

"""simple docstring"""





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
				raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE       :  str        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  str        = """Hello world! cécé herlolip"""



def        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							str      ,							UpperCAmelCase     :							bool				):


					'''simple docstring'''






					UpperCamelCase__  ,			UpperCamelCase__		:    Tuple         =os.path.split(UpperCAmelCase				)
					UpperCamelCase__		:    Any         =DataaVecTextModel.from_pretrained(
					    UpperCAmelCase      ,							checkpoint_file=UpperCAmelCase				)
					dataavec.eval()  # disable dropout
					UpperCamelCase__		:    Dict         =dataavec.models[0]
					UpperCamelCase__		:    Union[str, Any]         =dataavec_model.encoder.sentence_encoder
					UpperCamelCase__		:    Tuple         =DataaVecTextConfig(
					    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings      ,							hidden_size=dataavec_model.args.encoder_embed_dim      ,							num_hidden_layers=dataavec_model.args.encoder_layers      ,							num_attention_heads=dataavec_model.args.encoder_attention_heads      ,							intermediate_size=dataavec_model.args.encoder_ffn_embed_dim      ,							max_position_embeddings=514      ,							type_vocab_size=1      ,							layer_norm_eps=1E-5      ,							)
					if classification_head:
										UpperCamelCase__		:    Dict         =dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
					print('''Our BERT config:'''      ,							UpperCAmelCase				)

					UpperCamelCase__		:    int         =DataaVecTextForSequenceClassification(UpperCAmelCase				) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase				)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					UpperCamelCase__		:    str         =dataavec_sent_encoder.embed_tokens.weight
					UpperCamelCase__		:    Optional[Any]         =dataavec_sent_encoder.embed_positions.weight
					UpperCamelCase__		:    str         =torch.zeros_like(
					    model.dataavec_text.embeddings.token_type_embeddings.weight				)  # just zero them out b/c data2vec doesn't use them.
					UpperCamelCase__		:    Dict         =dataavec_sent_encoder.layernorm_embedding.weight
					UpperCamelCase__		:    List[Any]         =dataavec_sent_encoder.layernorm_embedding.bias

					for i in range(config.num_hidden_layers				):
										# Encoder: start of layer
										UpperCamelCase__		:    BertLayer         =model.dataavec_text.encoder.layer[i]
										UpperCamelCase__		:    TransformerSentenceEncoderLayer         =dataavec_sent_encoder.layers[i]

										# self attention
										UpperCamelCase__		:    BertSelfAttention         =layer.attention.self
										assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)				), (
										    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size)				)}'''
										)
										assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)				), (
										    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size)				)}'''
										)
										assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)				), (
										    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
										    F''' {torch.Size((config.hidden_size, config.hidden_size)				)}'''
										)

										UpperCamelCase__		:    Dict         =dataavec_layer.self_attn.q_proj.weight
										UpperCamelCase__		:    Optional[Any]         =dataavec_layer.self_attn.q_proj.bias
										UpperCamelCase__		:    Any         =dataavec_layer.self_attn.k_proj.weight
										UpperCamelCase__		:    List[Any]         =dataavec_layer.self_attn.k_proj.bias
										UpperCamelCase__		:    Union[str, Any]         =dataavec_layer.self_attn.v_proj.weight
										UpperCamelCase__		:    Optional[Any]         =dataavec_layer.self_attn.v_proj.bias

										# self-attention output
										UpperCamelCase__		:    BertSelfOutput         =layer.attention.output
										assert (
										    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
										), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
										UpperCamelCase__		:    Union[str, Any]         =dataavec_layer.self_attn.out_proj.weight
										UpperCamelCase__		:    str         =dataavec_layer.self_attn.out_proj.bias
										UpperCamelCase__		:    int         =dataavec_layer.self_attn_layer_norm.weight
										UpperCamelCase__		:    List[str]         =dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										UpperCamelCase__		:    BertIntermediate         =layer.intermediate
										assert (
										    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
										), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										UpperCamelCase__		:    List[str]         =dataavec_layer.fca.weight
										UpperCamelCase__		:    Optional[Any]         =dataavec_layer.fca.bias

										# output
										UpperCamelCase__		:    BertOutput         =layer.output
										assert (
										    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
										), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
										UpperCamelCase__		:    Dict         =dataavec_layer.fca.weight
										UpperCamelCase__		:    Dict         =dataavec_layer.fca.bias
										UpperCamelCase__		:    Union[str, Any]         =dataavec_layer.final_layer_norm.weight
										UpperCamelCase__		:    str         =dataavec_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										UpperCamelCase__		:    Optional[Any]         =dataavec.model.classification_heads['''mnli'''].dense.weight
										UpperCamelCase__		:    Tuple         =dataavec.model.classification_heads['''mnli'''].dense.bias
										UpperCamelCase__		:    List[str]         =dataavec.model.classification_heads['''mnli'''].out_proj.weight
										UpperCamelCase__		:    Optional[Any]         =dataavec.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										UpperCamelCase__		:    Optional[Any]         =dataavec_model.encoder.lm_head.dense.weight
										UpperCamelCase__		:    List[str]         =dataavec_model.encoder.lm_head.dense.bias
										UpperCamelCase__		:    Union[str, Any]         =dataavec_model.encoder.lm_head.layer_norm.weight
										UpperCamelCase__		:    Union[str, Any]         =dataavec_model.encoder.lm_head.layer_norm.bias
										UpperCamelCase__		:    Dict         =dataavec_model.encoder.lm_head.weight
										UpperCamelCase__		:    Union[str, Any]         =dataavec_model.encoder.lm_head.bias

					# Let's check that we get the same results.
					UpperCamelCase__		:    torch.Tensor         =dataavec.encode(UpperCAmelCase				).unsqueeze(0				)  # batch of size 1

					UpperCamelCase__		:    Union[str, Any]         =model(UpperCAmelCase				)[0]
					if classification_head:
										UpperCamelCase__		:    int         =dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCAmelCase				)				)
					else:
										UpperCamelCase__		:    List[str]         =dataavec_model(UpperCAmelCase				)[0]
					print(our_output.shape      ,							their_output.shape				)
					UpperCamelCase__		:    Dict         =torch.max(torch.abs(our_output - their_output				)				).item()
					print(F'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
					UpperCamelCase__		:    Dict         =torch.allclose(UpperCAmelCase      ,							UpperCAmelCase      ,							atol=1E-3				)
					print('''Do both models output the same tensors?'''      ,							'''🔥''' if success else '''💩'''				)
					if not success:
										raise Exception('''Something went wRoNg'''				)

					pathlib.Path(UpperCAmelCase				).mkdir(parents=UpperCAmelCase      ,							exist_ok=UpperCAmelCase				)
					print(F'''Saving model to {pytorch_dump_folder_path}'''				)
					model.save_pretrained(UpperCAmelCase				)


if __name__ == "__main__":
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
				)
				_SCREAMING_SNAKE_CASE       :  int        = parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


