






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase					:		Optional[int]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		Union[str, Any]	  =						{"""vocab_file""": """vocab.json"""}

__UpperCamelCase					:		Any	  =						{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

__UpperCamelCase					:		Optional[Any]	  =						{"""mgp-str""": 27}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       VOCAB_FILES_NAMES
							_UpperCAmelCase     =       PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self:      int       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[int]="[GO]"       ,	UpperCamelCase:      int="[GO]"       ,	UpperCamelCase:      Union[str, Any]="[s]"       ,	UpperCamelCase:      Optional[Any]="[GO]"       ,	**UpperCamelCase:      List[Any]						)		->		Optional[int]:
											super().__init__(
											    unk_token=UpperCamelCase       ,	bos_token=UpperCamelCase       ,	eos_token=UpperCamelCase       ,	pad_token=UpperCamelCase       ,	**UpperCamelCase       ,	)

											with open(UpperCamelCase       ,	encoding='utf-8'						) as vocab_handle:
															snake_case__									=     json.load(UpperCamelCase						)
											snake_case__									=     {v: k for k, v in self.vocab.items()}
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[str]:
											return len(self.vocab						)
							def lowerCAmelCase_ (     self:      Any						)		->		Dict:
											return dict(self.vocab       ,	**self.added_tokens_encoder						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      int						)		->		Optional[Any]:
											snake_case__									=     []
											for s in text:
															char_tokens.extend(UpperCamelCase						)
											return char_tokens
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      Optional[Any]						)		->		str:
											return self.vocab.get(UpperCamelCase       ,	self.vocab.get(self.unk_token						)						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Any						)		->		Dict:
											return self.decoder.get(UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Optional[str] = None						)		->		Tuple[str]:
											if not os.path.isdir(UpperCamelCase						):
															logger.error('Vocabulary path ({}) should be a directory'.format(UpperCamelCase						)						)
															return
											snake_case__									=     os.path.join(
											    UpperCamelCase       ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

											with open(UpperCamelCase       ,	'w'       ,	encoding='utf-8'						) as f:
															f.write(json.dumps(self.vocab       ,	indent=2       ,	sort_keys=UpperCamelCase       ,	ensure_ascii=UpperCamelCase						) + '\n'						)

											return (vocab_file,)












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def __init__(     self:      List[str]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=3       ,	UpperCamelCase:      Union[str, Any]=32       ,	UpperCamelCase:      Optional[int]=3       ,	UpperCamelCase:      Any=10       ,	UpperCamelCase:      List[str]=[10, 20, 30, 40]       ,	UpperCamelCase:      Optional[Any]=[1, 1, 2, 1]       ,	UpperCamelCase:      Tuple=True       ,	UpperCamelCase:      List[str]=True       ,	UpperCamelCase:      Optional[int]="relu"       ,	UpperCamelCase:      List[str]=3       ,	UpperCamelCase:      Dict=None       ,	)		->		Optional[Any]:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     image_size
											snake_case__									=     num_channels
											snake_case__									=     embeddings_size
											snake_case__									=     hidden_sizes
											snake_case__									=     depths
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     hidden_act
											snake_case__									=     num_labels
											snake_case__									=     scope
											snake_case__									=     len(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case__									=     self.get_config()

											return config, pixel_values
							def lowerCAmelCase_ (     self:      int						)		->		Optional[Any]:
											return RegNetConfig(
											    num_channels=self.num_channels       ,	embeddings_size=self.embeddings_size       ,	hidden_sizes=self.hidden_sizes       ,	depths=self.depths       ,	hidden_act=self.hidden_act       ,	num_labels=self.num_labels       ,	image_size=self.image_size       ,	)
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      List[Any]						)		->		Optional[Any]:
											snake_case__									=     FlaxRegNetModel(config=UpperCamelCase						)
											snake_case__									=     model(UpperCamelCase						)

											# Output shape (b, c, h, w)
											self.parent.assertEqual(
											    result.last_hidden_state.shape       ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,	)
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Optional[Any]						)		->		List[str]:
											snake_case__									=     self.num_labels
											snake_case__									=     FlaxRegNetForImageClassification(config=UpperCamelCase						)
											snake_case__									=     model(UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		List[str]:
											snake_case__									=     self.prepare_config_and_inputs()
											snake_case__							,						snake_case__									=     config_and_inputs
											snake_case__									=     {'pixel_values': pixel_values}
											return config, inputs_dict


@require_flax
class   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Dict						)		->		None:
											snake_case__									=     FlaxRegNetModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=UpperCamelCase       ,	has_text_modality=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											return
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Union[str, Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase						)
							@unittest.skip(reason='RegNet does not use inputs_embeds'						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Dict:
											pass
							@unittest.skip(reason='RegNet does not support input and output embeddings'						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Dict:
											pass
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     model_class(UpperCamelCase						)
															snake_case__									=     inspect.signature(model.__call__						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case__									=     [*signature.parameters.keys()]

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		int:
											def check_hidden_states_output(UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      List[Any]						):
															snake_case__									=     model_class(UpperCamelCase						)

															snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)

															snake_case__									=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															snake_case__									=     self.model_tester.num_stages
															self.assertEqual(len(UpperCamelCase						)       ,	expected_num_stages + 1						)

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     True
															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case__									=     True

															check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      str						)		->		str:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																			snake_case__									=     self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)
																			snake_case__									=     model_class(UpperCamelCase						)

																			@jax.jit
																			def model_jitted(UpperCamelCase:      str       ,	**UpperCamelCase:      Tuple						):
																							return model(pixel_values=UpperCamelCase       ,	**UpperCamelCase						)

																			with self.subTest('JIT Enabled'						):
																							snake_case__									=     model_jitted(**UpperCamelCase						).to_tuple()

																			with self.subTest('JIT Disabled'						):
																							with jax.disable_jit():
																											snake_case__									=     model_jitted(**UpperCamelCase						).to_tuple()

																			self.assertEqual(len(UpperCamelCase						)       ,	len(UpperCamelCase						)						)
																			for jitted_output, output in zip(UpperCamelCase       ,	UpperCamelCase						):
																							self.assertEqual(jitted_output.shape       ,	output.shape						)


def      a_ (      )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
				return image


@require_flax
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      str						)		->		Any:
											return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'						) if is_vision_available() else None


							@slow
							def lowerCAmelCase_ (     self:      List[str]						)		->		Optional[Any]:
											snake_case__									=     FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'						)

											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='np'						)

											snake_case__									=     model(**UpperCamelCase						)

											# verify the logits
											snake_case__									=     (1, 10_00)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)

											snake_case__									=     jnp.array([-0.4_180, -1.5_051, -3.4_836]						)

											self.assertTrue(jnp.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)




