
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE__   :			Dict												=    {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    {"facebook/blenderbot-3B": 128}

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[str]        =		VOCAB_FILES_NAMES
		a__       :						Union[str, Any]        =		PRETRAINED_VOCAB_FILES_MAP
		a__       :						List[str]        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__       :						Dict        =		["""input_ids""", """attention_mask"""]
		a__       :						str        =		BlenderbotTokenizer






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							str=None							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=None							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=None							,			SCREAMING_SNAKE_CASE__		:							Tuple="replace"							,			SCREAMING_SNAKE_CASE__		:							Dict="<s>"							,			SCREAMING_SNAKE_CASE__		:							Any="</s>"							,			SCREAMING_SNAKE_CASE__		:							List[Any]="</s>"							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]="<s>"							,			SCREAMING_SNAKE_CASE__		:							List[Any]="<unk>"							,			SCREAMING_SNAKE_CASE__		:							List[Any]="<pad>"							,			SCREAMING_SNAKE_CASE__		:							str="<mask>"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			**SCREAMING_SNAKE_CASE__		:							Dict							,			)     ->							int:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			tokenizer_file=SCREAMING_SNAKE_CASE__							,			errors=SCREAMING_SNAKE_CASE__							,			bos_token=SCREAMING_SNAKE_CASE__							,			eos_token=SCREAMING_SNAKE_CASE__							,			sep_token=SCREAMING_SNAKE_CASE__							,			cls_token=SCREAMING_SNAKE_CASE__							,			unk_token=SCREAMING_SNAKE_CASE__							,			pad_token=SCREAMING_SNAKE_CASE__							,			mask_token=SCREAMING_SNAKE_CASE__							,			add_prefix_space=SCREAMING_SNAKE_CASE__							,			trim_offsets=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)

						__lowerCamelCase    					=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
						if pre_tok_state.get('''add_prefix_space'''							,			SCREAMING_SNAKE_CASE__  ) != add_prefix_space:
										__lowerCamelCase    					=	getattr(SCREAMING_SNAKE_CASE__							,			pre_tok_state.pop('''type'''  )  )
										__lowerCamelCase    					=	add_prefix_space
										__lowerCamelCase    					=	pre_tok_class(**SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	add_prefix_space

						__lowerCamelCase    					=	'''post_processor'''
						__lowerCamelCase    					=	getattr(self.backend_tokenizer							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						if tokenizer_component_instance:
										__lowerCamelCase    					=	json.loads(tokenizer_component_instance.__getstate__()  )

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
														__lowerCamelCase    					=	tuple(state['''sep''']  )
										if "cls" in state:
														__lowerCamelCase    					=	tuple(state['''cls''']  )

										__lowerCamelCase    					=	False

										if state.get('''add_prefix_space'''							,			SCREAMING_SNAKE_CASE__  ) != add_prefix_space:
														__lowerCamelCase    					=	add_prefix_space
														__lowerCamelCase    					=	True

										if state.get('''trim_offsets'''							,			SCREAMING_SNAKE_CASE__  ) != trim_offsets:
														__lowerCamelCase    					=	trim_offsets
														__lowerCamelCase    					=	True

										if changes_to_apply:
														__lowerCamelCase    					=	getattr(SCREAMING_SNAKE_CASE__							,			state.pop('''type'''  )  )
														__lowerCamelCase    					=	component_class(**SCREAMING_SNAKE_CASE__  )
														setattr(self.backend_tokenizer							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		@property
		# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
		def  __A       (		self		:							Tuple  )     ->							str:
						if self._mask_token is None:
										if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''  )
										return None
						return str(self._mask_token  )






		@mask_token.setter
		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							Any  )     ->							List[Any]:
						__lowerCamelCase    					=	AddedToken(SCREAMING_SNAKE_CASE__							,			lstrip=SCREAMING_SNAKE_CASE__							,			rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else value
						__lowerCamelCase    					=	value






		def  __A       (		self		:							Any							,			*SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			**SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							BatchEncoding:
						__lowerCamelCase    					=	kwargs.get('''is_split_into_words'''							,			SCREAMING_SNAKE_CASE__  )
						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							int							,			*SCREAMING_SNAKE_CASE__		:							Dict							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							BatchEncoding:
						__lowerCamelCase    					=	kwargs.get('''is_split_into_words'''							,			SCREAMING_SNAKE_CASE__  )

						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[str] = None  )     ->							Tuple[str]:
						__lowerCamelCase    					=	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__							,			name=SCREAMING_SNAKE_CASE__  )
						return tuple(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							List[int]:
						__lowerCamelCase    					=	[self.sep_token_id]
						__lowerCamelCase    					=	[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  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							Tuple:
						return token_ids_a + [self.eos_token_id]







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							"Conversation"  )     ->							List[int]:
						__lowerCamelCase    					=	[]
						for is_user, text in conversation.iter_texts():
										if is_user:
														# We need to space prefix as it's being done within blenderbot
														inputs.append(''' ''' + text  )
										else:
														# Generated responses should contain them already.
														inputs.append(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	'''  '''.join(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	self.encode(SCREAMING_SNAKE_CASE__  )
						if len(SCREAMING_SNAKE_CASE__  ) > self.model_max_length:
										__lowerCamelCase    					=	input_ids[-self.model_max_length :]
										logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
						return input_ids





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:
					SCREAMING_SNAKE_CASE__   :			str												=    False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






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






		def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  __A       (		self		:							int  )     ->							Any:
						__lowerCamelCase    					=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						__lowerCamelCase    					=	Image.open(requests.get(SCREAMING_SNAKE_CASE__							,			stream=SCREAMING_SNAKE_CASE__  ).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    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self  )






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






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )






		def  __A       (		self		:							int  )     ->							str:
						__lowerCamelCase    					=	self.image_processor_tester.prepare_dummy_image()

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

						__lowerCamelCase    					=	image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,			torch.tensor(0.0606  )							,			atol=1e-3							,			rtol=1e-3  )  )






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

						# Test not batched input
						__lowerCamelCase    					=	(
						    (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
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

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






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

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

						__lowerCamelCase    					=	True

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

										__lowerCamelCase    					=	'''Hello'''

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

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






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

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

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







		def  __A       (		self		:							Tuple  )     ->							List[str]:
						# Initialize image_processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  )

						# Test not batched input
						__lowerCamelCase    					=	(
						    (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
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).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    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Any        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self							,			num_channels=4  )
						__lowerCamelCase    					=	3






		@property
		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )







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

						# Test not batched input
						__lowerCamelCase    					=	(
						    (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
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

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



