code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       Any							)  ->	Any:
       __lowerCamelCase     :			str              =	TaConfig.from_pretrained(UpperCAmelCase_							)
       __lowerCamelCase     :			Union[str, Any]              =	FlaxTaForConditionalGeneration(config=UpperCAmelCase_							)
       __lowerCamelCase     :			Tuple              =	checkpoints.load_tax_checkpoint(UpperCAmelCase_							)
  
       __lowerCamelCase     :			str              =	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
  
       # Encoder
       for layer_index in range(config.num_layers							):
            __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'
  
            # Self-Attention
            __lowerCamelCase     :			Tuple              =	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
  
            if split_mlp_wi:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                 __lowerCamelCase     :			Optional[int]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
  
            __lowerCamelCase     :			Dict              =	tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Any              =	tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
  
            # Assigning
            __lowerCamelCase     :			int              =	tax_attention_key
            __lowerCamelCase     :			Dict              =	tax_attention_out
            __lowerCamelCase     :			int              =	tax_attention_query
            __lowerCamelCase     :			Dict              =	tax_attention_value
  
            __lowerCamelCase     :			Tuple              =	tax_attention_layer_norm
  
            if split_mlp_wi:
                 __lowerCamelCase     :			Optional[int]              =	tax_mlp_wi_a
                 __lowerCamelCase     :			List[Any]              =	tax_mlp_wi_a
            else:
                 __lowerCamelCase     :			Tuple              =	tax_mlp_wi
  
            __lowerCamelCase     :			str              =	tax_mlp_wo
            __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_layer_norm
  
       # Only for layer 0:
       __lowerCamelCase     :			Any              =	tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
       __lowerCamelCase     :			List[str]              =	tax_encoder_rel_embedding
  
       # Assigning
       __lowerCamelCase     :			List[Any]              =	tax_model['target']['encoder']['encoder_norm']['scale']
       __lowerCamelCase     :			str              =	tax_encoder_norm
  
       # Decoder
       for layer_index in range(config.num_decoder_layers							):
            __lowerCamelCase     :			Optional[Any]              =	F'layers_{str(UpperCAmelCase_							)}'
  
            # Self-Attention
            __lowerCamelCase     :			int              =	tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
            __lowerCamelCase     :			str              =	tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                'scale'
            ]
  
            # Encoder-Decoder-Attention
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
                'kernel'
            ]
            __lowerCamelCase     :			int              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                'kernel'
            ]
            __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                'kernel'
            ]
            __lowerCamelCase     :			Optional[Any]              =	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                'kernel'
            ]
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
  
            # MLP
            if split_mlp_wi:
                 __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                 __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                 __lowerCamelCase     :			List[Any]              =	tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
  
            __lowerCamelCase     :			Dict              =	tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
  
            # Layer Normalization
            __lowerCamelCase     :			Tuple              =	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
  
            # Assigning
            __lowerCamelCase     :			Any              =	tax_attention_key
            __lowerCamelCase     :			int              =	tax_attention_out
            __lowerCamelCase     :			List[str]              =	tax_attention_query
            __lowerCamelCase     :			Tuple              =	tax_attention_value
  
            __lowerCamelCase     :			int              =	tax_pre_attention_layer_norm
  
            __lowerCamelCase     :			Any              =	tax_enc_dec_attention_key
            __lowerCamelCase     :			str              =	tax_enc_dec_attention_out
            __lowerCamelCase     :			Dict              =	tax_enc_dec_attention_query
            __lowerCamelCase     :			List[str]              =	tax_enc_dec_attention_value
  
            __lowerCamelCase     :			Dict              =	tax_cross_layer_norm
  
            if split_mlp_wi:
                 __lowerCamelCase     :			Union[str, Any]              =	tax_mlp_wi_a
                 __lowerCamelCase     :			Dict              =	tax_mlp_wi_a
            else:
                 __lowerCamelCase     :			str              =	tax_mlp_wi
  
            __lowerCamelCase     :			Optional[int]              =	tax_mlp_wo
  
            __lowerCamelCase     :			int              =	txa_mlp_layer_norm
  
       # Decoder Normalization
       __lowerCamelCase     :			Union[str, Any]              =	tax_model['target']['decoder']['decoder_norm']['scale']
       __lowerCamelCase     :			List[str]              =	txa_decoder_norm
  
       # Only for layer 0:
       __lowerCamelCase     :			Optional[int]              =	tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
       __lowerCamelCase     :			List[Any]              =	tax_decoder_rel_embedding
  
       # Token Embeddings
       __lowerCamelCase     :			Optional[int]              =	tax_model['target']['token_embedder']['embedding']
       __lowerCamelCase     :			Dict              =	txa_token_embeddings
  
       # LM Head (only in v1.1 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
            __lowerCamelCase     :			Any              =	tax_model['target']['decoder']['logits_dense']['kernel']
  
       flax_model.save_pretrained(UpperCAmelCase_							)
       print('T5X Model was sucessfully converted!'							)
  
  
  if __name__ == "__main__":
        A__	:      str				=						argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
        )
        parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
        parser.add_argument(
            """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
        )
        A__	:      str				=						parser.parse_args()
        convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  
  
  
  
  
  
style_context: 
  
  
  '''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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  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()
  A__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[Any]              =	RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,					hidden_size=roberta.args.encoder_embed_dim   ,					num_hidden_layers=roberta.args.encoder_layers   ,					num_attention_heads=roberta.args.encoder_attention_heads   ,					intermediate_size=roberta.args.encoder_ffn_embed_dim   ,					max_position_embeddings=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
            __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
            # self attention
            __lowerCamelCase     :			BertSelfAttention              =	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     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
            # output
            __lowerCamelCase     :			BertOutput              =	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			str              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	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__":
        A__	:      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."""
        )
        A__	:      Optional[Any]				=						parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
  
  
  