





'''simple docstring'''






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()
A__    :       Tuple              =logging.get_logger(__name__)

A__    :       Optional[int]              ='''Hello world! cécé herlolip'''
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							FairseqRobertaModel.from_pretrained(lowerCAmelCase  )
      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:"""							,     lowerCAmelCase  )

      _lowerCAmelCase												=							XLMRobertaXLForSequenceClassification(lowerCAmelCase  ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase  )
      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(lowerCAmelCase  ).unsqueeze(0  )  # batch of size 1

      _lowerCAmelCase												=							model(lowerCAmelCase  )[0]
      if classification_head:
            _lowerCAmelCase												=							roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCAmelCase  )  )
      else:
            _lowerCAmelCase												=							roberta.model(lowerCAmelCase  )[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(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__":
      A__    :       Union[str, 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.'''
      )
      A__    :       Dict              =parser.parse_args()
      convert_xlm_roberta_xl_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )













'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase												=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
            # fmt: on
            _lowerCAmelCase												=							dict(zip(__snake_case      ,      range(len(__snake_case  )  )  )  )
            _lowerCAmelCase												=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase												=							{"""unk_token""": """<unk>"""}

            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]  )
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""merges_file"""]  )
            with open(self.vocab_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write(json.dumps(__snake_case  ) + """\n"""  )
            with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write("""\n""".join(__snake_case  )  )

            _lowerCAmelCase												=							{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      __snake_case  )
            with open(self.image_processor_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  json.dump(__snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				List[str]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      **__snake_case      :			Any  )    ->				List[Any]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      **__snake_case      :			List[str]  )    ->				int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            shutil.rmtree(self.tmpdirname  )




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							[np.random.randint(2_55      ,      size=(3, 30, 4_00)      ,      dtype=np.uinta  )]

            _lowerCAmelCase												=							[Image.fromarray(np.moveaxis(__snake_case      ,      0      ,      -1  )  ) for x in image_inputs]

            return image_inputs




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.get_tokenizer()
            _lowerCAmelCase												=							self.get_rust_tokenizer()
            _lowerCAmelCase												=							self.get_image_processor()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_slow.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__snake_case  )

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_fast.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname  )

            self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()  )
            self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertIsInstance(processor_slow.tokenizer      ,      __snake_case  )
            self.assertIsInstance(processor_fast.tokenizer      ,      __snake_case  )

            self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertIsInstance(processor_slow.image_processor      ,      __snake_case  )
            self.assertIsInstance(processor_fast.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Dict:
            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

            _lowerCAmelCase												=							self.get_tokenizer(bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""  )
            _lowerCAmelCase												=							self.get_image_processor(do_normalize=__snake_case      ,      padding_value=1.0  )

            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(
                self.tmpdirname      ,      bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""      ,      do_normalize=__snake_case      ,      padding_value=1.0  )

            self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()  )
            self.assertIsInstance(processor.tokenizer      ,      __snake_case  )

            self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()  )
            self.assertIsInstance(processor.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							image_processor(__snake_case      ,      return_tensors="""np"""  )
            _lowerCAmelCase												=							processor(images=__snake_case      ,      return_tensors="""np"""  )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2  )




      def     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""

            _lowerCAmelCase												=							processor(text=__snake_case  )

            _lowerCAmelCase												=							tokenizer(__snake_case  )

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(text=__snake_case      ,      images=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""input_ids""", """attention_mask""", """pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(images=__snake_case      ,      visual_prompt=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""pixel_values""", """conditional_pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCAmelCase												=							processor.batch_decode(__snake_case  )
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )

            self.assertListEqual(__snake_case      ,      __snake_case  )






