


import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=3			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3			,						__lowerCamelCase=1_0			,						__lowerCamelCase=[1_0, 2_0, 3_0, 4_0]			,						__lowerCamelCase=[1, 1, 2, 1]			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase="relu"			,						__lowerCamelCase=3			,						__lowerCamelCase=None			,						)      ->    Dict:
            _A				:	List[Any]       =			parent
            _A				:	Union[str, Any]       =			batch_size
            _A				:	Any       =			image_size
            _A				:	Dict       =			num_channels
            _A				:	Union[str, Any]       =			embeddings_size
            _A				:	Tuple       =			hidden_sizes
            _A				:	Any       =			depths
            _A				:	List[Any]       =			is_training
            _A				:	List[str]       =			use_labels
            _A				:	str       =			hidden_act
            _A				:	Dict       =			num_labels
            _A				:	Dict       =			scope
            _A				:	Dict       =			len(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	Dict       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

            _A				:	str       =			self.get_config()

            return config, pixel_values


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            return ResNetConfig(
                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			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	Optional[Any]       =			FlaxResNetModel(config=__lowerCamelCase)
            _A				:	Optional[int]       =			model(__lowerCamelCase)

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	Tuple       =			self.num_labels
            _A				:	Any       =			FlaxResNetForImageClassification(config=__lowerCamelCase)
            _A				:	List[str]       =			model(__lowerCamelCase)
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	Union[str, Any]       =			self.prepare_config_and_inputs()
            _A   ,      _A				:	int       =			config_and_inputs
            _A				:	Optional[Any]       =			{"pixel_values": pixel_values}
            return config, inputs_dict







@require_flax
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    None:
            _A				:	Tuple       =			FlaxResNetModelTester(self)
            _A				:	str       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						has_text_modality=__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    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)      ->    Dict:
            return


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	int       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)


     @unittest.skip(reason="ResNet does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            pass


     @unittest.skip(reason="ResNet does not support input and output embeddings")
     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            _A   ,      _A				:	int       =			self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A				:	Union[str, Any]       =			["pixel_values"]
                   self.assertListEqual(arg_names[:1]			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Any:
            def check_hidden_states_output(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase):
                   _A				:	Optional[int]       =			model_class(__lowerCamelCase)

                   _A				:	Any       =			model(**self._prepare_for_class(__lowerCamelCase			,						__lowerCamelCase))

                   _A				:	Dict       =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   _A				:	List[str]       =			self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase)			,						expected_num_stages + 1)


     @unittest.skip(reason="ResNet does not use feedforward chunking")
     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A   ,      _A				:	Dict       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__):
                          _A				:	Optional[int]       =			self._prepare_for_class(__lowerCamelCase			,						__lowerCamelCase)
                          _A				:	Tuple       =			model_class(__lowerCamelCase)

                          @jax.jit
                          def model_jitted(__lowerCamelCase			,						**__lowerCamelCase):
                                 return model(pixel_values=__lowerCamelCase			,						**__lowerCamelCase)

                          with self.subTest("JIT Enabled"):
                                 _A				:	Dict       =			model_jitted(**__lowerCamelCase).to_tuple()

                          with self.subTest("JIT Disabled"):
                                 with jax.disable_jit():
                                        _A				:	str       =			model_jitted(**__lowerCamelCase).to_tuple()

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




def 							_UpperCAmelCase    ():
       _A				:	Tuple       =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       return image







@require_flax
class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     @cached_property
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            return AutoImageProcessor.from_pretrained("microsoft/resnet-50") if is_vision_available() else None


     @slow
     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Any       =			FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")

            _A				:	List[Any]       =			self.default_image_processor
            _A				:	Union[str, Any]       =			prepare_img()
            _A				:	Union[str, Any]       =			image_processor(images=__lowerCamelCase			,						return_tensors="np")

            _A				:	Optional[int]       =			model(**__lowerCamelCase)

            # verify the logits
            _A				:	List[str]       =			(1, 1_0_0_0)
            self.assertEqual(outputs.logits.shape			,						__lowerCamelCase)

            _A				:	Tuple       =			jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7])

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




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowerCAmelCase__						=			logging.get_logger(__name__)

lowerCAmelCase__						=			{'vocab_file': 'vocab.txt'}

lowerCAmelCase__						=			{
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

lowerCAmelCase__						=			{
    'tau/splinter-base': 5_12,
    'tau/splinter-base-qass': 5_12,
    'tau/splinter-large': 5_12,
    'tau/splinter-large-qass': 5_12,
}

lowerCAmelCase__						=			{
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE        =			SplinterTokenizer


     def __init__(						self			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=True			,						__lowerCamelCase="[UNK]"			,						__lowerCamelCase="[SEP]"			,						__lowerCamelCase="[PAD]"			,						__lowerCamelCase="[CLS]"			,						__lowerCamelCase="[MASK]"			,						__lowerCamelCase="[QUESTION]"			,						__lowerCamelCase=True			,						__lowerCamelCase=None			,						**__lowerCamelCase			,						)      ->    Union[str, Any]:
            super().__init__(
                __lowerCamelCase			,						tokenizer_file=__lowerCamelCase			,						do_lower_case=__lowerCamelCase			,						unk_token=__lowerCamelCase			,						sep_token=__lowerCamelCase			,						pad_token=__lowerCamelCase			,						cls_token=__lowerCamelCase			,						mask_token=__lowerCamelCase			,						tokenize_chinese_chars=__lowerCamelCase			,						strip_accents=__lowerCamelCase			,						additional_special_tokens=(question_token,)			,						**__lowerCamelCase			,						)

            _A				:	Tuple       =			json.loads(self.backend_tokenizer.normalizer.__getstate__())
            if (
                pre_tok_state.get("lowercase"			,						__lowerCamelCase) != do_lower_case
                or pre_tok_state.get("strip_accents"			,						__lowerCamelCase) != strip_accents
            ):
                   _A				:	Dict       =			getattr(__lowerCamelCase			,						pre_tok_state.pop("type"))
                   _A				:	List[str]       =			do_lower_case
                   _A				:	Any       =			strip_accents
                   _A				:	int       =			pre_tok_class(**__lowerCamelCase)

            _A				:	int       =			do_lower_case


     @property
     def 			_lowerCamelCase			(						self)      ->    str:
            return self.convert_tokens_to_ids(self.question_token)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

            _A				:	Tuple       =			[self.cls_token_id]
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if self.padding_side == "right":
                   # Input is question-then-context
                   return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
            else:
                   # Input is context-then-question
                   return cls + token_ids_a + sep + token_ids_a + question_suffix + sep


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.cls_token_id]
            _A				:	Union[str, Any]       =			[self.question_token_id] + [self.convert_tokens_to_ids(".")]
            if token_ids_a is None:
                   return len(cls + token_ids_a + sep) * [0]

            if self.padding_side == "right":
                   # Input is question-then-context
                   return len(cls + token_ids_a + question_suffix + sep) * [0] + len(token_ids_a + sep) * [1]
            else:
                   # Input is context-then-question
                   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + question_suffix + sep) * [1]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    Tuple[str]:
            _A				:	Tuple       =			self._tokenizer.model.save(__lowerCamelCase			,						name=__lowerCamelCase)
            return tuple(__lowerCamelCase)
