	"data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."  )[-1] == name.split("."  )[0]:
										_snake_case     	=	True
										if "*" in mapped_key:
												_snake_case     	=	name.split(lowercase_  )[0].split("."  )[-2]
												_snake_case     	=	mapped_key.replace("*"						,							lowercase_  )
										if "weight_g" in name:
												_snake_case     	=	"weight_g"
										elif "weight_v" in name:
												_snake_case     	=	"weight_v"
										elif "bias" in name:
												_snake_case     	=	"bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_snake_case     	=	"weight"
										else:
												_snake_case     	=	None
										set_recursively(lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )
								continue
				if not is_used:
						unused_weights.append(lowercase_  )

		logger.warning(f'''Unused weights: {unused_weights}'''  )






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	path.split("."  )
		return reduce(lowercase_						,							lowercase_						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	access_by_string(lowercase_						,							lowercase_  )
		_snake_case     	=	hf_weight.data

		if fsq_value.shape != hf_value.shape:
				raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
		_snake_case     	=	fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	full_name.split("conv_layers."  )[-1]
		_snake_case     	=	name.split("."  )
		_snake_case     	=	int(items[0]  )
		_snake_case     	=	int(items[1]  )

		_snake_case     	=	name.split("."  )[-1]
		if type_id == 0:
				_snake_case     	=	"conv"
		elif type_id == 2:
				_snake_case     	=	"layer_norm"
		else:
				unused_weights.append(lowercase_  )
				return

		set_weights(lowercase_						,							lowercase_						,							lowercase_						,							f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	full_name.split("pos_conv."  )[-1]
		_snake_case     	=	name.split("."  )
		_snake_case     	=	int(items[0]  )
		_snake_case     	=	int(items[1]  )

		_snake_case     	=	name.split("."  )[-1]
		if type_id != 0:
				unused_weights.append(lowercase_  )
				return
		else:
				_snake_case     	=	"conv"

		set_weights(lowercase_						,							lowercase_						,							lowercase_						,							f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )






@torch.no_grad()
def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_=None						,							lowercase_=None						,							lowercase_=True  ):



		'''simple docstring'''







		if config_path is not None:
				_snake_case     	=	DataaVecAudioConfig.from_pretrained(lowercase_  )
		else:
				_snake_case     	=	DataaVecAudioConfig()

		if not is_finetuned:
				# Modify final_proj layer name
				_snake_case     	=	DataaVecAudioModel(lowercase_  )
				_snake_case     	=	os.path.dirname(lowercase_  )

				_snake_case     	=	torch.load(lowercase_  )
				_snake_case     	=	state_dict["model"].pop("final_proj.0.weight"  )
				_snake_case     	=	state_dict["model"].pop("final_proj.0.bias"  )
				_snake_case     	=	os.path.join(lowercase_						,							"converted.pt"  )
				torch.save(lowercase_						,							lowercase_  )
		else:
				_snake_case     	=	DataaVecAudioForCTC(lowercase_  )
				_snake_case     	=	checkpoint_path

		def load_dataavec(lowercase_  ):
				_snake_case	,   _snake_case	,   _snake_case     	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
				return model[0].eval()

		_snake_case     	=	load_dataavec(lowercase_  )

		recursively_load_weights(lowercase_						,							lowercase_						,							not is_finetuned  )

		_snake_case     	=	WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"  )

		_snake_case     	=	load_dataset("patrickvonplaten/librispeech_asr_dummy"						,							"clean"						,							split="validation"  )
		_snake_case     	=	[x["array"] for x in ds[:4]["audio"]]

		_snake_case     	=	processor(lowercase_						,							return_tensors="pt"						,							padding=lowercase_  )

		_snake_case     	=	inputs.input_values
		_snake_case     	=	inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

		hf_wavavec.eval()
		model.eval()
		if is_finetuned:
				_snake_case     	=	model(source=lowercase_						,							padding_mask=(1 - attention_mask)						,							mask=lowercase_						,							features_only=lowercase_  )[
				    "encoder_out"
				].transpose(0						,							1  )
				_snake_case     	=	hf_wavavec(lowercase_						,							attention_mask=lowercase_  )["logits"]

				_snake_case     	=	torch.argmax(lowercase_						,							dim=-1  )
				_snake_case     	=	processor.batch_decode(lowercase_  )

				print(f'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''  )
		else:
				_snake_case     	=	model(source=lowercase_						,							padding_mask=(1 - attention_mask)						,							mask=lowercase_						,							features_only=lowercase_  )[
				    "layer_results"
				][-1][0].transpose(0						,							1  )
				_snake_case     	=	hf_wavavec(lowercase_						,							attention_mask=lowercase_  )["last_hidden_state"]

		print(our_output.shape						,							their_output.shape  )
		_snake_case     	=	torch.max(torch.abs(our_output - their_output  )  ).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
		_snake_case     	=	torch.allclose(lowercase_						,							lowercase_						,							atol=1e-3  )
		print("Do both models output the same tensors?"						,							"🔥" if success else "💩"  )
		if not success:
				raise Exception("Something went wRoNg"  )

		hf_wavavec.save_pretrained(lowercase_  )

		if is_finetuned:
				processor.save_pretrained(lowercase_  )
		else:
				processor.feature_extractor.save_pretrained(lowercase_  )


if __name__ == "__main__":
						_lowerCamelCase							:						Optional[int]							     =  argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						_lowerCamelCase							:						Tuple							     =  parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)











'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CLIPSegTextConfig()
		_snake_case     	=	CLIPSegVisionConfig(patch_size=16  )

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

		_snake_case     	=	CLIPSegConfig.from_text_vision_configs(
		    lowercase_						,							lowercase_						,							use_complex_transposed_convolution=lowercase_						,							reduce_dim=lowercase_						,							)
		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







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

		return name






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				_snake_case     	=	orig_state_dict.pop(lowercase_  )

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

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

		return orig_state_dict






def 		_lowercase		(     ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	get_clipseg_config(lowercase_  )
		_snake_case     	=	CLIPSegForImageSegmentation(lowercase_  )
		model.eval()

		_snake_case     	=	torch.load(lowercase_						,							map_location="cpu"  )

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

    # rename some keys
		_snake_case     	=	convert_state_dict(lowercase_						,							lowercase_  )
		_snake_case	,   _snake_case     	=	model.load_state_dict(lowercase_						,							strict=lowercase_  )

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

		_snake_case     	=	ViTImageProcessor(size=352  )
		_snake_case     	=	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"  )
		_snake_case     	=	CLIPSegProcessor(image_processor=lowercase_						,							tokenizer=lowercase_  )

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

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

		with torch.no_grad():
				_snake_case     	=	model(**lowercase_  )

		# verify values
		_snake_case     	=	torch.tensor([0.1110, -0.1882, 0.1645]  )
		_snake_case     	=	torch.tensor([0.2692, -0.7197, -0.1328]  )
		if model_name == "clipseg-rd64-refined":
				_snake_case     	=	torch.tensor(
				    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]  )
		elif model_name == "clipseg-rd64":
				_snake_case     	=	torch.tensor(
				    [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]  )
		elif model_name == "clipseg-rd16":
				_snake_case     	=	torch.tensor(
				    [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]  )
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''  )

		assert torch.allclose(outputs.logits[0, :3, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.conditional_embeddings[0, :3]						,							lowercase_						,							atol=1e-3  )
		assert torch.allclose(outputs.pooled_output[0, :3]						,							lowercase_						,							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(lowercase_  )
				processor.save_pretrained(lowercase_  )

		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__":
						_lowerCamelCase							:						str							     =  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.'
						)

						_lowerCamelCase							:						Optional[int]							     =  parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





