

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 _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def __init__(	self							:       List[Any]					,					a							:       Dict					,					a							:       Tuple=3					,					a							:       Any=32					,					a							:       str=3					,					a							:       Optional[int]=10					,					a							:       Union[str, Any]=[10, 20, 30, 40]					,					a							:       Any=[1, 1, 2, 1]					,					a							:       str=True					,					a							:       Dict=True					,					a							:       Optional[Any]="relu"					,					a							:       List[Any]=3					,					a							:       Any=None					,					)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				parent
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image_size
				SCREAMING_SNAKE_CASE      :					str    =				num_channels
				SCREAMING_SNAKE_CASE      :					List[str]    =				embeddings_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				hidden_sizes
				SCREAMING_SNAKE_CASE      :					Tuple    =				depths
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				is_training
				SCREAMING_SNAKE_CASE      :					Tuple    =				use_labels
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				hidden_act
				SCREAMING_SNAKE_CASE      :					Any    =				num_labels
				SCREAMING_SNAKE_CASE      :					List[str]    =				scope
				SCREAMING_SNAKE_CASE      :					Any    =				len(a		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_config()

				return config, pixel_values
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				str:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       List[str]					,					a							:       int		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				FlaxRegNetModel(config=a		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				model(a		)

				# 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 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       List[Any]					,					a							:       int		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.num_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				FlaxRegNetForImageClassification(config=a		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)		)






			def 							__UpperCamelCase       (	self							:       Any		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					str    =				{"pixel_values": pixel_values}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			lowerCamelCase__							=False
			lowerCamelCase__							=False
			lowerCamelCase__							=False
			def 							__UpperCamelCase       (	self							:       Dict		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				FlaxRegNetModelTester(self		)
				SCREAMING_SNAKE_CASE      :					Dict    =				ConfigTester(self					,					config_class=a					,					has_text_modality=a		)
			def 							__UpperCamelCase       (	self							:       int		)							->				Any:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				return
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a		)
			@unittest.skip(reason="RegNet does not use inputs_embeds"		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				pass
			@unittest.skip(reason="RegNet does not support input and output embeddings"		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				List[str]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase       (	self							:       int		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE      :					Any    =				["pixel_values"]
					self.assertListEqual(arg_names[:1]					,					a		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[str]:

				"""simple docstring"""

				def check_hidden_states_output(a							:       List[str]					,					a							:       Tuple					,					a							:       Optional[int]		):
					SCREAMING_SNAKE_CASE      :					Dict    =				model_class(a		)

					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(**self._prepare_for_class(a					,					a		)		)

					SCREAMING_SNAKE_CASE      :					Optional[int]    =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE      :					int    =				self.model_tester.num_stages
					self.assertEqual(len(a		)					,					expected_num_stages + 1		)

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					List[Any]    =				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				True
					check_hidden_states_output(a					,					a					,					a		)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					SCREAMING_SNAKE_CASE      :					Tuple    =				True

					check_hidden_states_output(a					,					a					,					a		)






			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__		):
						SCREAMING_SNAKE_CASE      :					List[str]    =				self._prepare_for_class(a					,					a		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model_class(a		)

						@jax.jit
						def model_jitted(a							:       Optional[int]					,					**a							:       str		):
							return model(pixel_values=a					,					**a		)

						with self.subTest("JIT Enabled"		):
							SCREAMING_SNAKE_CASE      :					str    =				model_jitted(**a		).to_tuple()

						with self.subTest("JIT Disabled"		):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model_jitted(**a		).to_tuple()

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

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Dict:

				"""simple docstring"""

				return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"		) if is_vision_available() else None






			@slow
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.default_image_processor
				SCREAMING_SNAKE_CASE      :					Any    =				prepare_img()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				image_processor(images=a					,					return_tensors="np"		)

				SCREAMING_SNAKE_CASE      :					int    =				model(**a		)

				# verify the logits
				SCREAMING_SNAKE_CASE      :					str    =				(1, 1000)
				self.assertEqual(outputs.logits.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array([-0.4180, -1.5051, -3.4836]		)

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







import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "spiece.model"}

lowercase  							=					{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

lowercase  							=					{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= ['''input_ids''', '''attention_mask''']
	lowerCAmelCase													= []




	def __init__(       self       ,			a       ,			a="<unk>"       ,			a="<s>"       ,			a="</s>"       ,			a="<pad>"       ,			a="[SEP]"       ,			a="[MASK]"       ,			a="[CLS]"       ,			a = None       ,			**a       ,			)       ->    None:
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else bos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else eos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else unk_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else pad_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else cls_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else sep_token

								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else mask_token

								snake_case_				=							{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=a       ,			eos_token=a       ,			unk_token=a       ,			pad_token=a       ,			sep_token=a       ,			mask_token=a       ,			cls_token=a       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**a       ,			)

								snake_case_				=							vocab_file

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(a					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								return self.sp_model.get_piece_size()




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							{self.convert_ids_to_tokens(a					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab




	def __getstate__(       self					)       ->    Optional[int]:
								snake_case_				=							self.__dict__.copy()
								snake_case_				=							None
								return state




	def __setstate__(       self       ,			a					)       ->    Optional[Any]:
								snake_case_				=							d

								# for backward compatibility
								if not hasattr(self       ,			'sp_model_kwargs'					):
															snake_case_				=							{}

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								return self.sp_model.encode(a       ,			out_type=a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Dict:
								return self.sp_model.piece_to_id(a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							self.sp_model.IdToPiece(a					)
								return token




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[Any]:
								snake_case_				=							[]
								snake_case_				=							''
								snake_case_				=							False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(a					) + token
																						snake_case_				=							True
																						snake_case_				=							[]
															else:
																						current_sub_tokens.append(a					)
																						snake_case_				=							False
								out_string += self.sp_model.decode(a					)
								return out_string.strip()




	def 							_UpperCamelCase			(       self       ,			a       ,			a = False       ,			a = None       ,			a = True       ,			**a       ,			)       ->    str:
								snake_case_				=							kwargs.pop('use_source_tokenizer'       ,			a					)

								snake_case_				=							self.convert_ids_to_tokens(a       ,			skip_special_tokens=a					)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								snake_case_				=							[]
								snake_case_				=							[]
								for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																						continue
															if token in self.added_tokens_encoder:
																						if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(a					)					)
																													snake_case_				=							[]
																						sub_texts.append(a					)
															else:
																						current_sub_text.append(a					)
								if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(a					)					)

								# Mimic the behavior of the Rust tokenizer:
								# No space before [MASK] and [SEP]
								if spaces_between_special_tokens:
															snake_case_				=							re.sub(R' (\[(MASK|SEP)\])'       ,			R'\1'       ,			' '.join(a					)					)
								else:
															snake_case_				=							''.join(a					)

								snake_case_				=							(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
															snake_case_				=							self.clean_up_tokenization(a					)
															return clean_text
								else:
															return text




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								if not os.path.isdir(a					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								snake_case_				=							os.path.join(
								    a       ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(a					) and os.path.isfile(self.vocab_file					):
															copyfile(self.vocab_file       ,			a					)
								elif not os.path.isfile(self.vocab_file					):
															with open(a       ,			'wb'					) as fi:
																						snake_case_				=							self.sp_model.serialized_model_proto()
																						fi.write(a					)

								return (out_vocab_file,)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								snake_case_				=							[self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None       ,			a = False					)       ->    List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a       ,			token_ids_a=a       ,			already_has_special_tokens=a					)

								if token_ids_a is None:
															return [1] + ([0] * len(a					)) + [1]
								return [1] + ([0] * len(a					)) + [1] + ([0] * len(a					)) + [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]





