
from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_UpperCAmelCase      :				str          = logging.get_logger(__name__)  # pylint: disable=invalid-name


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE     ):



		@register_to_config
		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None )						->      Any:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase						=  learnable

				if self.learnable:
						assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
						assert length is not None, "learnable=True requires `length` to be set"

						UpperCamelCase						=  torch.zeros(A_       ,   A_ )
				else:
						UpperCamelCase						=  None

				UpperCamelCase						=  torch.nn.Parameter(A_ )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : VQModel
		__lowercase : CLIPTextModel
		__lowercase : CLIPTokenizer
		__lowercase : TransformeraDModel
		__lowercase : LearnedClassifierFreeSamplingEmbeddings
		__lowercase : VQDiffusionScheduler



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__()

				self.register_modules(
				    vqvae=A_       ,   transformer=A_       ,   text_encoder=A_       ,   tokenizer=A_       ,   scheduler=A_       ,   learned_classifier_free_sampling_embeddings=A_       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  len(A_ ) if isinstance(A_       ,   A_ ) else 1

				# get prompt text embeddings
				UpperCamelCase						=  self.tokenizer(
				    A_       ,   padding='max_length'       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors='pt'       ,   )
				UpperCamelCase						=  text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						UpperCamelCase						=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
						logger.warning(
						    'The following part of your input was truncated because CLIP can only handle sequences up to'
						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
						UpperCamelCase						=  text_input_ids[:, : self.tokenizer.model_max_length]
				UpperCamelCase						=  self.text_encoder(text_input_ids.to(self.device ) )[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				UpperCamelCase						=  prompt_embeds / prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

				# duplicate text embeddings for each generation per prompt
				UpperCamelCase						=  prompt_embeds.repeat_interleave(A_       ,   dim=0 )

				if do_classifier_free_guidance:
						if self.learned_classifier_free_sampling_embeddings.learnable:
								UpperCamelCase						=  self.learned_classifier_free_sampling_embeddings.embeddings
								UpperCamelCase						=  negative_prompt_embeds.unsqueeze(0 ).repeat(A_       ,   1       ,   1 )
						else:
								UpperCamelCase						=  [''] * batch_size

								UpperCamelCase						=  text_input_ids.shape[-1]
								UpperCamelCase						=  self.tokenizer(
								    A_       ,   padding='max_length'       ,   max_length=A_       ,   truncation=A_       ,   return_tensors='pt'       ,   )
								UpperCamelCase						=  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
								# See comment for normalizing text embeddings
								UpperCamelCase						=  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase						=  negative_prompt_embeds.shape[1]
						UpperCamelCase						=  negative_prompt_embeds.repeat(1       ,   A_       ,   1 )
						UpperCamelCase						=  negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,   A_       ,   -1 )

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						UpperCamelCase						=  torch.cat([negative_prompt_embeds, prompt_embeds] )

				return prompt_embeds



		@torch.no_grad()
		def __call__(				self       ,   A_       ,   A_ = 100       ,   A_ = 5.0       ,   A_ = 1.0       ,   A_ = 1       ,   A_ = None       ,   A_ = None       ,   A_ = "pil"       ,   A_ = True       ,   A_ = None       ,   A_ = 1       ,   )						->      Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				if isinstance(A_       ,   A_ ):
						UpperCamelCase						=  1
				elif isinstance(A_       ,   A_ ):
						UpperCamelCase						=  len(A_ )
				else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_ )}''' )

				UpperCamelCase						=  batch_size * num_images_per_prompt

				UpperCamelCase						=  guidance_scale > 1.0

				UpperCamelCase						=  self._encode_prompt(A_       ,   A_       ,   A_ )

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(A_       ,   A_ ) or callback_steps <= 0)
				):
						raise ValueError(
						    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
						    F''' {type(A_ )}.''' )

				# get the initial completely masked latents unless the user supplied it

				UpperCamelCase						=  (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
						UpperCamelCase						=  self.transformer.num_vector_embeds - 1
						UpperCamelCase						=  torch.full(A_       ,   A_ ).to(self.device )
				else:
						if latents.shape != latents_shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
						if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
								raise ValueError(
								    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
								    F''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
						UpperCamelCase						=  latents.to(self.device )

				# set timesteps
				self.scheduler.set_timesteps(A_       ,   device=self.device )

				UpperCamelCase						=  self.scheduler.timesteps.to(self.device )

				UpperCamelCase						=  latents

				for i, t in enumerate(self.progress_bar(A_ ) ):
						# expand the sample if we are doing classifier free guidance
						UpperCamelCase						=  torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample

						# predict the un-noised image
						# model_output == `log_p_x_0`
						UpperCamelCase						=  self.transformer(A_       ,   encoder_hidden_states=A_       ,   timestep=A_ ).sample

						if do_classifier_free_guidance:
								UpperCamelCase			,						UpperCamelCase						=  model_output.chunk(2 )
								UpperCamelCase						=  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
								model_output -= torch.logsumexp(A_       ,   dim=1       ,   keepdim=A_ )

						UpperCamelCase						=  self.truncate(A_       ,   A_ )

						# remove `log(0)`'s (`-inf`s)
						UpperCamelCase						=  model_output.clamp(-70 )

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase						=  self.scheduler.step(A_       ,   timestep=A_       ,   sample=A_       ,   generator=A_ ).prev_sample

						# call the callback, if provided
						if callback is not None and i % callback_steps == 0:
								callback(A_       ,   A_       ,   A_ )

				UpperCamelCase						=  self.vqvae.config.vq_embed_dim
				UpperCamelCase						=  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				UpperCamelCase						=  self.vqvae.quantize.get_codebook_entry(A_       ,   shape=A_ )
				UpperCamelCase						=  self.vqvae.decode(A_       ,   force_not_quantize=A_ ).sample

				UpperCamelCase						=  (image / 2 + 0.5).clamp(0       ,   1 )
				UpperCamelCase						=  image.cpu().permute(0       ,   2       ,   3       ,   1 ).numpy()

				if output_type == "pil":
						UpperCamelCase						=  self.numpy_to_pil(A_ )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  torch.sort(A_       ,   1       ,   descending=A_ )
				UpperCamelCase						=  torch.exp(A_ )
				UpperCamelCase						=  sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				UpperCamelCase						=  torch.full_like(keep_mask[:, 0:1, :]       ,   A_ )
				UpperCamelCase						=  torch.cat((all_true, keep_mask)       ,   dim=1 )
				UpperCamelCase						=  keep_mask[:, :-1, :]

				UpperCamelCase						=  keep_mask.gather(1       ,   indices.argsort(1 ) )

				UpperCamelCase						=  log_p_x_0.clone()

				UpperCamelCase						=  -torch.inf  # -inf = log(0)

				return rv


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_UpperCAmelCase      :				Tuple          = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_UpperCAmelCase      :				Any          = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''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
		elif weight_type == "running_mean":
				UpperCamelCase						=  value
		elif weight_type == "running_var":
				UpperCamelCase						=  value
		elif weight_type == "num_batches_tracked":
				UpperCamelCase						=  value
		elif weight_type == "inv_freq":
				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				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  []
		UpperCamelCase						=  fairseq_model.state_dict()

		UpperCamelCase						=  hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase						=  'wav2vec2_conformer.' + 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 "pos_bias_u" in name:
												UpperCamelCase						=  None
										elif "pos_bias_v" in name:
												UpperCamelCase						=  None
										elif "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'
										elif "running_mean" in name:
												UpperCamelCase						=  'running_mean'
										elif "inv_freq" in name:
												UpperCamelCase						=  'inv_freq'
										elif "running_var" in name:
												UpperCamelCase						=  'running_var'
										elif "num_batches_tracked" in name:
												UpperCamelCase						=  'num_batches_tracked'
										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					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

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

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
						UpperCamelCase						=  value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
		else:
				unused_weights.append(lowercase				)


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=True				)     ->						int:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  WavaVecaConformerConfig.from_pretrained(lowercase					, hidden_act='swish'				)
		else:
				UpperCamelCase						=  WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				UpperCamelCase						=  'rotary'

		if is_finetuned:
				if dict_path:
						UpperCamelCase						=  Dictionary.load(lowercase				)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCamelCase						=  target_dict.pad_index
						UpperCamelCase						=  target_dict.bos_index
						UpperCamelCase						=  target_dict.eos_index
						UpperCamelCase						=  len(target_dict.symbols				)
						UpperCamelCase						=  os.path.join(lowercase					, 'vocab.json'				)
						if not os.path.isdir(lowercase				):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase				)				)
								return
						os.makedirs(lowercase					, exist_ok=lowercase				)
						UpperCamelCase						=  target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase						=  0
						UpperCamelCase						=  1
						with open(lowercase					, 'w'					, encoding='utf-8'				) as vocab_handle:
								json.dump(lowercase					, lowercase				)
						UpperCamelCase						=  WavaVecaCTCTokenizer(
						    lowercase					, unk_token=target_dict.unk_word					, pad_token=target_dict.pad_word					, bos_token=target_dict.bos_word					, eos_token=target_dict.eos_word					, word_delimiter_token='|'					, do_lower_case=lowercase					, )
						UpperCamelCase						=  True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase						=  WavaVecaFeatureExtractor(
						    feature_size=1					, sampling_rate=16_000					, padding_value=0					, do_normalize=lowercase					, return_attention_mask=lowercase					, )
						UpperCamelCase						=  WavaVecaProcessor(feature_extractor=lowercase					, tokenizer=lowercase				)
						processor.save_pretrained(lowercase				)

				UpperCamelCase						=  WavaVecaConformerForCTC(lowercase				)
		else:
				UpperCamelCase						=  WavaVecaConformerForPreTraining(lowercase				)

		if is_finetuned:
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]					, arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
		else:
				UpperCamelCase						=  argparse.Namespace(task='audio_pretraining'				)
				UpperCamelCase						=  fairseq.tasks.setup_task(lowercase				)

				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					, task=lowercase				)

		UpperCamelCase						=  model[0].eval()

		recursively_load_weights(lowercase					, lowercase					, not is_finetuned				)

		hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Tuple          = 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      :				Dict          = parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)
