





"""simple docstring"""





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					      =   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__ ):
				"""simple docstring"""




				@register_to_config
				def __init__(  self	:Dict    ,     __lowercase	:Optional[int]    ,     __lowercase	:Optional[Any] = None    ,     __lowercase	:Any = None      ):
											super().__init__()

											__lowerCamelCase   :   Any       =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"

																		__lowerCamelCase   :   Union[str, Any]       =torch.zeros(UpperCamelCase_    ,     UpperCamelCase_      )
											else:
																		__lowerCamelCase   :   Dict       =None

											__lowerCamelCase   :   List[Any]       =torch.nn.Parameter(UpperCamelCase_      )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							VQModel
				__snake_case :							CLIPTextModel
				__snake_case :							CLIPTokenizer
				__snake_case :							TransformeraDModel
				__snake_case :							LearnedClassifierFreeSamplingEmbeddings
				__snake_case :							VQDiffusionScheduler




				def __init__(  self	:str    ,     __lowercase	:List[Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Any    ,     __lowercase	:int    ,     __lowercase	:Any    ,     __lowercase	:int    ,     ):
											super().__init__()

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




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:Any    ,     __lowercase	:Optional[int]    ,     __lowercase	:str      ):
											__lowerCamelCase   :   str       =len(UpperCamelCase_      ) if isinstance(UpperCamelCase_    ,     UpperCamelCase_      ) else 1

											# get prompt text embeddings
											__lowerCamelCase   :   str       =self.tokenizer(
											    UpperCamelCase_    ,     padding='''max_length'''    ,     max_length=self.tokenizer.model_max_length    ,     return_tensors='''pt'''    ,     )
											__lowerCamelCase   :   int       =text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		__lowerCamelCase   :   Dict       =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}'      )
																		__lowerCamelCase   :   List[str]       =text_input_ids[:, : self.tokenizer.model_max_length]
											__lowerCamelCase   :   Dict       =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
											__lowerCamelCase   :   List[str]       =prompt_embeds / prompt_embeds.norm(dim=-1    ,     keepdim=UpperCamelCase_      )

											# duplicate text embeddings for each generation per prompt
											__lowerCamelCase   :   int       =prompt_embeds.repeat_interleave(UpperCamelCase_    ,     dim=0      )

											if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																									__lowerCamelCase   :   str       =self.learned_classifier_free_sampling_embeddings.embeddings
																									__lowerCamelCase   :   Optional[Any]       =negative_prompt_embeds.unsqueeze(0      ).repeat(UpperCamelCase_    ,     1    ,     1      )
																		else:
																									__lowerCamelCase   :   Tuple       =[''] * batch_size

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

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		__lowerCamelCase   :   List[str]       =negative_prompt_embeds.shape[1]
																		__lowerCamelCase   :   int       =negative_prompt_embeds.repeat(1    ,     UpperCamelCase_    ,     1      )
																		__lowerCamelCase   :   Tuple       =negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,     UpperCamelCase_    ,     -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
																		__lowerCamelCase   :   Dict       =torch.cat([negative_prompt_embeds, prompt_embeds]      )

											return prompt_embeds




				@torch.no_grad()
				def __call__(  self	:Dict    ,     __lowercase	:str    ,     __lowercase	:Tuple = 100    ,     __lowercase	:Tuple = 5.0    ,     __lowercase	:Optional[Any] = 1.0    ,     __lowercase	:str = 1    ,     __lowercase	:List[str] = None    ,     __lowercase	:List[Any] = None    ,     __lowercase	:Optional[int] = "pil"    ,     __lowercase	:Optional[int] = True    ,     __lowercase	:str = None    ,     __lowercase	:Tuple = 1    ,     ):
											if isinstance(UpperCamelCase_    ,     UpperCamelCase_      ):
																		__lowerCamelCase   :   Union[str, Any]       =1
											elif isinstance(UpperCamelCase_    ,     UpperCamelCase_      ):
																		__lowerCamelCase   :   List[str]       =len(UpperCamelCase_      )
											else:
																		raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_      )}'      )

											__lowerCamelCase   :   List[Any]       =batch_size * num_images_per_prompt

											__lowerCamelCase   :   Any       =guidance_scale > 1.0

											__lowerCamelCase   :   str       =self._encode_prompt(UpperCamelCase_    ,     UpperCamelCase_    ,     UpperCamelCase_      )

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

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

											__lowerCamelCase   :   Dict       =(batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		__lowerCamelCase   :   List[str]       =self.transformer.num_vector_embeds - 1
																		__lowerCamelCase   :   Any       =torch.full(UpperCamelCase_    ,     UpperCamelCase_      ).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).'      )
																		__lowerCamelCase   :   str       =latents.to(self.device      )

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

											__lowerCamelCase   :   Optional[Any]       =self.scheduler.timesteps.to(self.device      )

											__lowerCamelCase   :   List[str]       =latents

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

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		__lowerCamelCase   :   Optional[int]       =self.transformer(UpperCamelCase_    ,     encoder_hidden_states=UpperCamelCase_    ,     timestep=UpperCamelCase_      ).sample

																		if do_classifier_free_guidance:
																									__lowerCamelCase   :   Union[str, Any]       =model_output.chunk(2      )
																									__lowerCamelCase   :   Dict       =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																									model_output -= torch.logsumexp(UpperCamelCase_    ,     dim=1    ,     keepdim=UpperCamelCase_      )

																		__lowerCamelCase   :   Union[str, Any]       =self.truncate(UpperCamelCase_    ,     UpperCamelCase_      )

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

																		# compute the previous noisy sample x_t -> x_t-1
																		__lowerCamelCase   :   List[str]       =self.scheduler.step(UpperCamelCase_    ,     timestep=UpperCamelCase_    ,     sample=UpperCamelCase_    ,     generator=UpperCamelCase_      ).prev_sample

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

											__lowerCamelCase   :   str       =self.vqvae.config.vq_embed_dim
											__lowerCamelCase   :   Optional[Any]       =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
											__lowerCamelCase   :   Optional[int]       =self.vqvae.quantize.get_codebook_entry(UpperCamelCase_    ,     shape=UpperCamelCase_      )
											__lowerCamelCase   :   Tuple       =self.vqvae.decode(UpperCamelCase_    ,     force_not_quantize=UpperCamelCase_      ).sample

											__lowerCamelCase   :   Union[str, Any]       =(image / 2 + 0.5).clamp(0    ,     1      )
											__lowerCamelCase   :   Union[str, Any]       =image.cpu().permute(0    ,     2    ,     3    ,     1      ).numpy()

											if output_type == "pil":
																		__lowerCamelCase   :   List[Any]       =self.numpy_to_pil(UpperCamelCase_      )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=UpperCamelCase_      )




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:List[str]    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   Optional[int]       =torch.sort(UpperCamelCase_    ,     1    ,     descending=UpperCamelCase_      )
											__lowerCamelCase   :   Optional[Any]       =torch.exp(UpperCamelCase_      )
											__lowerCamelCase   :   Union[str, Any]       =sorted_p_x_0.cumsum(dim=1      ) < truncation_rate

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

											__lowerCamelCase   :   Union[str, Any]       =keep_mask.gather(1    ,     indices.argsort(1      )      )

											__lowerCamelCase   :   List[str]       =log_p_x_0.clone()

											__lowerCamelCase   :   Optional[Any]       =-torch.inf  # -inf = log(0)

											return rv






"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,encoder_layers=args.encoder_layers    ,decoder_layers=args.decoder_layers    ,encoder_attention_heads=args.encoder_attention_heads    ,decoder_attention_heads=args.decoder_attention_heads    ,encoder_ffn_dim=args.encoder_ffn_embed_dim    ,decoder_ffn_dim=args.decoder_ffn_embed_dim    ,d_model=args.encoder_embed_dim    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

