



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
				__snake_case     :List[str]			       =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')
				__snake_case     :Dict			       =parser.parse_args()

				if args.model_type == "roberta":
								__snake_case     :int			       =RobertaForMaskedLM.from_pretrained(args.model_name)
								__snake_case     :Any			       ='roberta'
				elif args.model_type == "gpt2":
								__snake_case     :List[str]			       =GPTaLMHeadModel.from_pretrained(args.model_name)
								__snake_case     :Optional[Any]			       ='transformer'

				__snake_case     :List[Any]			       =model.state_dict()
				__snake_case     :Optional[Any]			       ={}

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

    # Transformer Blocks #
				__snake_case     :str			       =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"]:
																				__snake_case     :Optional[int]			       =state_dict[
																				    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																				]
												__snake_case     :Tuple			       =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"]:
																				__snake_case     :Dict			       =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"]:
												__snake_case     :List[Any]			       =state_dict[F'''{layer}''']
								if args.vocab_transform:
												for w in ["weight", "bias"]:
																__snake_case     :Any			       =state_dict[F'''lm_head.dense.{w}''']
																__snake_case     :Tuple			       =state_dict[F'''lm_head.layer_norm.{w}''']
				elif args.model_type == "gpt2":
								for w in ["weight", "bias"]:
												__snake_case     :str			       =state_dict[F'''{prefix}.ln_f.{w}''']
								__snake_case     :List[Any]			       =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)




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_prompt=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											A      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											A      						=    cs.out[:-1]  # Remove the final "\n"
											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
											self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text