import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_						=       {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase_						=       {
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

lowercase_						=       {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class 			_snake_case     (   snake_case_):
				UpperCamelCase__			:   Dict       						=VOCAB_FILES_NAMES
				UpperCamelCase__			:   List[Any]       						=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__			:   Any       						=PRETRAINED_INIT_CONFIGURATION
				UpperCamelCase__			:   int       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__			:   Dict       						=RealmTokenizer




				def __init__(  self   :  str,   __lowercase   :  Tuple=None,   __lowercase   :  Tuple=None,   __lowercase   :  Any=True,   __lowercase   :  Union[str, Any]="[UNK]",   __lowercase   :  Any="[SEP]",   __lowercase   :  int="[PAD]",   __lowercase   :  Optional[Any]="[CLS]",   __lowercase   :  Any="[MASK]",   __lowercase   :  Dict=True,   __lowercase   :  int=None,   **__lowercase   :  Any,   ):
							super().__init__(
							    __lowercase,   tokenizer_file=__lowercase,   do_lower_case=__lowercase,   unk_token=__lowercase,   sep_token=__lowercase,   pad_token=__lowercase,   cls_token=__lowercase,   mask_token=__lowercase,   tokenize_chinese_chars=__lowercase,   strip_accents=__lowercase,   **__lowercase,   )

							lowercase__   					=			json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
							if (
							    normalizer_state.get("lowercase",   __lowercase  ) != do_lower_case
							    or normalizer_state.get("strip_accents",   __lowercase  ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars",   __lowercase  ) != tokenize_chinese_chars
							):
										lowercase__   					=			getattr(__lowercase,   normalizer_state.pop("type"  )  )
										lowercase__   					=			do_lower_case
										lowercase__   					=			strip_accents
										lowercase__   					=			tokenize_chinese_chars
										lowercase__   					=			normalizer_class(**__lowercase  )

							lowercase__   					=			do_lower_case




				def 							A__ (  self   :  int,   __lowercase   :  List[Any],   **__lowercase   :  Tuple  ):
							lowercase__   					=			PaddingStrategy.MAX_LENGTH

							lowercase__   					=			text
							lowercase__   					=			kwargs.pop("text_pair",   __lowercase  )
							lowercase__   					=			kwargs.pop("return_tensors",   __lowercase  )

							lowercase__   					=			{
							    'input_ids': [],
							    'attention_mask': [],
							    'token_type_ids': [],
							}

							for idx, candidate_text in enumerate(__lowercase  ):
										if batch_text_pair is not None:
													lowercase__   					=			batch_text_pair[idx]
										else:
													lowercase__   					=			None

										lowercase__   					=			super().__call__(__lowercase,   __lowercase,   return_tensors=__lowercase,   **__lowercase  )

										lowercase__   					=			encoded_candidates.get("input_ids"  )
										lowercase__   					=			encoded_candidates.get("attention_mask"  )
										lowercase__   					=			encoded_candidates.get("token_type_ids"  )

										if encoded_input_ids is not None:
													output_data["input_ids"].append(__lowercase  )
										if encoded_attention_mask is not None:
													output_data["attention_mask"].append(__lowercase  )
										if encoded_token_type_ids is not None:
													output_data["token_type_ids"].append(__lowercase  )

							lowercase__   					=			{key: item for key, item in output_data.items() if len(__lowercase  ) != 0}

							return BatchEncoding(__lowercase,   tensor_type=__lowercase  )




				def 							A__ (  self   :  int,   __lowercase   :  Tuple,   __lowercase   :  Union[str, Any]=None  ):
							lowercase__   					=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
										output += token_ids_a + [self.sep_token_id]

							return output




				def 							A__ (  self   :  List[Any],   __lowercase   :  List[int],   __lowercase   :  Optional[List[int]] = None  ):
							lowercase__   					=			[self.sep_token_id]
							lowercase__   					=			[self.cls_token_id]
							if token_ids_a is None:
										return len(cls + token_ids_a + sep  ) * [0]
							return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




				def 							A__ (  self   :  List[str],   __lowercase   :  str,   __lowercase   :  Optional[str] = None  ):
							lowercase__   					=			self._tokenizer.model.save(__lowercase,   name=__lowercase  )
							return tuple(__lowercase  )









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       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""")
							lowercase_						=       parser.parse_args()

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

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

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

    # Transformer Blocks #
							lowercase_						=       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"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       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"]:
																																			lowercase_						=       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"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       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)




