
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()
_UpperCAmelCase   :					List[Any]    =			logging.get_logger(__name__)

_UpperCAmelCase   :					Optional[int]    =			{
    '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',
}
_UpperCAmelCase   :					int    =			[
    'lm_head',
]


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Optional[int]:
		'''simple docstring'''

		for attribute in key.split('.'					):
				UpperCamelCase            =	getattr(lowercase       ,			lowercase					)

		if weight_type is not None:
				UpperCamelCase            =	getattr(lowercase       ,			lowercase					).shape
		else:
				UpperCamelCase            =	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":
				UpperCamelCase            =	value
		elif weight_type == "weight_g":
				UpperCamelCase            =	value
		elif weight_type == "weight_v":
				UpperCamelCase            =	value
		elif weight_type == "bias":
				UpperCamelCase            =	value
		else:
				UpperCamelCase            =	value

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


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	[]
		UpperCamelCase            =	fairseq_model.state_dict()

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

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

		for name, value in fairseq_dict.items():
				UpperCamelCase            =	False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase       ,			lowercase       ,			lowercase       ,			lowercase       ,			)
						UpperCamelCase            =	True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    lowercase       ,			lowercase       ,			lowercase       ,			lowercase       ,			)
						UpperCamelCase            =	True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										UpperCamelCase            =	'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]:
										UpperCamelCase            =	True
										if "*" in mapped_key:
												UpperCamelCase            =	name.split(lowercase					)[0].split('.'					)[-2]
												UpperCamelCase            =	mapped_key.replace('*'       ,			lowercase					)
										if "weight_g" in name:
												UpperCamelCase            =	'weight_g'
										elif "weight_v" in name:
												UpperCamelCase            =	'weight_v'
										elif "bias" in name:
												UpperCamelCase            =	'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase            =	'weight'
										else:
												UpperCamelCase            =	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  A			(   lowercase       ,			lowercase					)  ->    List[str]:
		'''simple docstring'''

		UpperCamelCase            =	path.split('.'					)
		return reduce(lowercase       ,			lowercase       ,			lowercase					)


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		UpperCamelCase            =	access_by_string(lowercase       ,			lowercase					)
		UpperCamelCase            =	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.'''					)
		UpperCamelCase            =	fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''					)


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Tuple:
		'''simple docstring'''

		UpperCamelCase            =	full_name.split('conv_layers.'					)[-1]
		UpperCamelCase            =	name.split('.'					)
		UpperCamelCase            =	int(items[0]					)
		UpperCamelCase            =	int(items[1]					)

		UpperCamelCase            =	name.split('.'					)[-1]
		if type_id == 0:
				UpperCamelCase            =	'conv'
		elif type_id == 2:
				UpperCamelCase            =	'layer_norm'
		else:
				unused_weights.append(lowercase					)
				return

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


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	full_name.split('pos_conv.'					)[-1]
		UpperCamelCase            =	name.split('.'					)
		UpperCamelCase            =	int(items[0]					)
		UpperCamelCase            =	int(items[1]					)

		UpperCamelCase            =	name.split('.'					)[-1]
		if type_id != 0:
				unused_weights.append(lowercase					)
				return
		else:
				UpperCamelCase            =	'conv'

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


@torch.no_grad()
def  A			(   lowercase       ,			lowercase       ,			lowercase=None       ,			lowercase=None       ,			lowercase=True					)  ->    List[Any]:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase            =	DataaVecAudioConfig.from_pretrained(lowercase					)
		else:
				UpperCamelCase            =	DataaVecAudioConfig()

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

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

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

		UpperCamelCase            =	load_dataavec(lowercase					)

		recursively_load_weights(lowercase       ,			lowercase       ,			not is_finetuned					)

		UpperCamelCase            =	WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'					)

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

		UpperCamelCase            =	processor(lowercase       ,			return_tensors='pt'       ,			padding=lowercase					)

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

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

				UpperCamelCase            =	torch.argmax(lowercase       ,			dim=-1					)
				UpperCamelCase            =	processor.batch_decode(lowercase					)

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

		print(our_output.shape       ,			their_output.shape					)
		UpperCamelCase            =	torch.max(torch.abs(our_output - their_output					)					).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
		UpperCamelCase            =	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__":
					_UpperCAmelCase   :					Any    =			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"
					)
					_UpperCAmelCase   :					List[str]    =			parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)






import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def      __magic_name__							(					A						:   str,    A						:   List[str],    A						:   int  ):






			'''simple docstring'''







			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3,    dim=0  )
			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3,    dim=0  )

			a       =     pt_weights[F"""{prefix}.out_proj.weight"""]
			a       =     pt_weights[F"""{prefix}.out_proj.bias"""]

			a       =     q_proj
			a       =     q_proj_bias

			a       =     k_proj
			a       =     k_proj_bias

			a       =     v_proj
			a       =     v_proj_bias

			a       =     out_proj_weights
			a       =     out_proj_bias




def      __magic_name__							(					A						:   Optional[int],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_fc"""  )
			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_proj"""  )




def      __magic_name__							(					A						:   int,    A						:   Tuple,    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     pt_weights[F"""{prefix}.weight"""].data
			a       =     pt_weights[F"""{prefix}.bias"""].data




def      __magic_name__							(					A						:   List[Any],    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_1"""  )
			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_2"""  )

			# copy MLP
			copy_mlp(hf_layer.mlp,    A,    F"""{prefix}.mlp"""  )

			# copy attn
			copy_attn_layer(hf_layer.self_attn,    A,    F"""{prefix}.attn"""  )




def      __magic_name__							(					A						:   int,    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			for layer_id, hf_layer in enumerate(A  ):
						copy_layer(A,    A,    F"""{prefix}.{layer_id}"""  )




def      __magic_name__							(					A						:   Any,    A						:   Any  ):






			'''simple docstring'''







			a       =     pt_weights["text_projection"].data.T

			# copy text encoder
			for name, param in hf_model.text_model.named_parameters():
						a       =     pt_weights[F"""bert.{name}"""].data




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any]  ):






			'''simple docstring'''







			a       =     pt_weights["visual.proj"].data.T

			# copy layer norms
			copy_linear(hf_model.vision_model.pre_layrnorm,    A,    "visual.ln_pre"  )
			copy_linear(hf_model.vision_model.post_layernorm,    A,    "visual.ln_post"  )

			# copy embeddings
			a       =     pt_weights["visual.conv1.weight"].data
			a       =     pt_weights["visual.class_embedding"].data
			a       =     pt_weights["visual.positional_embedding"].data

			# copy encoder
			copy_layers(hf_model.vision_model.encoder.layers,    A,    "visual.transformer.resblocks"  )




@torch.no_grad()
def      __magic_name__							(					A						:   Optional[Any],    A						:   List[str],    A						:   Dict=None  ):






			'''simple docstring'''







			assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
			a       =     ChineseCLIPConfig.from_pretrained(A  )

			a       =     ChineseCLIPModel(A  ).eval()

			a       =     torch.load(A,    map_location="cpu"  )["state_dict"]
			a       =     {(name[7:] if name.startswith("module."  ) else name): value for name, value in pt_weights.items()}

			copy_text_model_and_projection(A,    A  )
			copy_vision_model_and_projection(A,    A  )
			a       =     pt_weights["logit_scale"].data

			hf_model.save_pretrained(A  )


if __name__ == "__main__":
		__lowerCAmelCase						:							Dict			     =						argparse.ArgumentParser()
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    help='Path to the output folder storing converted hf PyTorch model.',
		)
		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
		)
		parser.add_argument(
		    '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
		)
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

		convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
		print('The conversion is finished!')






