


'''simple docstring'''



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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       {
			    """attention_cell""": """multi_head""",
			    """num_layers""": 4,
			    """units""": 1_0_2_4,
			    """hidden_size""": 7_6_8,
			    """max_length""": 5_1_2,
			    """num_heads""": 8,
			    """scaled""": True,
			    """dropout""": 0.1,
			    """use_residual""": True,
			    """embed_size""": 1_0_2_4,
			    """embed_dropout""": 0.1,
			    """word_embed""": None,
			    """layer_norm_eps""": 1e-5,
			    """token_type_vocab_size""": 2,
			}

			UpperCAmelCase__					:   Any       				=       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
			UpperCAmelCase__					:   Optional[Any]       				=       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=UpperCamelCase__	, output_all_encodings=UpperCamelCase__	, use_residual=predefined_args["""use_residual"""]	, activation=predefined_args.get("""activation"""	, """gelu"""   )	, layer_norm_eps=predefined_args.get("""layer_norm_eps"""	, UpperCamelCase__   )	, )

			# Vocab information needs to be fetched first
			# It's the same as RoBERTa, so RobertaTokenizer can be used later
			UpperCAmelCase__					:   Union[str, Any]       				=       """openwebtext_ccnews_stories_books_cased"""

			# Specify download folder to Gluonnlp's vocab
			UpperCAmelCase__					:   Dict       				=       os.path.join(get_home_dir()	, """models"""   )
			UpperCAmelCase__					:   List[str]       				=       _load_vocab(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, cls=UpperCamelCase__   )

			UpperCAmelCase__					:   int       				=       nlp.model.BERTModel(
			    UpperCamelCase__	, len(UpperCamelCase__   )	, units=predefined_args["""units"""]	, embed_size=predefined_args["""embed_size"""]	, embed_dropout=predefined_args["""embed_dropout"""]	, word_embed=predefined_args["""word_embed"""]	, use_pooler=UpperCamelCase__	, use_token_type_embed=UpperCamelCase__	, token_type_vocab_size=predefined_args["""token_type_vocab_size"""]	, use_classifier=UpperCamelCase__	, use_decoder=UpperCamelCase__	, )

			original_bort.load_parameters(UpperCamelCase__	, cast_dtype=UpperCamelCase__	, ignore_extra=UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       original_bort._collect_params_with_prefix()

			# Build our config 🤗
			UpperCAmelCase__					:   Optional[int]       				=       {
			    """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(UpperCamelCase__   ),
			}

			UpperCAmelCase__					:   List[Any]       				=       BertConfig.from_dict(UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       BertForMaskedLM(UpperCamelCase__   )
			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(UpperCamelCase__   ) -> 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(UpperCamelCase__	, UpperCamelCase__   ):
						UpperCAmelCase__					:   str       				=       hf_param.shape

						UpperCAmelCase__					:   Any       				=       to_torch(params[gluon_param]   )
						UpperCAmelCase__					:   Optional[Any]       				=       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

			UpperCAmelCase__					:   List[Any]       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight	, """word_embed.0.weight"""   )
			UpperCAmelCase__					:   Dict       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight	, """encoder.position_weight"""   )
			UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias	, """encoder.layer_norm.beta"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       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)
			UpperCAmelCase__					:   Tuple       				=       torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

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

						# self attention
						UpperCAmelCase__					:   BertSelfAttention       				=       layer.attention.self

						UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
						    self_attn.key.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''   )

						UpperCAmelCase__					:   Tuple       				=       check_and_map_params(
						    self_attn.key.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    self_attn.query.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''   )
						UpperCAmelCase__					:   str       				=       check_and_map_params(
						    self_attn.query.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''   )
						UpperCAmelCase__					:   List[Any]       				=       check_and_map_params(
						    self_attn.value.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    self_attn.value.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''   )

						# self attention output
						UpperCAmelCase__					:   BertSelfOutput       				=       layer.attention.output

						UpperCAmelCase__					:   Tuple       				=       check_and_map_params(
						    self_output.dense.bias	, f'''encoder.transformer_cells.{i}.proj.bias'''   )
						UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
						    self_output.dense.weight	, f'''encoder.transformer_cells.{i}.proj.weight'''   )
						UpperCAmelCase__					:   Optional[Any]       				=       check_and_map_params(
						    self_output.LayerNorm.bias	, f'''encoder.transformer_cells.{i}.layer_norm.beta'''   )
						UpperCAmelCase__					:   Union[str, Any]       				=       check_and_map_params(
						    self_output.LayerNorm.weight	, f'''encoder.transformer_cells.{i}.layer_norm.gamma'''   )

						# intermediate
						UpperCAmelCase__					:   BertIntermediate       				=       layer.intermediate

						UpperCAmelCase__					:   int       				=       check_and_map_params(
						    intermediate.dense.bias	, f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''   )
						UpperCAmelCase__					:   Dict       				=       check_and_map_params(
						    intermediate.dense.weight	, f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''   )

						# output
						UpperCAmelCase__					:   BertOutput       				=       layer.output

						UpperCAmelCase__					:   Union[str, Any]       				=       check_and_map_params(
						    bert_output.dense.bias	, f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''   )
						UpperCAmelCase__					:   Any       				=       check_and_map_params(
						    bert_output.dense.weight	, f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    bert_output.LayerNorm.bias	, f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''   )
						UpperCAmelCase__					:   List[Any]       				=       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
			UpperCAmelCase__					:   Optional[int]       				=       RobertaTokenizer.from_pretrained("""roberta-base"""   )

			UpperCAmelCase__					:   str       				=       tokenizer.encode_plus(UpperCamelCase__   )["""input_ids"""]

			# Get gluon output
			UpperCAmelCase__					:   Dict       				=       mx.nd.array([input_ids]   )
			UpperCAmelCase__					:   str       				=       original_bort(inputs=UpperCamelCase__	, token_types=[]   )

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       BertModel.from_pretrained(UpperCamelCase__   )
			hf_bort_model.eval()

			UpperCAmelCase__					:   List[str]       				=       tokenizer.encode_plus(UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Tuple       				=       hf_bort_model(**UpperCamelCase__   )[0]

			UpperCAmelCase__					:   Tuple       				=       output_gluon[0].asnumpy()
			UpperCAmelCase__					:   Dict       				=       output_hf[0].detach().numpy()

			UpperCAmelCase__					:   Dict       				=       np.max(np.abs(hf_layer - gluon_layer   )   ).item()
			UpperCAmelCase__					:   Optional[Any]       				=       np.allclose(UpperCamelCase__	, UpperCamelCase__	, 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:"""	, UpperCamelCase__   )


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)



'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


__A		=[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       Github(os.environ["""GITHUB_TOKEN"""]   )
			UpperCAmelCase__					:   Any       				=       g.get_repo("""huggingface/diffusers"""   )
			UpperCAmelCase__					:   Optional[int]       				=       repo.get_issues(state="""open"""   )

			for issue in open_issues:
						UpperCAmelCase__					:   Any       				=       sorted(issue.get_comments()	, key=lambda UpperCamelCase__   : i.created_at	, reverse=UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       comments[0] if len(UpperCamelCase__   ) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""   )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""   )
									issue.remove_from_labels("""stale"""   )
						elif (
						    (dt.utcnow() - issue.updated_at).days > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									issue.create_comment(
									    """This issue has been automatically marked as stale because it has not had """
									    """recent activity. If you think this still needs to be addressed """
									    """please comment on this thread.\n\nPlease note that issues that do not follow the """
									    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""   )
									issue.add_to_labels("""stale"""   )


if __name__ == "__main__":
		main()