



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def     a(      A       :    List[Any] ,     A       :    Optional[Any] ,     A       :    Any=0       )		->       Optional[int]:
					"""simple docstring"""





					if name is None:
										a					     =   None
					else:
										a					     =   "." * max(0 ,     spaces - 2       ) + "# {:" + str(50 - spaces       ) + "s}"
										a					     =   fmt.format(A       )

					# Print and recurse (if needed).
					if isinstance(A ,     A       ):
										if msg is not None:
															print(A       )
										for k in val.keys():
															recursive_print(A ,     val[k] ,     spaces + 2       )
					elif isinstance(A ,     torch.Tensor       ):
										print(A ,     ":" ,     val.size()       )
					else:
										print(A ,     ":" ,     A       )






def     a(      A       :    Tuple ,     A       :    Optional[int] ,     A       :    int ,     A       :    Tuple ,     A       :    Optional[int]       )		->       List[Any]:
					"""simple docstring"""





					a					     =   param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										a					     =   (num_heads, hidden_size, num_splits) + input_shape[1:]
										a					     =   param.view(*A       )
										a					     =   param.transpose(0 ,     2       )
										a					     =   param.transpose(1 ,     2       ).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										a					     =   (num_heads, num_splits, hidden_size) + input_shape[1:]
										a					     =   param.view(*A       )
										a					     =   param.transpose(0 ,     1       ).contiguous()
					a					     =   param.view(*A       )
					return param






def     a(      A       :    Dict ,     A       :    Union[str, Any] ,     A       :    Dict       )		->       Tuple:
					"""simple docstring"""





					a					     =   {}

					# old versions did not store training args
					a					     =   input_state_dict.get("args" ,     A       )
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										a					     =   ds_args.padded_vocab_size
										a					     =   ds_args.max_position_embeddings
										a					     =   ds_args.hidden_size
										a					     =   ds_args.num_layers
										a					     =   ds_args.num_attention_heads
										a					     =   ds_args.ffn_hidden_size
										# pprint(config)

					# The number of heads.
					a					     =   config.n_head
					# The hidden_size per head.
					a					     =   config.n_embd // config.n_head
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										a					     =   input_state_dict["checkpoint_version"]
					else:
										a					     =   0.0

					# The model.
					a					     =   input_state_dict["model"]
					# The language model.
					a					     =   model["language_model"]
					# The embeddings.
					a					     =   lm["embedding"]

					# The word embeddings.
					a					     =   embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					a					     =   word_embeddings[: config.vocab_size, :]
					a					     =   word_embeddings

					# The position embeddings.
					a					     =   embeddings["position_embeddings"]["weight"]
					# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
					a					     =   pos_embeddings.size(0       )
					if n_positions != config.n_positions:
										raise ValueError(
										    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''       )
					# Store the position embeddings.
					a					     =   pos_embeddings

					# The transformer.
					a					     =   lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					a					     =   re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"       )

					# The simple map of names for "automated" rules.
					a					     =   {
					    "attention.dense": ".attn.c_proj.",
					    "self_attention.dense": ".attn.c_proj.",
					    "mlp.dense_h_to_4h": ".mlp.c_fc.",
					    "mlp.dense_4h_to_h": ".mlp.c_proj.",
					}

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										a					     =   layer_re.match(A       )

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										a					     =   int(m.group(1       )       )
										# The name of the operation.
										a					     =   m.group(2       )
										# Is it a weight or a bias?
										a					     =   m.group(3       )

										# The name of the layer.
										a					     =   f'''transformer.h.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"       ):
															a					     =   "ln_1" if op_name.startswith("input"       ) else "ln_2"
															a					     =   val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Insert a tensor of 1x1xDxD bias.
															a					     =   torch.tril(torch.ones((n_positions, n_positions) ,     dtype=torch.floataa       )       ).view(
															    1 ,     1 ,     A ,     A       )
															a					     =   causal_mask

															# Insert a "dummy" tensor for masked_bias.
															a					     =   torch.tensor(-1e4 ,     dtype=torch.floataa       )
															a					     =   masked_bias

															a					     =   fix_query_key_value_ordering(A ,     A ,     3 ,     A ,     A       )
															# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
															a					     =   out_val.transpose(0 ,     1       ).contiguous()
															# Store.
															a					     =   out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															a					     =   fix_query_key_value_ordering(A ,     A ,     3 ,     A ,     A       )
															# Store. No change of shape.
															a					     =   out_val

										# Transpose the weights.
										elif weight_or_bias == "weight":
															a					     =   megatron_to_transformers[op_name]
															a					     =   val.transpose(0 ,     1       )

										# Copy the bias.
										elif weight_or_bias == "bias":
															a					     =   megatron_to_transformers[op_name]
															a					     =   val

    # DEBUG.
					assert config.n_layer == layer_idx + 1

					# The final layernorm.
					a					     =   transformer["final_layernorm.weight"]
					a					     =   transformer["final_layernorm.bias"]

					# For LM head, transformers' wants the matrix to weight embeddings.
					a					     =   word_embeddings

					# It should be done!
					return output_state_dict






def     a(      )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" ,     action="store_true"       )
					parser.add_argument(
					    "path_to_checkpoint" ,     type=A ,     help="Path to the checkpoint file (.zip archive or direct .pt file)" ,     )
					parser.add_argument(
					    "--config_file" ,     default="" ,     type=A ,     help="An optional config json file describing the pre-trained model." ,     )
					a					     =   parser.parse_args()

					# Extract the basename.
					a					     =   os.path.dirname(args.path_to_checkpoint       )

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''       )
					if args.path_to_checkpoint.endswith(".zip"       ):
										with zipfile.ZipFile(args.path_to_checkpoint ,     "r"       ) as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"       ) as pytorch_dict:
																				a					     =   torch.load(A ,     map_location="cpu"       )
					else:
										a					     =   torch.load(args.path_to_checkpoint ,     map_location="cpu"       )

					a					     =   input_state_dict.get("args" ,     A       )

					# Read the config, or default to the model released by NVIDIA.
					if args.config_file == "":
										if ds_args is not None:
															if ds_args.bias_gelu_fusion:
																				a					     =   "gelu_fast"
															elif ds_args.openai_gelu:
																				a					     =   "gelu_new"
															else:
																				a					     =   "gelu"
										else:
															# in the very early days this used to be "gelu_new"
															a					     =   "gelu_new"

										# Spell out all parameters in case the defaults change.
										a					     =   GPTaConfig(
										    vocab_size=5_0257 ,     n_positions=1024 ,     n_embd=1024 ,     n_layer=24 ,     n_head=16 ,     n_inner=4096 ,     activation_function=A ,     resid_pdrop=0.1 ,     embd_pdrop=0.1 ,     attn_pdrop=0.1 ,     layer_norm_epsilon=1e-5 ,     initializer_range=0.02 ,     summary_type="cls_index" ,     summary_use_proj=A ,     summary_activation=A ,     summary_proj_to_labels=A ,     summary_first_dropout=0.1 ,     scale_attn_weights=A ,     use_cache=A ,     bos_token_id=5_0256 ,     eos_token_id=5_0256 ,     )
					else:
										a					     =   GPTaConfig.from_json_file(args.config_file       )

					a					     =   ["GPT2LMHeadModel"]

					# Convert.
					print("Converting"       )
					a					     =   convert_megatron_checkpoint(A ,     A ,     A       )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(A ,     A       )

					# Add tokenizer class info to config
					# see https://github.com/huggingface/transformers/issues/13906)
					if ds_args is not None:
										a					     =   ds_args.tokenizer_type
										if tokenizer_type == "GPT2BPETokenizer":
															a					     =   "gpt2"
										elif tokenizer_type == "PretrainedFromHF":
															a					     =   ds_args.tokenizer_name_or_path
										else:
															raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''       )
					else:
										a					     =   "gpt2"

					a					     =   AutoTokenizer.from_pretrained(A       )
					a					     =   type(A       ).__name__
					a					     =   tokenizer_class

					# Store the config to file.
					print("Saving config"       )
					config.save_pretrained(A       )

					# Save tokenizer based on args
					print(f'''Adding {tokenizer_class} tokenizer files'''       )
					tokenizer.save_pretrained(A       )

					# Store the state_dict to file.
					a					     =   os.path.join(A ,     "pytorch_model.bin"       )
					print(f'''Saving checkpoint to "{output_checkpoint_file}"'''       )
					torch.save(A ,     A       )


####################################################################################################

if __name__ == "__main__":
				main()

####################################################################################################





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   tempfile.mkdtemp()

						a					     =   [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						a					     =   os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["vocab_file"]		)
						with open(self.vocab_file		,       "w"		,       encoding="utf-8"		) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

						a					     =   {
						    "do_resize": True,
						    "size": {"height": 224, "width": 224},
						    "do_center_crop": True,
						    "crop_size": {"height": 18, "width": 18},
						    "do_normalize": True,
						    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
						    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
						    "do_convert_rgb": True,
						}
						a					     =   os.path.join(self.tmpdirname		,       lowerCamelCase_		)
						with open(self.image_processor_file		,       "w"		,       encoding="utf-8"		) as fp:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return BertTokenizer.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return BertTokenizerFast.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						shutil.rmtree(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   [np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta		)]

						a					     =   [Image.fromarray(np.moveaxis(lowerCamelCase_		,       0		,       -1		)		) for x in image_inputs]

						return image_inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizer()
						a					     =   self.get_rust_tokenizer()
						a					     =   self.get_image_processor()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_slow.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,       use_fast=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_fast.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor_slow.tokenizer.get_vocab()		,       tokenizer_slow.get_vocab()		)
						self.assertEqual(processor_fast.tokenizer.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertEqual(tokenizer_slow.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertIsInstance(processor_slow.tokenizer		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.tokenizer		,       lowerCamelCase_		)

						self.assertEqual(processor_slow.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertEqual(processor_fast.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertIsInstance(processor_slow.image_processor		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						a					     =   self.get_tokenizer(cls_token="(CLS)"		,       sep_token="(SEP)"		)
						a					     =   self.get_image_processor(do_normalize=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname		,       cls_token="(CLS)"		,       sep_token="(SEP)"		,       do_normalize=lowerCamelCase_		)

						self.assertEqual(processor.tokenizer.get_vocab()		,       tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer		,       lowerCamelCase_		)

						self.assertEqual(processor.image_processor.to_json_string()		,       image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   self.prepare_image_inputs()

						a					     =   image_processor(lowerCamelCase_		,       return_tensors="np"		)
						a					     =   processor(images=lowerCamelCase_		,       return_tensors="np"		)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()		,       input_processor[key].sum()		,       delta=1E-2		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"

						a					     =   processor(text=lowerCamelCase_		)

						a					     =   tokenizer(lowerCamelCase_		)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]		,       encoded_processor[key]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

						self.assertListEqual(list(inputs.keys()		)		,       ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

						# test if it raises when no input is passed
						with pytest.raises(lowerCamelCase_		):
											processor()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						a					     =   processor.batch_decode(lowerCamelCase_		)
						a					     =   tokenizer.batch_decode(lowerCamelCase_		)

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

						self.assertListEqual(list(inputs.keys()		)		,       processor.model_input_names		)
