

import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
		raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
		raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
A											=     logging.get_logger(__name__)

A											=     'The Nymphenburg Palace is a beautiful palace in Munich!'





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= {
	    'attention_cell': 'multi_head',
	    'num_layers': 4,
	    'units': 1024,
	    'hidden_size': 768,
	    'max_length': 512,
	    'num_heads': 8,
	    'scaled': True,
	    'dropout': 0.1,
	    'use_residual': True,
	    'embed_size': 1024,
	    'embed_dropout': 0.1,
	    'word_embed': None,
	    'layer_norm_eps': 1e-5,
	    'token_type_vocab_size': 2,
	}

	snake_case_      		= bort_4_8_768_1024_hparams

	# Let's construct the original Bort model here
	# Taken from official BERT implementation, see:
	# https://github.com/alexa/bort/blob/master/bort/bort.py
	snake_case_      		= BERTEncoder(
	    attention_cell=predefined_args['attention_cell']			, num_layers=predefined_args['num_layers']			, units=predefined_args['units']			, hidden_size=predefined_args['hidden_size']			, max_length=predefined_args['max_length']			, num_heads=predefined_args['num_heads']			, scaled=predefined_args['scaled']			, dropout=predefined_args['dropout']			, output_attention=lowercase__			, output_all_encodings=lowercase__			, use_residual=predefined_args['use_residual']			, activation=predefined_args.get('activation'			, 'gelu'	)			, layer_norm_eps=predefined_args.get('layer_norm_eps'			, lowercase__	)			, )

	# Vocab information needs to be fetched first
	# It's the same as RoBERTa, so RobertaTokenizer can be used later
	snake_case_      		= 'openwebtext_ccnews_stories_books_cased'

	# Specify download folder to Gluonnlp's vocab
	snake_case_      		= os.path.join(get_home_dir()			, 'models'	)
	snake_case_      		= _load_vocab(lowercase__			, lowercase__			, lowercase__			, cls=lowercase__	)

	snake_case_      		= nlp.model.BERTModel(
	    lowercase__			, len(lowercase__	)			, units=predefined_args['units']			, embed_size=predefined_args['embed_size']			, embed_dropout=predefined_args['embed_dropout']			, word_embed=predefined_args['word_embed']			, use_pooler=lowercase__			, use_token_type_embed=lowercase__			, token_type_vocab_size=predefined_args['token_type_vocab_size']			, use_classifier=lowercase__			, use_decoder=lowercase__			, )

	original_bort.load_parameters(lowercase__			, cast_dtype=lowercase__			, ignore_extra=lowercase__	)
	snake_case_      		= original_bort._collect_params_with_prefix()

	# Build our config 🤗
	snake_case_      		= {
	    'architectures': ['BertForMaskedLM'],
	    'attention_probs_dropout_prob': predefined_args['dropout'],
	    'hidden_act': 'gelu',
	    'hidden_dropout_prob': predefined_args['dropout'],
	    'hidden_size': predefined_args['embed_size'],
	    'initializer_range': 0.02,
	    'intermediate_size': predefined_args['hidden_size'],
	    'layer_norm_eps': predefined_args['layer_norm_eps'],
	    'max_position_embeddings': predefined_args['max_length'],
	    'model_type': 'bort',
	    'num_attention_heads': predefined_args['num_heads'],
	    'num_hidden_layers': predefined_args['num_layers'],
	    'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
	    'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
	    'vocab_size': len(lowercase__	),
	}

	snake_case_      		= BertConfig.from_dict(lowercase__	)
	snake_case_      		= BertForMaskedLM(lowercase__	)
	hf_bort_model.eval()

	# Parameter mapping table (Gluonnlp to Transformers)
	# * denotes layer index
	#
	# | Gluon Parameter                                                | Transformers Parameter
	# | -------------------------------------------------------------- | ----------------------
	# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
	# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
	# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
	# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
	# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
	# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
	# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
	# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
	# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
	# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
	# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
	# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
	# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
	# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

	# Helper function to convert MXNET Arrays to PyTorch
	def to_torch(lowercase__	) -> nn.Parameter:
		return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

	# Check param shapes and map new HF param back
	def check_and_map_params(lowercase__			, lowercase__	):
		snake_case_      		= hf_param.shape

		snake_case_      		= to_torch(params[gluon_param]	)
		snake_case_      		= gluon_param.shape

		assert (
		    shape_hf == shape_gluon
		), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

		return gluon_param

	snake_case_      		= check_and_map_params(
	    hf_bort_model.bert.embeddings.word_embeddings.weight			, 'word_embed.0.weight'	)
	snake_case_      		= check_and_map_params(
	    hf_bort_model.bert.embeddings.position_embeddings.weight			, 'encoder.position_weight'	)
	snake_case_      		= check_and_map_params(
	    hf_bort_model.bert.embeddings.LayerNorm.bias			, 'encoder.layer_norm.beta'	)
	snake_case_      		= check_and_map_params(
	    hf_bort_model.bert.embeddings.LayerNorm.weight			, 'encoder.layer_norm.gamma'	)

	# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
	snake_case_      		= torch.zeros_like(
	    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

	for i in range(hf_bort_config.num_hidden_layers	):
		snake_case_      		= hf_bort_model.bert.encoder.layer[i]

		# self attention
		snake_case_      		= layer.attention.self

		snake_case_      		= check_and_map_params(
		    self_attn.key.bias.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""	)

		snake_case_      		= check_and_map_params(
		    self_attn.key.weight.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""	)
		snake_case_      		= check_and_map_params(
		    self_attn.query.bias.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""	)
		snake_case_      		= check_and_map_params(
		    self_attn.query.weight.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""	)
		snake_case_      		= check_and_map_params(
		    self_attn.value.bias.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""	)
		snake_case_      		= check_and_map_params(
		    self_attn.value.weight.data			, f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""	)

		# self attention output
		snake_case_      		= layer.attention.output

		snake_case_      		= check_and_map_params(
		    self_output.dense.bias			, f"""encoder.transformer_cells.{i}.proj.bias"""	)
		snake_case_      		= check_and_map_params(
		    self_output.dense.weight			, f"""encoder.transformer_cells.{i}.proj.weight"""	)
		snake_case_      		= check_and_map_params(
		    self_output.LayerNorm.bias			, f"""encoder.transformer_cells.{i}.layer_norm.beta"""	)
		snake_case_      		= check_and_map_params(
		    self_output.LayerNorm.weight			, f"""encoder.transformer_cells.{i}.layer_norm.gamma"""	)

		# intermediate
		snake_case_      		= layer.intermediate

		snake_case_      		= check_and_map_params(
		    intermediate.dense.bias			, f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""	)
		snake_case_      		= check_and_map_params(
		    intermediate.dense.weight			, f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""	)

		# output
		snake_case_      		= layer.output

		snake_case_      		= check_and_map_params(
		    bert_output.dense.bias			, f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""	)
		snake_case_      		= check_and_map_params(
		    bert_output.dense.weight			, f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""	)
		snake_case_      		= check_and_map_params(
		    bert_output.LayerNorm.bias			, f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""	)
		snake_case_      		= check_and_map_params(
		    bert_output.LayerNorm.weight			, f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""	)

	# Save space and energy 🎄
	hf_bort_model.half()

	# Compare output of both models
	snake_case_      		= RobertaTokenizer.from_pretrained('roberta-base'	)

	snake_case_      		= tokenizer.encode_plus(lowercase__	)['input_ids']

	# Get gluon output
	snake_case_      		= mx.nd.array([input_ids]	)
	snake_case_      		= original_bort(inputs=lowercase__			, token_types=[]	)

	# Get Transformer output (save and reload model again)
	hf_bort_model.save_pretrained(lowercase__	)
	snake_case_      		= BertModel.from_pretrained(lowercase__	)
	hf_bort_model.eval()

	snake_case_      		= tokenizer.encode_plus(lowercase__			, return_tensors='pt'	)
	snake_case_      		= hf_bort_model(**lowercase__	)[0]

	snake_case_      		= output_gluon[0].asnumpy()
	snake_case_      		= output_hf[0].detach().numpy()

	snake_case_      		= np.max(np.abs(hf_layer - gluon_layer	)	).item()
	snake_case_      		= np.allclose(lowercase__			, lowercase__			, atol=1e-3	)

	if success:
		print('✔️ Both model do output the same tensors'	)
	else:
		print('❌ Both model do **NOT** output the same tensors'	)
		print('Absolute difference is:'			, lowercase__	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		A											=     parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



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


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


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			@register_to_config
			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None							):

				"""simple docstring"""


				super().__init__()

				snake_case_      		= 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"

					snake_case_      		= torch.zeros(__UpperCamelCase ,				__UpperCamelCase							)
				else:
					snake_case_      		= None

				snake_case_      		= torch.nn.Parameter(__UpperCamelCase							)







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42
			__A						  =  42


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__()

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) if isinstance(__UpperCamelCase ,				__UpperCamelCase							) else 1

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

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					snake_case_      		= 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}"""							)
					snake_case_      		= text_input_ids[:, : self.tokenizer.model_max_length]
				snake_case_      		= 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
				snake_case_      		= prompt_embeds / prompt_embeds.norm(dim=-1 ,				keepdim=__UpperCamelCase							)

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

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

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

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					snake_case_      		= negative_prompt_embeds.shape[1]
					snake_case_      		= negative_prompt_embeds.repeat(1 ,				__UpperCamelCase ,				1							)
					snake_case_      		= 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
					snake_case_      		= torch.cat([negative_prompt_embeds, prompt_embeds]							)

				return prompt_embeds


			@torch.no_grad()
			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = 1_00 ,				__UpperCamelCase = 5.0 ,				__UpperCamelCase = 1.0 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = "pil" ,				__UpperCamelCase = True ,				__UpperCamelCase = None ,				__UpperCamelCase = 1 ,				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= 1
				elif isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= len(__UpperCamelCase							)
				else:
					raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase							)}"""							)

				snake_case_      		= batch_size * num_images_per_prompt

				snake_case_      		= guidance_scale > 1.0

				snake_case_      		= 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

				snake_case_      		= (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
					snake_case_      		= self.transformer.num_vector_embeds - 1
					snake_case_      		= 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)."""							)
					snake_case_      		= latents.to(self.device							)

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

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

				snake_case_      		= latents

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

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

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

					snake_case_      		= self.truncate(__UpperCamelCase ,				__UpperCamelCase							)

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

					# compute the previous noisy sample x_t -> x_t-1
					snake_case_      		= 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							)

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

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

				if output_type == "pil":
					snake_case_      		= self.numpy_to_pil(__UpperCamelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= torch.sort(__UpperCamelCase ,				1 ,				descending=__UpperCamelCase							)
				snake_case_      		= torch.exp(__UpperCamelCase							)
				snake_case_      		= sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

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

				snake_case_      		= log_p_x_0.clone()

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

				return rv
