




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCamelCase    :		Dict			   =       logging.get_logger(__name__)







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def __init__(    self :  str						,							*A_ :  Optional[Any]						,							**A_ :  str  )       ->     int:

       """simple docstring"""






       super().__init__(*A_						,							**A_  )
       requires_backends(self						,							'vision'  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING  )

 def     a__			(    self :  List[str]						,							A_ :  List[Any]=None						,							A_ :  Any=None						,							A_ :  Union[str, Any]=None  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   {}
       lowerCamelCase_							=   {}

       if prompt is not None:
             lowerCamelCase_							=   prompt

       if generate_kwargs is not None:
             lowerCamelCase_							=   generate_kwargs
       if max_new_tokens is not None:
             if "generate_kwargs" not in forward_kwargs:
                   lowerCamelCase_							=   {}
             if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                   raise ValueError(
                       '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
                       ' please use only one'  )
             lowerCamelCase_							=   max_new_tokens
       return preprocess_params, forward_kwargs, {}

 def __call__(    self :  Dict						,							A_ :  Union[str, List[str], "Image.Image", List["Image.Image"]]						,							**A_ :  Dict  )       ->     Dict:

       """simple docstring"""






       return super().__call__(A_						,							**A_  )

 def     a__			(    self :  List[Any]						,							A_ :  str						,							A_ :  Optional[Any]=None  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   load_image(A_  )

       if prompt is not None:
             if not isinstance(A_						,							A_  ):
                   raise ValueError(
                       f"""Received an invalid text input, got - {type(A_  )} - but expected a single string. """
                       'Note also that one single text can be provided for conditional image to text generation.'  )

             lowerCamelCase_							=   self.model.config.model_type

             if model_type == "git":
                   lowerCamelCase_							=   self.image_processor(images=A_						,							return_tensors=self.framework  )
                   lowerCamelCase_							=   self.tokenizer(text=A_						,							add_special_tokens=A_  ).input_ids
                   lowerCamelCase_							=   [self.tokenizer.cls_token_id] + input_ids
                   lowerCamelCase_							=   torch.tensor(A_  ).unsqueeze(0  )
                   model_inputs.update({'input_ids': input_ids}  )

             elif model_type == "pix2struct":
                   lowerCamelCase_							=   self.image_processor(images=A_						,							header_text=A_						,							return_tensors=self.framework  )

             elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   lowerCamelCase_							=   self.image_processor(images=A_						,							return_tensors=self.framework  )
                   lowerCamelCase_							=   self.tokenizer(A_						,							return_tensors=self.framework  )
                   model_inputs.update(A_  )

             else:
                   raise ValueError(f"""Model type {model_type} does not support conditional text generation"""  )

       else:
             lowerCamelCase_							=   self.image_processor(images=A_						,							return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
             lowerCamelCase_							=   None

       return model_inputs

 def     a__			(    self :  Optional[Any]						,							A_ :  List[Any]						,							A_ :  Union[str, Any]=None  )       ->     List[str]:

       """simple docstring"""






       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs['input_ids']						,							A_  )
           and all(x is None for x in model_inputs['input_ids']  )
       ):
             lowerCamelCase_							=   None

       if generate_kwargs is None:
             lowerCamelCase_							=   {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       lowerCamelCase_							=   model_inputs.pop(self.model.main_input_name  )
       lowerCamelCase_							=   self.model.generate(A_						,							**A_						,							**A_  )
       return model_outputs





 def     a__			(    self :  Optional[int]						,							A_ :  int  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   []
       for output_ids in model_outputs:
             lowerCamelCase_							=   {
                 'generated_text': self.tokenizer.decode(
                     A_						,							skip_special_tokens=A_						,							)
             }
             records.append(A_  )
       return records









import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
 raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
lowerCamelCase    :		Tuple			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       "Hello world! cécé herlolip"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      roberta.eval()  # disable dropout
      lowerCamelCase_							=   roberta.model.encoder.sentence_encoder
      lowerCamelCase_							=   XLMRobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,							hidden_size=roberta.cfg.model.encoder_embed_dim     ,							num_hidden_layers=roberta.cfg.model.encoder_layers     ,							num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,							max_position_embeddings=5_14     ,							type_vocab_size=1     ,							layer_norm_eps=1e-5     ,							)
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowerCamelCase_							=   roberta_sent_encoder.embed_tokens.weight
      lowerCamelCase_							=   roberta_sent_encoder.embed_positions.weight
      lowerCamelCase_							=   torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.

      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.weight
      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.bias

      for i in range(config.num_hidden_layers					):
            # Encoder: start of layer
            lowerCamelCase_							=   model.roberta.encoder.layer[i]
            lowerCamelCase_							=   roberta_sent_encoder.layers[i]

            lowerCamelCase_							=   layer.attention
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.bias

            # self attention
            lowerCamelCase_							=   layer.attention.self
            assert (
                roberta_layer.self_attn.k_proj.weight.data.shape
                == roberta_layer.self_attn.q_proj.weight.data.shape
                == roberta_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)					)
            )

            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.bias

            # self-attention output
            lowerCamelCase_							=   layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            lowerCamelCase_							=   roberta_layer.final_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.final_layer_norm.bias

            # intermediate
            lowerCamelCase_							=   layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias

            # output
            lowerCamelCase_							=   layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            lowerCamelCase_							=   roberta_layer.fca.weight
            lowerCamelCase_							=   roberta_layer.fca.bias
            # end of layer

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

      # Let's check that we get the same results.
      lowerCamelCase_							=   roberta.encode(lowercase					).unsqueeze(0					)  # batch of size 1

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

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


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--roberta_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."
 )
 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



