




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__UpperCamelCase      : int					      =   {
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 24,
        "vision_width": 1_024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=""							,    __lowerCamelCase="/"					)   -> Dict:
	a		       =							[]

	for k, v in params.items():
		a		       =							parent_key + sep + k if parent_key else k

		if isinstance(__lowerCamelCase							,    collections.MutableMapping					):
			items.extend(flatten_nested_dict(__lowerCamelCase							,    __lowerCamelCase							,    sep=__lowerCamelCase					).items()					)
		else:
			items.append((new_key, v)					)
	return dict(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase					)   -> str:
	return jax.tree_util.tree_map(lambda __lowerCamelCase					: x.astype(jnp.floataa					) if x.dtype == jnp.bfloataa else x							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Any:
	a       ,				a       ,				a		       =							pt_attn_layer.in_proj_weight.chunk(3							,    dim=0					)
	a       ,				a       ,				a		       =							pt_attn_layer.in_proj_bias.chunk(3							,    dim=0					)

	a		       =							pt_attn_layer.out_proj.weight
	a		       =							pt_attn_layer.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 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	copy_linear(hf_mlp.fca							,    pt_mlp.c_fc					)
	copy_linear(hf_mlp.fca							,    pt_mlp.c_proj					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							pt_linear.weight
	a		       =							pt_linear.bias





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[str]:
	# copy layer norms
	copy_linear(hf_layer.layer_norma							,    pt_layer.ln_a					)
	copy_linear(hf_layer.layer_norma							,    pt_layer.ln_a					)

	# copy MLP
	copy_mlp(hf_layer.mlp							,    pt_layer.mlp					)

	# copy attn
	copy_attn_layer(hf_layer.self_attn							,    pt_layer.attn					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	for hf_layer, pt_layer in zip(__lowerCamelCase							,    __lowerCamelCase					):
		copy_layer(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	# copy  embeds
	a		       =							pt_model.token_embedding.weight
	a		       =							pt_model.positional_embedding

	# copy layer norm
	copy_linear(hf_encoder.final_layer_norm							,    pt_model.ln_final					)

	# copy hidden layers
	copy_layers(hf_encoder.encoder.layers							,    pt_model.transformer.resblocks					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	# copy projection
	a		       =							pt_model.text_projection.data.T

	# copy text encoder
	copy_encoder(hf_model.text_model							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	# copy projection
	a		       =							pt_model.visual.proj.data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layernorm							,    pt_model.visual.ln_pre					)
	copy_linear(hf_model.vision_model.post_layernorm							,    pt_model.visual.ln_post					)

	# copy embeds
	a		       =							pt_model.visual.conva.weight.data
	a		       =							pt_model.visual.class_embedding
	a		       =							pt_model.visual.positional_embedding.data

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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]					)

	a		       =							torch.from_numpy(flax_class_token_params["""scale"""]					)
	a		       =							torch.from_numpy(flax_class_token_params["""bias"""]					)
	a		       =							nn.Parameter(__lowerCamelCase					)
	a		       =							nn.Parameter(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	a		       =							hf_model.state_dict()
	a		       =							{}

	# Rename class prediction head flax params to pytorch HF
	a		       =							flatten_nested_dict(flax_params["""class_head"""]					)

	for flax_key, v in flax_class_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)
		a		       =							torch_key.replace("""Dense_0"""							,    """dense0"""					)
		a		       =							"""class_head.""" + torch_key

		if "weight" in torch_key and v.ndim == 2:
			a		       =							v.T

		a		       =							nn.Parameter(torch.from_numpy(__lowerCamelCase					)					)

	# Rename box prediction box flax params to pytorch HF
	a		       =							flatten_nested_dict(flax_params["""obj_box_head"""]					)

	for flax_key, v in flax_box_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)
		a		       =							torch_key.replace("""_"""							,    """"""					).lower()
		a		       =							"""box_head.""" + torch_key

		if "weight" in torch_key and v.ndim == 2:
			a		       =							v.T

		a		       =							nn.Parameter(torch.from_numpy(__lowerCamelCase					)					)

	# Copy flax params to PyTorch params
	for name, param in new_params.items():
		if name in pt_params.keys():
			pt_params[name].copy_(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Dict:
	for k, v in flax_attn_params.items():
		if k.startswith("""transformer"""					):
			a		       =							k.replace("""transformer.resblocks"""							,    """text_model.encoder.layers"""					)
		else:
			a		       =							k.replace("""visual.transformer.resblocks"""							,    """vision_model.encoder.layers"""					)

		a		       =							torch_key.replace("""attn"""							,    """self_attn"""					)
		a		       =							torch_key.replace("""key"""							,    """k_proj"""					)
		a		       =							torch_key.replace("""value"""							,    """v_proj"""					)
		a		       =							torch_key.replace("""query"""							,    """q_proj"""					)
		a		       =							torch_key.replace("""out"""							,    """out_proj"""					)

		if "bias" in torch_key and v.ndim == 2:
			a		       =							v.shape[0] * v.shape[1]
			a		       =							v.reshape(__lowerCamelCase					)

		if "weight" in torch_key and "out" in torch_key:
			a		       =							(v.shape[0] * v.shape[1], v.shape[2])
			a		       =							v.reshape(__lowerCamelCase					).T

		if "weight" in torch_key and "out" not in torch_key:
			a		       =							(v.shape[0], v.shape[1] * v.shape[2])
			a		       =							v.reshape(__lowerCamelCase					).T

		# Copy flax CLIP attn params to HF PyTorch params
		a		       =							torch.from_numpy(__lowerCamelCase					)
		hf_backbone.state_dict()[torch_key].copy_(__lowerCamelCase					)





def 	__A	(			__lowerCamelCase					)   -> Any:
	a		       =							{}
	a		       =							[]

	for k, v in params.items():
		if "attn." in k:
			a		       =							k[: k.rindex("""attn."""					) + 5]
			if base in processed_attn_layers:
				continue

			processed_attn_layers.append(__lowerCamelCase					)
			a		       =							params[base + """out.weight"""].shape[-1]
			a		       =							params[base + """out.weight"""].reshape(__lowerCamelCase							,    __lowerCamelCase					).T
			a		       =							params[base + """out.bias"""]
		else:
			a		       =							v
	return new_params





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							CLIP(**__lowerCamelCase					)
	torch_model.eval()
	a		       =							torch_model.state_dict()

	a		       =							flatten_nested_dict(flax_params["""backbone"""]["""clip"""]					)
	a		       =							{}

	for flax_key, v in flax_clip_params.items():
		a		       =							flax_key.replace("""/"""							,    """."""					)
		a		       =							torch_key.replace("""text.token_embedding.embedding"""							,    """token_embedding.kernel"""					)

		if (
		    torch_key.startswith("""text.transformer"""					)
		    or torch_key.startswith("""text.text_projection"""					)
		    or torch_key.startswith("""text.ln_final"""					)
		    or torch_key.startswith("""text.positional_embedding"""					)
		):
			a		       =							torch_key[5:]

		a		       =							torch_key.replace("""text_projection.kernel"""							,    """text_projection"""					)
		a		       =							torch_key.replace("""visual.proj.kernel"""							,    """visual.proj"""					)
		a		       =							torch_key.replace(""".scale"""							,    """.weight"""					)
		a		       =							torch_key.replace(""".kernel"""							,    """.weight"""					)

		if "conv" in torch_key or "downsample.0.weight" in torch_key:
			a		       =							v.transpose(3							,    2							,    0							,    1					)

		elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
			# Fully connected layers are transposed, embeddings are not
			a		       =							v.T

		a		       =							v

	a		       =							_convert_attn_layers(__lowerCamelCase					)
	new_torch_params.update(__lowerCamelCase					)
	a		       =							{}

	# Copy flax CLIP backbone params to PyTorch params
	for name, param in new_torch_params.items():
		if name in torch_clip_params.keys():
			a		       =							torch.from_numpy(new_torch_params[name]					)
			torch_clip_params[name].copy_(__lowerCamelCase					)
		else:
			a		       =							param

	return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=None					)   -> Optional[int]:
	a		       =							Repository(__lowerCamelCase							,    clone_from=f'google/{pytorch_dump_folder_path}'					)
	repo.git_pull()

	if config_path is not None:
		a		       =							OwlViTConfig.from_pretrained(__lowerCamelCase					)
	else:
		a		       =							OwlViTConfig()

	a		       =							OwlViTModel(__lowerCamelCase					).eval()
	a		       =							OwlViTForObjectDetection(__lowerCamelCase					).eval()

	copy_text_model_and_projection(__lowerCamelCase							,    __lowerCamelCase					)
	copy_vision_model_and_projection(__lowerCamelCase							,    __lowerCamelCase					)
	a		       =							pt_backbone.logit_scale
	copy_flax_attn_params(__lowerCamelCase							,    __lowerCamelCase					)

	a		       =							hf_backbone
	copy_class_merge_token(__lowerCamelCase							,    __lowerCamelCase					)
	copy_class_box_heads(__lowerCamelCase							,    __lowerCamelCase					)

	# Save HF model
	hf_model.save_pretrained(repo.local_dir					)

	# Initialize image processor
	a		       =							OwlViTImageProcessor(
	    size=config.vision_config.image_size							,    crop_size=config.vision_config.image_size					)
	# Initialize tokenizer
	a		       =							CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""							,    pad_token="""!"""							,    model_max_length=16					)

	# Initialize processor
	a		       =							OwlViTProcessor(image_processor=__lowerCamelCase							,    tokenizer=__lowerCamelCase					)
	image_processor.save_pretrained(repo.local_dir					)
	processor.save_pretrained(repo.local_dir					)

	repo.git_add()
	repo.git_commit("""Upload model and processor"""					)
	repo.git_push()


if __name__ == "__main__":
		__UpperCamelCase      : Union[str, Any]					      =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--owlvit_version",
		    default=None,
		    type=str,
		    required=True,
		    help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
		)
		parser.add_argument(
		    "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
		)
		parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
		parser.add_argument(
		    "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
		)
		__UpperCamelCase      : List[Any]					      =   parser.parse_args()

		# Initialize PyToch clip model
		__UpperCamelCase      : Dict					      =   args.owlvit_version
		if model_name == "clip_b16":
				__UpperCamelCase      : Optional[int]					      =   CONFIGS["vit_b16"]
		elif model_name == "clip_b32":
				__UpperCamelCase      : List[str]					      =   CONFIGS["vit_b32"]
		elif model_name == "clip_l14":
				__UpperCamelCase      : Tuple					      =   CONFIGS["vit_l14"]

		# Load from checkpoint and convert params to float-32
		__UpperCamelCase      : List[Any]					      =   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
		__UpperCamelCase      : Any					      =   jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
		del variables

		# Convert CLIP backbone
		__UpperCamelCase      ,						__UpperCamelCase      ,						__UpperCamelCase      : Tuple					      =   convert_clip_backbone(flax_params, torch_config)

		convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)







import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 	__lowerCAmelCase			:
							def __init__(		self		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							cuda
								a		       =							AutoConfig.from_pretrained(__magic_name__       )
								a		       =							AutoModelForCTC.from_pretrained(__magic_name__       )
								self.model.eval()
								if self.cuda:
									self.model.to(device="""cuda"""       )
								a		       =							AutoProcessor.from_pretrained(__magic_name__       )
								a		       =							torchaudio.transforms.Resample(__magic_name__	,   1_6000       )
								a		       =							0
								a		       =							list(self.processor.tokenizer.get_vocab().keys()       )
								for i in range(len(__magic_name__       )       ):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										a		       =							i
								print("""Blank Token id [PAD]/<pad>"""	,   __magic_name__       )
								a		       =							blank_id
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a       ,				a		       =							torchaudio.load(__magic_name__       )
								a		       =							self.resampler(__magic_name__       ).squeeze().numpy()
								return speech
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a		       =							self.blank_id
								a		       =							"""|""".join(item["""sent"""].split(""" """       )       )
								if not os.path.isfile(item["""wav_path"""]       ):
									print(item["""wav_path"""]	,   """not found in wavs directory"""       )

								a		       =							self.speech_file_to_array_fn(item["""wav_path"""]       )
								a		       =							self.processor(__magic_name__	,   sampling_rate=1_6000	,   return_tensors="""pt"""	,   padding=__magic_name__       )
								if self.cuda:
									a		       =							inputs.to(device="""cuda"""       )

								with torch.no_grad():
									a		       =							self.model(inputs.input_values       ).logits

								# get the emission probability at frame level
								a		       =							torch.log_softmax(__magic_name__	,   dim=-1       )
								a		       =							emissions[0].cpu().detach()

								# get labels from vocab
								a		       =							([""""""] + list(self.processor.tokenizer.get_vocab().keys()       ))[
								    :-1
								]  # logits don't align with the tokenizer's vocab

								a		       =							{c: i for i, c in enumerate(__magic_name__       )}
								a		       =							[]
								for c in transcript:
									if c in dictionary:
										tokens.append(dictionary[c]       )

								def get_trellis(__magic_name__		:Optional[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:Any=0       ):
									a		       =							emission.size(0       )
									a		       =							len(__magic_name__       )

									# Trellis has extra diemsions for both time axis and tokens.
									# The extra dim for tokens represents <SoS> (start-of-sentence)
									# The extra dim for time axis is for simplification of the code.
									a		       =							torch.full((num_frames + 1, num_tokens + 1)	,   -float("""inf"""       )       )
									a		       =							0
									for t in range(__magic_name__       ):
										a		       =							torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]	,   trellis[t, :-1] + emission[t, tokens]	,   )
									return trellis

								a		       =							get_trellis(__magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42

								def backtrack(__magic_name__		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:int	,   __magic_name__		:str=0       ):
									a		       =							trellis.size(1       ) - 1
									a		       =							torch.argmax(trellis[:, j]       ).item()

									a		       =							[]
									for t in range(__magic_name__	,   0	,   -1       ):
										# 1. Figure out if the current position was stay or change
										# Note (again):
										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
										# Score for token staying the same from time frame J-1 to T.
										a		       =							trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										a		       =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										a		       =							emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
										# Return token index and time index in non-trellis coordinate.
										path.append(Point(j - 1	,   t - 1	,   __magic_name__       )       )

										# 3. Update the token
										if changed > stayed:
											j -= 1
											if j == 0:
												break
									else:
										raise ValueError("""Failed to align"""       )
									return path[::-1]

								a		       =							backtrack(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															def __repr__(		self		:Union[str, Any]       ):







																'''simple docstring'''







																return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'





															@property
															def    lowerCamelCase__							(		self		:int       ):







																'''simple docstring'''







																return self.end - self.start

								def merge_repeats(__magic_name__		:int       ):
									a       ,				a		       =							0, 0
									a		       =							[]
									while ia < len(__magic_name__       ):
										while ia < len(__magic_name__       ) and path[ia].token_index == path[ia].token_index:
											ia += 1
										a		       =							sum(path[k].score for k in range(__magic_name__	,   __magic_name__       )       ) / (ia - ia)
										segments.append(
										    Segment(
										        transcript[path[ia].token_index]	,   path[ia].time_index	,   path[ia - 1].time_index + 1	,   __magic_name__	,   )       )
										a		       =							ia
									return segments

								a		       =							merge_repeats(__magic_name__       )
								with open(item["""out_path"""]	,   """w"""       ) as out_align:
									for seg in segments:
										out_align.write(str(__magic_name__       ) + """\n"""       )





							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:str	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







								if not os.path.exists(__magic_name__       ):
									os.makedirs(__magic_name__       )

								# load text file
								a		       =							open(__magic_name__	,   encoding="""utf8"""       ).readlines()

								a		       =							[]
								for line in lines:
									if len(line.strip().split("""\t"""       )       ) != 2:
										print("""Script must be in format: 00001  this is my sentence"""       )
										exit()

									a       ,				a		       =							line.strip().split("""\t"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.wav"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.txt"""       )

									items.append({"""sent""": sentence, """wav_path""": wav_path, """out_path""": out_path}       )
								print("""Number of samples found in script file"""	,   len(__magic_name__       )       )

								for item in tqdm(__magic_name__       ):
									self.align_single_sample(__magic_name__       )







def 	__A	(			)   -> Union[str, Any]:
	a		       =							argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name"""							,    type=__lowerCamelCase							,    default="""arijitx/wav2vec2-xls-r-300m-bengali"""							,    help="""wav2vec model name"""					)
	parser.add_argument("""--wav_dir"""							,    type=__lowerCamelCase							,    default="""./wavs"""							,    help="""directory containing wavs"""					)
	parser.add_argument("""--text_file"""							,    type=__lowerCamelCase							,    default="""script.txt"""							,    help="""file containing text"""					)
	parser.add_argument("""--input_wavs_sr"""							,    type=__lowerCamelCase							,    default=1_6000							,    help="""sampling rate of input audios"""					)
	parser.add_argument(
	    """--output_dir"""							,    type=__lowerCamelCase							,    default="""./out_alignment"""							,    help="""output directory containing the alignment files"""					)
	parser.add_argument("""--cuda"""							,    action="""store_true"""					)

	a		       =							parser.parse_args()

	a		       =							WavaVecaAligner(args.model_name							,    args.input_wavs_sr							,    args.cuda					)
	aligner.align_data(args.wav_dir							,    args.text_file							,    args.output_dir					)


if __name__ == "__main__":
		main()

