




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		__lowerCAmelCase		:				List[str]       			=		False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor




class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str=7					,      __lowerCamelCase				:     Optional[int]=3					,      __lowerCamelCase				:     Dict=18					,      __lowerCamelCase				:     Dict=30					,      __lowerCamelCase				:     List[Any]=4_00					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     List[str]=True					,      __lowerCamelCase				:     Tuple=True					,      __lowerCamelCase				:     Union[str, Any]=None					,      )					->     Optional[int]:
							a    =			size if size is not None else {"height": 20, "width": 20}
							a    =			parent
							a    =			batch_size
							a    =			num_channels
							a    =			image_size
							a    =			min_resolution
							a    =			max_resolution
							a    =			size
							a    =			do_normalize
							a    =			do_convert_rgb
							a    =			[5_12, 10_24, 20_48, 40_96]
							a    =			patch_size if patch_size is not None else {"height": 16, "width": 16}





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[int]:
							a    =			"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
							a    =			Image.open(requests.get(__lowerCamelCase					,      stream=__lowerCamelCase							).raw							).convert("RGB"							)
							return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,       )
@require_torch
@require_vision
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					PixaStructImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[Any]:
							a    =			PixaStructImageProcessingTester(self							)





				@property
				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(      self				:     str							)					->     Optional[int]:
							a    =			self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase					,      "do_convert_rgb"							)							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     List[Any]:
							a    =			self.image_processor_tester.prepare_dummy_image()

							a    =			self.image_processing_class(**self.image_processor_dict							)
							a    =			20_48

							a    =			image_processor(__lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,      torch.tensor(0.0_606							)					,      atol=1e-3					,      rtol=1e-3							)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							# Initialize image_processor
							a    =			self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase					,      Image.Image							)

							# Test not batched input
							a    =			(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										a    =			image_processor(
										    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

										# Test batched
										a    =			image_processor(
										    __lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )





				def 							__UpperCAmelCase							(      self				:     int							)					->     Union[str, Any]:
							# Initialize image_processor
							a    =			self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase					,      Image.Image							)

							# Test not batched input
							a    =			(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							a    =			True

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(__lowerCamelCase							):
													a    =			image_processor(
													    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches

										a    =			"Hello"

										a    =			image_processor(
										    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase					,      header_text=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

										# Test batched
										a    =			image_processor(
										    __lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase					,      header_text=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							# Initialize image_processor
							a    =			self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							a    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase					,      np.ndarray							)

							a    =			(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										a    =			image_processor(
										    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

										# Test batched
										a    =			image_processor(
										    __lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )





				def 							__UpperCAmelCase							(      self				:     int							)					->     str:
							# Initialize image_processor
							a    =			self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      torchify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase					,      torch.Tensor							)

							# Test not batched input
							a    =			(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										a    =			image_processor(
										    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

										# Test batched
										a    =			image_processor(
										    __lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,       reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,       )
@require_torch
@require_vision
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					PixaStructImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    =			PixaStructImageProcessingTester(self					,      num_channels=4							)
							a    =			3





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							a    =			self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase					,      "do_convert_rgb"							)							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     int:
							# Initialize image_processor
							a    =			self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase					,      Image.Image							)

							# Test not batched input
							a    =			(
							    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										a    =			image_processor(
										    image_inputs[0]					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

										# Test batched
										a    =			image_processor(
										    __lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase							).flattened_patches
										self.assertEqual(
										    encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )










import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


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


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [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 + sep + token_ids_a + sep) * [0]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       str				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

            if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                   copyfile(self.vocab_file ,						__lowerCamelCase)

            return (out_vocab_file,)
