





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=13	,			__lowerCAmelCase=7	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=99	,			__lowerCAmelCase=32	,			__lowerCAmelCase=5	,			__lowerCAmelCase=4	,			__lowerCAmelCase=37	,			__lowerCAmelCase="gelu"	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=512	,			__lowerCAmelCase=16	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=4	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      seq_length
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_attention_mask
														UpperCamelCase__					  =      use_token_type_ids
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      hidden_size
														UpperCamelCase__					  =      num_hidden_layers
														UpperCamelCase__					  =      num_attention_heads
														UpperCamelCase__					  =      intermediate_size
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      type_vocab_size
														UpperCamelCase__					  =      type_sequence_label_size
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      num_choices






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size	)

														UpperCamelCase__					  =      None
														if self.use_attention_mask:
																					UpperCamelCase__					  =      random_attention_mask([self.batch_size, self.seq_length]	)

														UpperCamelCase__					  =      None
														if self.use_token_type_ids:
																					UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.type_vocab_size	)

														UpperCamelCase__					  =      RoFormerConfig(
														    vocab_size=self.vocab_size	,			hidden_size=self.hidden_size	,			num_hidden_layers=self.num_hidden_layers	,			num_attention_heads=self.num_attention_heads	,			intermediate_size=self.intermediate_size	,			hidden_act=self.hidden_act	,			hidden_dropout_prob=self.hidden_dropout_prob	,			attention_probs_dropout_prob=self.attention_probs_dropout_prob	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			is_decoder=__lowerCAmelCase	,			initializer_range=self.initializer_range	,			)

														return config, input_ids, token_type_ids, attention_mask







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.prepare_config_and_inputs()
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      config_and_inputs
														UpperCamelCase__					  =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
														return config, inputs_dict






@require_flax
class 							__SCREAMING_SNAKE_CASE       (  _a ,		unittest.TestCase							):
							snake_case  :   Any      =							True

							snake_case  :   str      =							(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      FlaxRoFormerModelTester(self	)







							@slow
							def 					_lowerCamelCase		(    self	):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__					  =      model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,			from_pt=__lowerCAmelCase	)
																					UpperCamelCase__					  =      model(np.ones((1, 1)	)	)
																					self.assertIsNotNone(__lowerCAmelCase	)






@require_flax
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""	)
														UpperCamelCase__					  =      jnp.array([[0, 1, 2, 3, 4, 5]]	)
														UpperCamelCase__					  =      model(__lowerCAmelCase	)[0]

														UpperCamelCase__					  =      50000

														UpperCamelCase__					  =      (1, 6, vocab_size)
														self.assertEqual(output.shape	,			__lowerCAmelCase	)

														UpperCamelCase__					  =      jnp.array(
														    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]	)

														self.assertTrue(jnp.allclose(output[:, :3, :3]	,			__lowerCAmelCase	,			atol=1E-4	)	)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""gptsan-japanese"""
							snake_case  :   Any      =							[
							    """past_key_values""",
							]
							snake_case  :   str      =							{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(    self	,			__lowerCAmelCase=36000	,			__lowerCAmelCase=1280	,			__lowerCAmelCase=1024	,			__lowerCAmelCase=8192	,			__lowerCAmelCase=4096	,			__lowerCAmelCase=128	,			__lowerCAmelCase=10	,			__lowerCAmelCase=0	,			__lowerCAmelCase=16	,			__lowerCAmelCase=16	,			__lowerCAmelCase=128	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=1E-5	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase="float32"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.002	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			__lowerCAmelCase=35998	,			__lowerCAmelCase=35995	,			__lowerCAmelCase=35999	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      d_model
														UpperCamelCase__					  =      d_ff
														UpperCamelCase__					  =      d_ext
														UpperCamelCase__					  =      d_spout
														UpperCamelCase__					  =      num_switch_layers
														UpperCamelCase__					  =      num_ext_layers
														UpperCamelCase__					  =      num_switch_layers + num_ext_layers
														UpperCamelCase__					  =      num_heads
														UpperCamelCase__					  =      num_experts
														UpperCamelCase__					  =      expert_capacity
														UpperCamelCase__					  =      dropout_rate
														UpperCamelCase__					  =      layer_norm_epsilon
														UpperCamelCase__					  =      router_bias
														UpperCamelCase__					  =      router_jitter_noise
														UpperCamelCase__					  =      router_dtype
														UpperCamelCase__					  =      router_ignore_padding_tokens
														UpperCamelCase__					  =      output_hidden_states
														UpperCamelCase__					  =      output_attentions
														UpperCamelCase__					  =      initializer_factor
														UpperCamelCase__					  =      output_router_logits
														UpperCamelCase__					  =      use_cache

														super().__init__(
														    separator_token_id=__lowerCAmelCase	,			pad_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	,			)




