
import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=0							):
	# Format the message.
	if name is None:
		lowercase       =    None
	else:
		lowercase       =    """.""" * max(0 ,							spaces - 2							) + """# {:""" + str(50 - 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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
	# for compatibility with later versions of NVIDIA Megatron-LM.
	# The inverse operation is performed inside Megatron-LM to read checkpoints:
	# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
	# If param is the weight tensor of the self-attention block, the returned tensor
	# will have to be transposed one more time to be read by HuggingFace GPT2.
	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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# The converted output model.
	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_		(							):
	# Create the argument parser.
	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_0257 ,							n_positions=1024 ,							n_embd=1024 ,							n_layer=24 ,							n_head=16 ,							n_inner=4096 ,							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_0256 ,							eos_token_id=5_0256 ,							)
	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"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




