



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM





@require_torch
class 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
          _lowerCAmelCase 			=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowercase__			)
          _lowerCAmelCase 			=  -1

          _lowerCAmelCase 			=  ids_tensor((1, 5)       ,     vocab_size=model.config.vocab_size			).to(lowercase__			)
          _lowerCAmelCase 			=  model.generate(lowercase__       ,     max_new_tokens=10       ,     do_sample=lowercase__			)
          _lowerCAmelCase 			=  tokenizer.decode(greedy_ids[0]			)

          with CaptureStdout() as cs:
                 _lowerCAmelCase 			=  TextStreamer(lowercase__			)
                 model.generate(lowercase__       ,     max_new_tokens=10       ,     do_sample=lowercase__       ,     streamer=lowercase__			)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          _lowerCAmelCase 			=  cs.out[:-1]

          self.assertEqual(lowercase__       ,     lowercase__			)





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
          _lowerCAmelCase 			=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowercase__			)
          _lowerCAmelCase 			=  -1

          _lowerCAmelCase 			=  ids_tensor((1, 5)       ,     vocab_size=model.config.vocab_size			).to(lowercase__			)
          _lowerCAmelCase 			=  model.generate(lowercase__       ,     max_new_tokens=10       ,     do_sample=lowercase__			)
          _lowerCAmelCase 			=  tokenizer.decode(greedy_ids[0]			)

          _lowerCAmelCase 			=  TextIteratorStreamer(lowercase__			)
          _lowerCAmelCase 			=  {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          _lowerCAmelCase 			=  Thread(target=model.generate       ,     kwargs=lowercase__			)
          thread.start()
          _lowerCAmelCase 			=  ""
          for new_text in streamer:
                 streamer_text += new_text

          self.assertEqual(lowercase__       ,     lowercase__			)





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
          _lowerCAmelCase 			=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowercase__			)
          _lowerCAmelCase 			=  -1

          _lowerCAmelCase 			=  ids_tensor((1, 5)       ,     vocab_size=model.config.vocab_size			).to(lowercase__			)
          _lowerCAmelCase 			=  model.generate(lowercase__       ,     max_new_tokens=10       ,     do_sample=lowercase__			)
          _lowerCAmelCase 			=  greedy_ids[:, input_ids.shape[1] :]
          _lowerCAmelCase 			=  tokenizer.decode(new_greedy_ids[0]			)

          with CaptureStdout() as cs:
                 _lowerCAmelCase 			=  TextStreamer(lowercase__       ,     skip_prompt=lowercase__			)
                 model.generate(lowercase__       ,     max_new_tokens=10       ,     do_sample=lowercase__       ,     streamer=lowercase__			)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          _lowerCAmelCase 			=  cs.out[:-1]

          self.assertEqual(lowercase__       ,     lowercase__			)





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''distilgpt2'''			)
          _lowerCAmelCase 			=  AutoModelForCausalLM.from_pretrained('''distilgpt2'''			).to(lowercase__			)
          _lowerCAmelCase 			=  -1

          _lowerCAmelCase 			=  torch.ones((1, 5)       ,     device=lowercase__			).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
                 _lowerCAmelCase 			=  TextStreamer(lowercase__       ,     skip_special_tokens=lowercase__			)
                 model.generate(lowercase__       ,     max_new_tokens=1       ,     do_sample=lowercase__       ,     streamer=lowercase__			)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          _lowerCAmelCase 			=  cs.out[:-1]  # Remove the final "\n"
          _lowerCAmelCase 			=  tokenizer(lowercase__       ,     return_tensors='''pt'''			)
          self.assertEqual(streamer_text_tokenized.input_ids.shape       ,     (1, 1)			)





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			)
          _lowerCAmelCase 			=  AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''			).to(lowercase__			)
          _lowerCAmelCase 			=  -1

          _lowerCAmelCase 			=  ids_tensor((1, 5)       ,     vocab_size=model.config.vocab_size			).to(lowercase__			)
          _lowerCAmelCase 			=  TextIteratorStreamer(lowercase__       ,     timeout=0.001			)
          _lowerCAmelCase 			=  {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          _lowerCAmelCase 			=  Thread(target=model.generate       ,     kwargs=lowercase__			)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(lowercase__			):
                 _lowerCAmelCase 			=  ""
                 for new_text in streamer:
                        streamer_text += new_text




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()
UpperCAmelCase_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							"Hello world! cécé herlolip"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	bool				)->List[Any]:
      _lowerCAmelCase 			=  FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
      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_1_4     ,				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:'''     ,				_SCREAMING_SNAKE_CASE				)

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

      _lowerCAmelCase 			=  model(_SCREAMING_SNAKE_CASE				)[0]
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(_SCREAMING_SNAKE_CASE				)				)
      else:
            _lowerCAmelCase 			=  roberta.model(_SCREAMING_SNAKE_CASE				)[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(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				atol=1e-3				)
      print('''Do both models output the same tensors?'''     ,				'''🔥''' if success else '''💩'''				)
      if not success:
            raise Exception('''Something went wRoNg'''				)

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


if __name__ == "__main__":
    UpperCAmelCase_									=							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."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )