



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 	__snake_case   ( unittest.TestCase ):
		def __init__(							self    ,a_    ,a_=13    ,a_=7    ,a_=True    ,a_=True    ,a_=True    ,a_=True    ,a_=99    ,a_=32    ,a_=5    ,a_=4    ,a_=37    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=16    ,a_=2    ,a_=0.02    ,a_=4    ,):







						"""simple docstring"""
						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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						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=a_    ,initializer_range=self.initializer_range    ,)

						return config, input_ids, token_type_ids, attention_mask



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						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 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	True

		SCREAMING_SNAKE_CASE__								=	(
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  FlaxRoFormerModelTester(self						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						for model_class_name in self.all_model_classes:
										lowerCAmelCase__		  =  model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,from_pt=a_						)
										lowerCAmelCase__		  =  model(np.ones((1, 1)						)						)
										self.assertIsNotNone(a_						)



@require_flax
class 	__snake_case   ( unittest.TestCase ):
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'						)
						lowerCAmelCase__		  =  jnp.array([[0, 1, 2, 3, 4, 5]]						)
						lowerCAmelCase__		  =  model(a_						)[0]

						lowerCAmelCase__		  =  5_0000

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

						lowerCAmelCase__		  =  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]    ,a_    ,atol=1e-4						)						)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
