



'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			'perceiver'



 def __init__(self					,  A=256					,  A=1_280					,  A=768					,  A=1					,  A=26					,  A=8					,  A=8					,  A=None					,  A=None					,  A="kv"					,  A=1					,  A=1					,  A="gelu"					,  A=0.1					,  A=0.02					,  A=1E-12					,  A=True					,  A=262					,  A=2_048					,  A=56					,  A=[368, 496]					,  A=16					,  A=1_920					,  A=16					,  A=[1, 16, 224, 224]					,  **A					,  )       ->	Union[str, Any]:


      """simple docstring"""



      super().__init__(**A			)

      _a           =    num_latents
      _a           =    d_latents
      _a           =    d_model
      _a           =    num_blocks
      _a           =    num_self_attends_per_block
      _a           =    num_self_attention_heads
      _a           =    num_cross_attention_heads
      _a           =    qk_channels
      _a           =    v_channels
      _a           =    cross_attention_shape_for_attention
      _a           =    self_attention_widening_factor
      _a           =    cross_attention_widening_factor
      _a           =    hidden_act
      _a           =    attention_probs_dropout_prob
      _a           =    initializer_range
      _a           =    layer_norm_eps
      _a           =    use_query_residual
      # masked language modeling attributes
      _a           =    vocab_size
      _a           =    max_position_embeddings
      # image classification attributes
      _a           =    image_size
      # flow attributes
      _a           =    train_size
      # multimodal autoencoding attributes
      _a           =    num_frames
      _a           =    audio_samples_per_frame
      _a           =    samples_per_patch
      _a           =    output_shape






class __A     (    A		):



 '''simple docstring'''







 @property
 def    a__    (self			)       ->	Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a           =    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a           =    {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''inputs''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)



 @property
 def    a__    (self			)       ->	float:


      """simple docstring"""



      return 1E-4



 def    a__    (self					,  A					,  A = -1					,  A = -1					,  A = -1					,  A = False					,  A = None					,  A = 3					,  A = 40					,  A = 40					,  )       ->	Mapping[str, Any]:


      """simple docstring"""



      if isinstance(A					,  A			):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _a           =    compute_effective_axis_dimension(
               A					,  fixed_dimension=OnnxConfig.default_fixed_batch					,  num_token_to_add=0			)
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _a           =    preprocessor.num_special_tokens_to_add(A			)
           _a           =    compute_effective_axis_dimension(
               A					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=A			)
           # Generate dummy inputs according to compute batch and sequence
           _a           =    [''' '''.join(['''a''']			) * seq_length] * batch_size
           _a           =    dict(preprocessor(A					,  return_tensors=A			)			)
           _a           =    inputs.pop('''input_ids'''			)
           return inputs
      elif isinstance(A					,  A			) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _a           =    compute_effective_axis_dimension(A					,  fixed_dimension=OnnxConfig.default_fixed_batch			)
           _a           =    self._generate_dummy_images(A					,  A					,  A					,  A			)
           _a           =    dict(preprocessor(images=A					,  return_tensors=A			)			)
           _a           =    inputs.pop('''pixel_values'''			)
           return inputs
      else:
           raise ValueError(
               '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''			)











"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

