"""simple docstring"""







import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
	import jax.numpy as jnp

	from transformers import FlaxXLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_flax
class 	UpperCamelCase    (					unittest.TestCase		):



				@slow
				def       _lowercase  (self       :   List[str])					->			List[Any]:
											__snake_case    :							Any   	=				FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base')
											__snake_case    :							Dict   	=				AutoTokenizer.from_pretrained('xlm-roberta-base')
											__snake_case    :							Any   	=				'The dog is cute and lives in the garden house'
											__snake_case    :							Optional[int]   	=				jnp.array([tokenizer.encode(__UpperCamelCase)])

											__snake_case    :							int   	=				(1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
											__snake_case    :							Tuple   	=				jnp.array(
											    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]])

											__snake_case    :							Dict   	=				model(__UpperCamelCase)['last_hidden_state']
											self.assertEqual(output.shape			,						__UpperCamelCase)
											# compare the actual values for a slice of last dim
											self.assertTrue(jnp.allclose(output[:, :, -1]			,						__UpperCamelCase			,						atol=1E-3))






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





