





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class        __SCREAMING_SNAKE_CASE					(     _UpperCAmelCase       ):
							snake_case     :      Tuple						     =       """t5"""
							snake_case     :      Union[str, Any]						     =       ["""past_key_values"""]
							snake_case     :      List[str]						     =       {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






							def __init__(						self  , __lowerCAmelCase=32128  , __lowerCAmelCase=512  , __lowerCAmelCase=64  , __lowerCAmelCase=2048  , __lowerCAmelCase=6  , __lowerCAmelCase=None  , __lowerCAmelCase=8  , __lowerCAmelCase=32  , __lowerCAmelCase=128  , __lowerCAmelCase=0.1  , __lowerCAmelCase=1E-6  , __lowerCAmelCase=1.0  , __lowerCAmelCase="relu"  , __lowerCAmelCase=True  , __lowerCAmelCase=True  , __lowerCAmelCase=0  , __lowerCAmelCase=1  , **__lowerCAmelCase  , ):
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			d_model
														UpperCamelCase__				     =			d_kv
														UpperCamelCase__				     =			d_ff
														UpperCamelCase__				     =			num_layers
														UpperCamelCase__				     =			(
														    num_decoder_layers if num_decoder_layers is not None else self.num_layers
														)  # default = symmetry
														UpperCamelCase__				     =			num_heads
														UpperCamelCase__				     =			relative_attention_num_buckets
														UpperCamelCase__				     =			relative_attention_max_distance
														UpperCamelCase__				     =			dropout_rate
														UpperCamelCase__				     =			layer_norm_epsilon
														UpperCamelCase__				     =			initializer_factor
														UpperCamelCase__				     =			feed_forward_proj
														UpperCamelCase__				     =			use_cache

														UpperCamelCase__				     =			self.feed_forward_proj.split("""-"""  )
														UpperCamelCase__				     =			act_info[-1]
														UpperCamelCase__				     =			act_info[0] == '''gated'''

														if len(lowerCamelCase_  ) > 1 and act_info[0] != "gated" or len(lowerCamelCase_  ) > 2:
																					raise ValueError(
																					    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																					    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
																					    """\'gated-gelu\' or \'relu\'"""  )

														# for backwards compatibility
														if feed_forward_proj == "gated-gelu":
																					UpperCamelCase__				     =			'''gelu_new'''

														super().__init__(
														    pad_token_id=lowerCamelCase_  , eos_token_id=lowerCamelCase_  , is_encoder_decoder=lowerCamelCase_  , **lowerCamelCase_  , )






class        __SCREAMING_SNAKE_CASE					(     _UpperCAmelCase       ):






							@property
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{
														    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
														    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
														}
														if self.use_past:
																					UpperCamelCase__				     =			'''past_encoder_sequence + sequence'''
																					UpperCamelCase__				     =			{0: '''batch'''}
																					UpperCamelCase__				     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
														else:
																					UpperCamelCase__				     =			{0: '''batch''', 1: '''decoder_sequence'''}
																					UpperCamelCase__				     =			{0: '''batch''', 1: '''decoder_sequence'''}

														if self.use_past:
																					self.fill_with_past_key_values_(lowerCamelCase_  , direction="""inputs"""  )

														return common_inputs







							@property
							def 				_lowerCamelCase		(						self  ):
														return 13











import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					with self.subTest(__lowerCAmelCase  ):
																												UpperCamelCase__				     =			AutoConfig.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )

																												UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  )
																												self.assertIsNotNone(__lowerCAmelCase  )
																												self.assertIsInstance(__lowerCAmelCase  , __lowerCAmelCase  )






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["bert-base-cased", "bert-large-uncased"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxBertModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in ["roberta-base", "roberta-large"]:
																					UpperCamelCase__				     =			AutoTokenizer.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxRobertaModel.from_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			tokenizer("""Do you support jax jitted function?"""  , return_tensors=TensorType.JAX  )

																					@jax.jit
																					def eval(**__lowerCAmelCase  ):
																												return model(**__lowerCAmelCase  )

																					eval(**__lowerCAmelCase  ).block_until_ready()






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """bert-base is not a local folder and is not a valid model identifier"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""bert-base"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained(__lowerCAmelCase  , revision="""aaaaaa"""  )






							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(
														    __lowerCAmelCase  , """hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"""  , ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""  )







							def 				_lowerCamelCase		(						self  ):
														with self.assertRaisesRegex(__lowerCAmelCase  , """Use `from_pt=True` to load this model"""  ):
																					UpperCamelCase__				     =			FlaxAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""  )




