
import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
						    FlaxAlbertForMaskedLM,
						    FlaxAlbertForMultipleChoice,
						    FlaxAlbertForPreTraining,
						    FlaxAlbertForQuestionAnswering,
						    FlaxAlbertForSequenceClassification,
						    FlaxAlbertForTokenClassification,
						    FlaxAlbertModel,
						)
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict=1_3       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=7       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=9_9       ,							SCREAMING_SNAKE_CASE_  :			Tuple=3_2       ,							SCREAMING_SNAKE_CASE_  :			int=5       ,							SCREAMING_SNAKE_CASE_  :			Dict=4       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=3_7       ,							SCREAMING_SNAKE_CASE_  :			Tuple="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			int=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Tuple=4       ,							)       ->     Dict:
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					seq_length
					lowercase_       =					is_training
					lowercase_       =					use_attention_mask
					lowercase_       =					use_token_type_ids
					lowercase_       =					use_labels
					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					type_sequence_label_size
					lowercase_       =					initializer_range
					lowercase_       =					num_choices
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Union[str, Any]:
					lowercase_       =					ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

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

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

					lowercase_       =					AlbertConfig(
					    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 						_lowercase  (       self  :			Optional[Any]       )       ->     List[str]:
					lowercase_       =					self.prepare_config_and_inputs()
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					config_and_inputs
					lowercase_       =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	(
	    (
	        FlaxAlbertModel,
	        FlaxAlbertForPreTraining,
	        FlaxAlbertForMaskedLM,
	        FlaxAlbertForMultipleChoice,
	        FlaxAlbertForQuestionAnswering,
	        FlaxAlbertForSequenceClassification,
	        FlaxAlbertForTokenClassification,
	        FlaxAlbertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					lowercase_       =					FlaxAlbertModelTester(self       )




	@slow
	def 						_lowercase  (       self  :			List[Any]       )       ->     int:
					for model_class_name in self.all_model_classes:
									lowercase_       =					model_class_name.from_pretrained('''albert-base-v2'''       )
									lowercase_       =					model(np.ones((1, 1)       )       )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )

@require_flax
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@slow
	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					lowercase_       =					FlaxAlbertModel.from_pretrained('''albert-base-v2'''       )
					lowercase_       =					np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]       )
					lowercase_       =					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_       )[0]
					lowercase_       =					(1, 1_1, 7_6_8)
					self.assertEqual(output.shape       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					np.array(
					    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]       )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




