"""simple docstring"""






import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Optional[Any]=0	)				->					Any:
      '''simple docstring'''


      if name is None:
            lowercase														=			None
      else:
            lowercase														=			""".""" * max(0      ,	spaces - 2	) + """# {:""" + str(5_0 - spaces	) + """s}"""
            lowercase														=			fmt.format(__lowerCAmelCase	)

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


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Dict	)				->					List[Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Tuple:
      '''simple docstring'''


      lowercase														=			{}

      # old versions did not store training args
      lowercase														=			input_state_dict.get("""args"""      ,	__lowerCAmelCase	)
      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))

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

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

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

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

      # The position embeddings.
      lowercase														=			embeddings["""position_embeddings"""]["""weight"""]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      lowercase														=			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.
      lowercase														=			pos_embeddings

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

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

      # The simple map of names for "automated" rules.
      lowercase														=			{
          """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.
            lowercase														=			layer_re.match(__lowerCAmelCase	)

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

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

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

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("""layernorm"""	):
                  lowercase														=			"""ln_1""" if op_name.startswith("""input"""	) else """ln_2"""
                  lowercase														=			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.
                  lowercase														=			torch.tril(torch.ones((n_positions, n_positions)      ,	dtype=torch.floataa	)	).view(
                      1      ,	1      ,	__lowerCAmelCase      ,	__lowerCAmelCase	)
                  lowercase														=			causal_mask

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

                  lowercase														=			fix_query_key_value_ordering(__lowerCAmelCase      ,	__lowerCAmelCase      ,	3      ,	__lowerCAmelCase      ,	__lowerCAmelCase	)
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  lowercase														=			out_val.transpose(0      ,	1	).contiguous()
                  # Store.
                  lowercase														=			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":
                  lowercase														=			fix_query_key_value_ordering(__lowerCAmelCase      ,	__lowerCAmelCase      ,	3      ,	__lowerCAmelCase      ,	__lowerCAmelCase	)
                  # Store. No change of shape.
                  lowercase														=			out_val

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

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

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

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

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

      # It should be done!
      return output_state_dict


def     UpperCAmelCase__ (       )				->					List[Any]:
      '''simple docstring'''


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

      # Extract the basename.
      lowercase														=			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:
                        lowercase														=			torch.load(__lowerCAmelCase      ,	map_location="""cpu"""	)
      else:
            lowercase														=			torch.load(args.path_to_checkpoint      ,	map_location="""cpu"""	)

      lowercase														=			input_state_dict.get("""args"""      ,	__lowerCAmelCase	)

      # 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:
                        lowercase														=			"""gelu_fast"""
                  elif ds_args.openai_gelu:
                        lowercase														=			"""gelu_new"""
                  else:
                        lowercase														=			"""gelu"""
            else:
                  # in the very early days this used to be "gelu_new"
                  lowercase														=			"""gelu_new"""

            # Spell out all parameters in case the defaults change.
            lowercase														=			GPTaConfig(
                vocab_size=5_0_2_5_7      ,	n_positions=1_0_2_4      ,	n_embd=1_0_2_4      ,	n_layer=2_4      ,	n_head=1_6      ,	n_inner=4_0_9_6      ,	activation_function=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	summary_activation=__lowerCAmelCase      ,	summary_proj_to_labels=__lowerCAmelCase      ,	summary_first_dropout=0.1      ,	scale_attn_weights=__lowerCAmelCase      ,	use_cache=__lowerCAmelCase      ,	bos_token_id=5_0_2_5_6      ,	eos_token_id=5_0_2_5_6      ,	)
      else:
            lowercase														=			GPTaConfig.from_json_file(args.config_file	)

      lowercase														=			["""GPT2LMHeadModel"""]

      # Convert.
      print("""Converting"""	)
      lowercase														=			convert_megatron_checkpoint(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase	)

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

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

      lowercase														=			AutoTokenizer.from_pretrained(__lowerCAmelCase	)
      lowercase														=			type(__lowerCAmelCase	).__name__
      lowercase														=			tokenizer_class

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

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

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


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

if __name__ == "__main__":
       main()

####################################################################################################
"""simple docstring"""






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.roberta.modeling_flax_roberta import (
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaModel,
       )





class 				_A		( unittest.TestCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=4	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_attention_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_choices

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_attention_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size		)

            lowercase														=			RobertaConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=__lowerCAmelCase	, initializer_range=self.initializer_range	, )

            return config, input_ids, token_type_ids, attention_mask

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs

            lowercase														=			True
            lowercase														=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2		)

            return (
                config,
                input_ids,
                token_type_ids,
                encoder_hidden_states,
                encoder_attention_mask,
            )






@require_flax
class 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Optional[int]													=			True

      snake_case__    :     List[str]													=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxRobertaModelTester(self		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase														=			model_class_name.from_pretrained("""roberta-base"""	, from_pt=__lowerCAmelCase		)
                  lowercase														=			model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)



