






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  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=13 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=7 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Any=True ,							SCREAMING_SNAKE_CASE_     :   Dict=99 ,							SCREAMING_SNAKE_CASE_     :   Dict=32 ,							SCREAMING_SNAKE_CASE_     :   Any=5 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=4 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=37 ,							SCREAMING_SNAKE_CASE_     :   Dict="gelu" ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=512 ,							SCREAMING_SNAKE_CASE_     :   Any=16 ,							SCREAMING_SNAKE_CASE_     :   int=2 ,							SCREAMING_SNAKE_CASE_     :   str=0.02 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=4 ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_attention_mask
											lowerCAmelCase__      			=						use_token_type_ids
											lowerCAmelCase__      			=						use_labels
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						type_vocab_size
											lowerCAmelCase__      			=						type_sequence_label_size
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						num_choices




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

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

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

											lowerCAmelCase__      			=						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=SCREAMING_SNAKE_CASE_ ,							initializer_range=self.initializer_range ,							)

											return config, input_ids, token_type_ids, attention_mask




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						config_and_inputs
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict



@require_flax
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Tuple		      =    True

							UpperCamelCase_		:List[Any]		      =    (
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						FlaxRoFormerModelTester(self	)




							@slow
							def 						__snake_case					(   self     :   Optional[int]	):
											for model_class_name in self.all_model_classes:
															lowerCAmelCase__      			=						model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' ,							from_pt=SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						model(np.ones((1, 1)	)	)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



@require_flax
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
											lowerCAmelCase__      			=						jnp.array([[0, 1, 2, 3, 4, 5]]	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]

											lowerCAmelCase__      			=						50_000

											lowerCAmelCase__      			=						(1, 6, vocab_size)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						jnp.array(
											    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase			:		int   				=    {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GitForCausalLM",
			    "GitModel",
			    "GitPreTrainedModel",
			    "GitVisionModel",
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Optional[int]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





