






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      :       str				  =   logging.get_logger(__name__)

__a      :       Union[str, Any]				  =   "The Nymphenburg Palace is a beautiful palace in Munich!"



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			str    )						->      str:





				"""simple docstring"""

				__A         =	{
				    """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,
				}

				__A         =	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
				__A         =	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
				__A         =	"""openwebtext_ccnews_stories_books_cased"""

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

				__A         =	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    )
				__A         =	original_bort._collect_params_with_prefix()

				# Build our config 🤗
				__A         =	{
				    """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    ),
				}

				__A         =	BertConfig.from_dict(__lowercase    )
				__A         =	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   :			Optional[int]    ) -> 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   :			Dict			,				__lowercase   :			Dict    ):
								__A         =	hf_param.shape

								__A         =	to_torch(params[gluon_param]    )
								__A         =	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

				__A         =	check_and_map_params(
				    hf_bort_model.bert.embeddings.word_embeddings.weight			,				"""word_embed.0.weight"""    )
				__A         =	check_and_map_params(
				    hf_bort_model.bert.embeddings.position_embeddings.weight			,				"""encoder.position_weight"""    )
				__A         =	check_and_map_params(
				    hf_bort_model.bert.embeddings.LayerNorm.bias			,				"""encoder.layer_norm.beta"""    )
				__A         =	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)
				__A         =	torch.zeros_like(
				    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

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

								# self attention
								__A         =	layer.attention.self

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

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

								# self attention output
								__A         =	layer.attention.output

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

								# intermediate
								__A         =	layer.intermediate

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

								# output
								__A         =	layer.output

								__A         =	check_and_map_params(
								    bert_output.dense.bias			,				f"encoder.transformer_cells.{i}.ffn.ffn_2.bias"    )
								__A         =	check_and_map_params(
								    bert_output.dense.weight			,				f"encoder.transformer_cells.{i}.ffn.ffn_2.weight"    )
								__A         =	check_and_map_params(
								    bert_output.LayerNorm.bias			,				f"encoder.transformer_cells.{i}.ffn.layer_norm.beta"    )
								__A         =	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
				__A         =	RobertaTokenizer.from_pretrained("""roberta-base"""    )

				__A         =	tokenizer.encode_plus(__lowercase    )["""input_ids"""]

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

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

				__A         =	tokenizer.encode_plus(__lowercase			,				return_tensors="""pt"""    )
				__A         =	hf_bort_model(**__lowercase    )[0]

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

				__A         =	np.max(np.abs(hf_layer - gluon_layer    )    ).item()
				__A         =	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      :       Optional[int]				  =   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      :       Optional[int]				  =   parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      ImageTextPipelineOutput,
      UniDiffuserPipeline,
  )
else:
  from .modeling_text_decoder import UniDiffuserTextDecoder
  from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
  from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




