




'''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()
UpperCAmelCase_				:     Union[str, Any]     			=						logging.get_logger(__name__)

UpperCAmelCase_				:     Any     			=						'''Hello world! cécé herlolip'''







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   bool		)->      Dict:




     '''simple docstring'''





     __snake_case    ,			__snake_case			    =     os.path.split(_lowerCamelCase		)
     __snake_case			    =     DataaVecTextModel.from_pretrained(
         _lowerCamelCase      ,		checkpoint_file=_lowerCamelCase		)
     dataavec.eval()  # disable dropout
     __snake_case			    =     dataavec.models[0]
     __snake_case			    =     dataavec_model.encoder.sentence_encoder
     __snake_case			    =     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=5_14      ,		type_vocab_size=1      ,		layer_norm_eps=1E-5      ,		)
     if classification_head:
          __snake_case			    =     dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''      ,		_lowerCamelCase		)

     __snake_case			    =     DataaVecTextForSequenceClassification(_lowerCamelCase		) if classification_head else DataaVecTextForMaskedLM(_lowerCamelCase		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __snake_case			    =     dataavec_sent_encoder.embed_tokens.weight
     __snake_case			    =     dataavec_sent_encoder.embed_positions.weight
     __snake_case			    =     torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
     __snake_case			    =     dataavec_sent_encoder.layernorm_embedding.weight
     __snake_case			    =     dataavec_sent_encoder.layernorm_embedding.bias

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

          # self attention
          __snake_case			    =     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)		)}'''
          )

          __snake_case			    =     dataavec_layer.self_attn.q_proj.weight
          __snake_case			    =     dataavec_layer.self_attn.q_proj.bias
          __snake_case			    =     dataavec_layer.self_attn.k_proj.weight
          __snake_case			    =     dataavec_layer.self_attn.k_proj.bias
          __snake_case			    =     dataavec_layer.self_attn.v_proj.weight
          __snake_case			    =     dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          __snake_case			    =     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}'''
          __snake_case			    =     dataavec_layer.self_attn.out_proj.weight
          __snake_case			    =     dataavec_layer.self_attn.out_proj.bias
          __snake_case			    =     dataavec_layer.self_attn_layer_norm.weight
          __snake_case			    =     dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          __snake_case			    =     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}'''
          __snake_case			    =     dataavec_layer.fca.weight
          __snake_case			    =     dataavec_layer.fca.bias

          # output
          __snake_case			    =     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}'''
          __snake_case			    =     dataavec_layer.fca.weight
          __snake_case			    =     dataavec_layer.fca.bias
          __snake_case			    =     dataavec_layer.final_layer_norm.weight
          __snake_case			    =     dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          __snake_case			    =     dataavec.model.classification_heads['''mnli'''].dense.weight
          __snake_case			    =     dataavec.model.classification_heads['''mnli'''].dense.bias
          __snake_case			    =     dataavec.model.classification_heads['''mnli'''].out_proj.weight
          __snake_case			    =     dataavec.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          __snake_case			    =     dataavec_model.encoder.lm_head.dense.weight
          __snake_case			    =     dataavec_model.encoder.lm_head.dense.bias
          __snake_case			    =     dataavec_model.encoder.lm_head.layer_norm.weight
          __snake_case			    =     dataavec_model.encoder.lm_head.layer_norm.bias
          __snake_case			    =     dataavec_model.encoder.lm_head.weight
          __snake_case			    =     dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __snake_case			    =     dataavec.encode(_lowerCamelCase		).unsqueeze(0		)  # batch of size 1

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

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


if __name__ == "__main__":
     UpperCAmelCase_				:     List[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.'''
     )
     UpperCAmelCase_				:     Any     			=						parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )












'''simple docstring'''



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase_				:     int     			=						get_logger(__name__)







def 	_UpperCamelCase       (_lowerCamelCase							:   Union[str, Any]		)->      Tuple:




     '''simple docstring'''





     if version.parse(_lowerCamelCase		) < version.parse(_lowerCamelCase		):
          if "dev" in min_version:
               __snake_case			    =     (
                   '''This example requires a source install from HuggingFace diffusers (see '''
                   '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
               )
          else:
               __snake_case			    =     f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(_lowerCamelCase		)






