import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase__  	=      {
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

lowerCAmelCase__  	=      {"Helsinki-NLP/opus-mt-en-de": 5_1_2}
lowerCAmelCase__  	=      {}
class 	_a  (							lowerCamelCase_					):



	"""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              =					['input_ids', 'attention_mask']
	__SCREAMING_SNAKE_CASE              =					re.compile('>>.+<<'					)  # type: re.Pattern


	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_="<pad>"       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_=False       ,  **lowerCAmelCase_       ,  ):
		_lowercase   ={} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
		    source_lang=lowerCAmelCase_       ,  target_lang=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  pad_token=lowerCAmelCase_       ,  model_max_length=lowerCAmelCase_       ,  sp_model_kwargs=self.sp_model_kwargs       ,  target_vocab_file=lowerCAmelCase_       ,  separate_vocabs=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )
		assert Path(lowerCAmelCase_       ).exists(), F'''cannot find spm source {source_spm}'''

		_lowercase   =separate_vocabs
		_lowercase   =load_json(lowerCAmelCase_       )
		if self.unk_token not in self.encoder:
			raise KeyError("<unk> token must be in vocab"       )
		assert self.pad_token in self.encoder

		if separate_vocabs:
			_lowercase   =load_json(lowerCAmelCase_       )
			_lowercase   ={v: k for k, v in self.target_encoder.items()}
			_lowercase   =[]
		else:
			_lowercase   ={v: k for k, v in self.encoder.items()}
			_lowercase   =[k for k in self.encoder if k.startswith(">>"       ) and k.endswith("<<"       )]

		_lowercase   =source_lang
		_lowercase   =target_lang
		_lowercase   =[source_spm, target_spm]

		# load SentencePiece model for pre-processing
		_lowercase   =load_spm(lowerCAmelCase_       ,  self.sp_model_kwargs       )
		_lowercase   =load_spm(lowerCAmelCase_       ,  self.sp_model_kwargs       )
		_lowercase   =self.spm_source
		_lowercase   =self.encoder

		# Multilingual target side: default to using first supported language code.

		self._setup_normalizer()


	def __lowerCAmelCase      (   self       ):
		try:
			from sacremoses import MosesPunctNormalizer

			_lowercase   =MosesPunctNormalizer(self.source_lang       ).normalize
		except (ImportError, FileNotFoundError):
			warnings.warn("Recommended: pip install sacremoses."       )
			_lowercase   =lambda lowerCAmelCase_       : x


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		return self.punc_normalizer(lowerCAmelCase_       ) if x else ""


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		return self.current_encoder.get(lowerCAmelCase_       ,  self.current_encoder[self.unk_token]       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.language_code_re.match(lowerCAmelCase_       )
		_lowercase   =[match.group(0       )] if match else []
		return code, self.language_code_re.sub(""       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =self.remove_language_code(lowerCAmelCase_       )
		_lowercase   =self.current_spm.encode(lowerCAmelCase_       ,  out_type=lowerCAmelCase_       )
		return code + pieces


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		return self.decoder.get(lowerCAmelCase_       ,  self.unk_token       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		return super().batch_decode(lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		return super().decode(lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.spm_source if self._decode_use_source_tokenizer else self.spm_target
		_lowercase   =[]
		_lowercase   =""
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				out_string += sp_model.decode_pieces(lowerCAmelCase_       ) + token + " "
				_lowercase   =[]
			else:
				current_sub_tokens.append(lowerCAmelCase_       )
		out_string += sp_model.decode_pieces(lowerCAmelCase_       )
		return out_string.strip()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ):
		if token_ids_a is None:
			return token_ids_a + [self.eos_token_id]
		# We don't expect to process pairs, but leave the pair logic for API consistency
		return token_ids_a + token_ids_a + [self.eos_token_id]


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.spm_source
		_lowercase   =self.encoder


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.spm_target
		if self.separate_vocabs:
			_lowercase   =self.target_encoder


	@property
	def __lowerCAmelCase      (   self       ):
		return len(self.encoder       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if not os.path.isdir(lowerCAmelCase_       ):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
			return
		_lowercase   =[]

		if self.separate_vocabs:
			_lowercase   =os.path.join(
			    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]       ,  )
			_lowercase   =os.path.join(
			    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]       ,  )
			save_json(self.encoder       ,  lowerCAmelCase_       )
			save_json(self.target_encoder       ,  lowerCAmelCase_       )
			saved_files.append(lowerCAmelCase_       )
			saved_files.append(lowerCAmelCase_       )
		else:
			_lowercase   =os.path.join(
			    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]       )
			save_json(self.encoder       ,  lowerCAmelCase_       )
			saved_files.append(lowerCAmelCase_       )

		for spm_save_filename, spm_orig_path, spm_model in zip(
		    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]       ,  self.spm_files       ,  [self.spm_source, self.spm_target]       ,  ):
			_lowercase   =os.path.join(
			    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + spm_save_filename       )
			if os.path.abspath(lowerCAmelCase_       ) != os.path.abspath(lowerCAmelCase_       ) and os.path.isfile(lowerCAmelCase_       ):
				copyfile(lowerCAmelCase_       ,  lowerCAmelCase_       )
				saved_files.append(lowerCAmelCase_       )
			elif not os.path.isfile(lowerCAmelCase_       ):
				with open(lowerCAmelCase_       ,  "wb"       ) as fi:
					_lowercase   =spm_model.serialized_model_proto()
					fi.write(lowerCAmelCase_       )
				saved_files.append(lowerCAmelCase_       )

		return tuple(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		return self.get_src_vocab()


	def __lowerCAmelCase      (   self       ):
		return dict(self.encoder       ,  **self.added_tokens_encoder       )


	def __lowerCAmelCase      (   self       ):
		return dict(self.target_encoder       ,  **self.added_tokens_decoder       )


	def __getstate__(   self       ):
		_lowercase   =self.__dict__.copy()
		state.update(
		    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}       )
		return state


	def __setstate__(   self       ,  lowerCAmelCase_       ):
		_lowercase   =d

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

		_lowercase   ,  _lowercase   =(load_spm(lowerCAmelCase_       ,  self.sp_model_kwargs       ) for f in self.spm_files)
		_lowercase   =self.spm_source
		self._setup_normalizer()


	def __lowerCAmelCase      (   self       ,  *lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		return 1


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =set(self.all_special_ids       )  # call it once instead of inside list comp
		all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special
		return [1 if x in all_special_ids else 0 for x in seq]


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = False       ):
		if already_has_special_tokens:
			return self._special_token_mask(lowerCAmelCase_       )
		elif token_ids_a is None:
			return self._special_token_mask(lowerCAmelCase_       ) + [1]
		else:
			return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]

def 	__lowerCamelCase							(				__a    : str      ,							__a    : Dict[str, Any]		)      ->       sentencepiece.SentencePieceProcessor:
	_lowercase   =sentencepiece.SentencePieceProcessor(**__a		)
	spm.Load(__a		)
	return spm

def 	__lowerCamelCase							(				__a    : str      ,							__a    : str		)      ->       None:
	with open(__a      ,							"w"		) as f:
		json.dump(__a      ,							__a      ,							indent=2		)

def 	__lowerCamelCase							(				__a    : str		)      ->       Union[Dict, List]:
	with open(__a      ,							"r"		) as f:
		return json.load(__a		)







'''simple docstring'''

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=13,						lowercase_=32,						lowercase_=3,						lowercase_=4,						lowercase_=[10, 20, 30, 40],						lowercase_=[2, 2, 3, 2],						lowercase_=True,						lowercase_=True,						lowercase_=37,						lowercase_="gelu",						lowercase_=10,						lowercase_=0.02,						lowercase_=3,						lowercase_=None,						)    ->							Dict:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					image_size
					snake_case							=					num_channels
					snake_case							=					num_stages
					snake_case							=					hidden_sizes
					snake_case							=					depths
					snake_case							=					is_training
					snake_case							=					use_labels
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					scope

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					snake_case							=					None
					if self.use_labels:
						snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )

					snake_case							=					self.get_config()

					return config, pixel_values, labels

				def 					_lowerCamelCase    (       self )    ->							Any:
					return ConvNextConfig(
					    num_channels=self.num_channels,						hidden_sizes=self.hidden_sizes,						depths=self.depths,						num_stages=self.num_stages,						hidden_act=self.hidden_act,						is_decoder=lowercase_,						initializer_range=self.initializer_range,						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							List[Any]:
					snake_case							=					TFConvNextModel(config=lowercase_ )
					snake_case							=					model(lowercase_,						training=lowercase_ )
					# expected last hidden states: B, C, H // 32, W // 32
					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,						lowercase_,						lowercase_,						lowercase_ )    ->							Union[str, Any]:
					snake_case							=					self.type_sequence_label_size
					snake_case							=					TFConvNextForImageClassification(lowercase_ )
					snake_case							=					model(lowercase_,						labels=lowercase_,						training=lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.type_sequence_label_size) )






				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.prepare_config_and_inputs()
					snake_case			,			snake_case			,			snake_case							=					config_and_inputs
					snake_case							=					{'pixel_values': pixel_values}
					return config, inputs_dict







@require_tf
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				snake_case_							     = (
				    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						has_text_modality=lowercase_,						hidden_size=37,						)

				@unittest.skip(reason='ConvNext does not use inputs_embeds' )
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					pass

				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0,						reason='TF does not support backprop for grouped convolutions on CPU.',						)
				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					super().test_keras_fit()

				@unittest.skip(reason='ConvNext does not support input and output embeddings' )
				def 					_lowerCamelCase    (       self )    ->							Dict:
					pass

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					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(lowercase_ )
						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],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0,						reason='TF does not support backprop for grouped convolutions on CPU.',						)
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					super().test_dataset_conversion()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					def check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ ):
						snake_case							=					model_class(lowercase_ )

						snake_case							=					model(**self._prepare_for_class(lowercase_,						lowercase_ ) )
						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(lowercase_ ),						expected_num_stages + 1 )

						# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:] ),						[self.model_tester.image_size // 4, self.model_tester.image_size // 4],						)

					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(lowercase_,						lowercase_,						lowercase_ )

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

						check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ )

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

					def check_equivalence(lowercase_,						lowercase_,						lowercase_,						lowercase_={} ):
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ )
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ ).to_tuple()

						def recursive_check(lowercase_,						lowercase_ ):
							if isinstance(lowercase_,						(List, Tuple) ):
								for tuple_iterable_value, dict_iterable_value in zip(lowercase_,						lowercase_ ):
									recursive_check(lowercase_,						lowercase_ )
							elif tuple_object is None:
								return
							else:
								self.assertTrue(
								    all(tf.equal(lowercase_,						lowercase_ ) ),						msg=(
								        'Tuple and dict output are not equal. Difference:'
								        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
								    ),						)

							recursive_check(lowercase_,						lowercase_ )

					for model_class in self.all_model_classes:
						snake_case							=					model_class(lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModel.from_pretrained('facebook/convnext-tiny-224' )
					self.assertIsNotNone(lowercase_ )

def      __magic_name__							(			)      ->		Optional[int]:
	snake_case							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image







@require_tf
@require_vision
class lowerCamelCase   (    unittest.TestCase				):

				@cached_property
				def 					_lowerCamelCase    (       self )    ->							List[str]:
					return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' )

					snake_case							=					self.default_image_processor
					snake_case							=					prepare_img()
					snake_case							=					image_processor(images=lowercase_,						return_tensors='tf' )

					# forward pass
					snake_case							=					model(**lowercase_ )

					# verify the logits
					snake_case							=					tf.TensorShape((1, 1000) )
					self.assertEqual(outputs.logits.shape,						lowercase_ )

					snake_case							=					tf.constant([-0.0_260, -0.4_739, 0.1_911] )

					tf.debugging.assert_near(outputs.logits[0, :3],						lowercase_,						atol=1E-4 )





