



'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					RobertaTokenizer
	__A			  =					RobertaTokenizerFast
	__A			  =					True
	__A			  =					{'''cls_token''': '''<s>'''}







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowercase        =							[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							]
							lowercase        =							dict(zip(_lowerCAmelCase ,				range(len(_lowerCAmelCase						)						)						)						)
							lowercase        =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							lowercase        =							{"""unk_token""": """<unk>"""}

							lowercase        =							os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["""vocab_file"""]						)
							lowercase        =							os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["""merges_file"""]						)
							with open(self.vocab_file ,				"""w""" ,				encoding="""utf-8"""						) as fp:
													fp.write(json.dumps(_lowerCAmelCase						) + """\n"""						)
							with open(self.merges_file ,				"""w""" ,				encoding="""utf-8"""						) as fp:
													fp.write("""\n""".join(_lowerCAmelCase						)						)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							kwargs.update(self.special_tokens_map						)
							return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						)







	def   _a   (    self ,				**_lowerCAmelCase						)    ->    List[Any]:
							'''simple docstring'''






							kwargs.update(self.special_tokens_map						)
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							"""lower newer"""
							lowercase        =							"""lower newer"""
							return input_text, output_text







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.tokenizer_class(self.vocab_file ,				self.merges_file ,				**self.special_tokens_map						)
							lowercase        =							"""lower newer"""
							lowercase        =							["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
							lowercase        =							tokenizer.tokenize(_lowerCAmelCase						)  # , add_prefix_space=True)
							self.assertListEqual(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase        =							tokens + [tokenizer.unk_token]
							lowercase        =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase						) ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("""Hello world!""" ,				add_special_tokens=_lowerCAmelCase						) ,				[0, 3_1414, 232, 328, 2]						)
							self.assertListEqual(
							    tokenizer.encode("""Hello world! cécé herlolip 418""" ,				add_special_tokens=_lowerCAmelCase						) ,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] ,				)







	@slow
	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.tokenizer_class.from_pretrained("""roberta-base"""						)

							lowercase        =							tokenizer.encode("""sequence builders""" ,				add_special_tokens=_lowerCAmelCase						)
							lowercase        =							tokenizer.encode("""multi-sequence build""" ,				add_special_tokens=_lowerCAmelCase						)

							lowercase        =							tokenizer.encode(
							    """sequence builders""" ,				add_special_tokens=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase						)
							lowercase        =							tokenizer.encode(
							    """sequence builders""" ,				"""multi-sequence build""" ,				add_special_tokens=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase						)

							lowercase        =							tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase						)
							lowercase        =							tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase ,				_lowerCAmelCase						)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.get_tokenizer()

							lowercase        =							"""Encode this sequence."""
							lowercase        =							tokenizer.byte_encoder[""" """.encode("""utf-8"""						)[0]]

							# Testing encoder arguments
							lowercase        =							tokenizer.encode(_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase						)
							lowercase        =							tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertNotEqual(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase        =							tokenizer.encode(_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase						)
							lowercase        =							tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertEqual(_lowerCAmelCase ,				_lowerCAmelCase						)

							tokenizer.add_special_tokens({"""bos_token""": """<s>"""}						)
							lowercase        =							tokenizer.encode(_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
							lowercase        =							tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
							self.assertNotEqual(_lowerCAmelCase ,				_lowerCAmelCase						)

							# Testing spaces after special tokens
							lowercase        =							"""<mask>"""
							tokenizer.add_special_tokens(
							    {"""mask_token""": AddedToken(_lowerCAmelCase ,				lstrip=_lowerCAmelCase ,				rstrip=_lowerCAmelCase						)}						)  # mask token has a left space
							lowercase        =							tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)

							lowercase        =							"""Encode <mask> sequence"""
							lowercase        =							"""Encode <mask>sequence"""

							lowercase        =							tokenizer.encode(_lowerCAmelCase						)
							lowercase        =							encoded.index(_lowerCAmelCase						)
							lowercase        =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertEqual(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase        =							tokenizer.encode(_lowerCAmelCase						)
							lowercase        =							encoded.index(_lowerCAmelCase						)
							lowercase        =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertNotEqual(_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																			lowercase        =							self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,				**_lowerCAmelCase						)
																			lowercase        =							self.tokenizer_class.from_pretrained(_lowerCAmelCase ,				**_lowerCAmelCase						)
																			lowercase        =							"""A, <mask> AllenNLP sentence."""
																			lowercase        =							tokenizer_r.encode_plus(_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase ,				return_token_type_ids=_lowerCAmelCase						)
																			lowercase        =							tokenizer_p.encode_plus(_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase ,				return_token_type_ids=_lowerCAmelCase						)

																			# token_type_ids should put 0 everywhere
																			self.assertEqual(sum(tokens_r["""token_type_ids"""]						) ,				sum(tokens_p["""token_type_ids"""]						)						)

																			# attention_mask should put 1 everywhere, so sum over length should be 1
																			self.assertEqual(
																			    sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						) ,				sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						) ,				)

																			lowercase        =							tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
																			lowercase        =							tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)

																			# Rust correctly handles the space before the mask while python doesnt
																			self.assertSequenceEqual(tokens_p["""input_ids"""] ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)
																			self.assertSequenceEqual(tokens_r["""input_ids"""] ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]						)

																			self.assertSequenceEqual(
																			    _lowerCAmelCase ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
																			self.assertSequenceEqual(
																			    _lowerCAmelCase ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							for trim_offsets, add_prefix_space in itertools.product([True, False] ,				repeat=2						):
													lowercase        =							self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)

													lowercase        =							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
													lowercase        =							json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

													self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] ,				_lowerCAmelCase						)

													self.assertEqual(post_processor_state["""add_prefix_space"""] ,				_lowerCAmelCase						)
													self.assertEqual(post_processor_state["""trim_offsets"""] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																			lowercase        =							"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																			lowercase        =							F"""{text_of_1_token} {text_of_1_token}"""

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(len(_lowerCAmelCase						) + 1, len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(len(_lowerCAmelCase						) + 1, len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(len(_lowerCAmelCase						), len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(len(_lowerCAmelCase						), len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							F""" {text}"""

																			# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																			#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																			# )
																			# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																			# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																			# self.assertEqual(
																			#     encoding.offset_mapping[1],
																			#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																			# )

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(1, 1 + len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(1 + len(_lowerCAmelCase						) + 1, 1 + len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, 1 + len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(1 + len(_lowerCAmelCase						), 1 + len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)

																			lowercase        =							self.rust_tokenizer_class.from_pretrained(
																			    _lowerCAmelCase ,				use_fast=_lowerCAmelCase ,				add_prefix_space=_lowerCAmelCase ,				trim_offsets=_lowerCAmelCase						)
																			lowercase        =							tokenizer_r(_lowerCAmelCase ,				return_offsets_mapping=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						)
																			self.assertEqual(encoding.offset_mapping[0] ,				(0, 1 + len(_lowerCAmelCase						))						)
																			self.assertEqual(
																			    encoding.offset_mapping[1] ,				(1 + len(_lowerCAmelCase						), 1 + len(_lowerCAmelCase						) + 1 + len(_lowerCAmelCase						)) ,				)







'''simple docstring'''


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

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




@add_end_docstrings(_UpperCAmelCase			)
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							requires_backends(self ,				"""vision"""						)
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							{}
							lowercase        =							{}

							if prompt is not None:
													lowercase        =							prompt

							if generate_kwargs is not None:
													lowercase        =							generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			lowercase        =							{}
													if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																			raise ValueError(
																			    """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
																			    """ please use only one"""						)
													lowercase        =							max_new_tokens
							return preprocess_params, forward_kwargs, {}







	def __call__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							return super().__call__(_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_image(_lowerCAmelCase						)

							if prompt is not None:
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			raise ValueError(
																			    F"""Received an invalid text input, got - {type(_lowerCAmelCase						)} - but expected a single string. """
																			    """Note also that one single text can be provided for conditional image to text generation."""						)

													lowercase        =							self.model.config.model_type

													if model_type == "git":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(text=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						).input_ids
																			lowercase        =							[self.tokenizer.cls_token_id] + input_ids
																			lowercase        =							torch.tensor(_lowerCAmelCase						).unsqueeze(0						)
																			model_inputs.update({"""input_ids""": input_ids}						)

													elif model_type == "pix2struct":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				header_text=_lowerCAmelCase ,				return_tensors=self.framework						)

													elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(_lowerCAmelCase ,				return_tensors=self.framework						)
																			model_inputs.update(_lowerCAmelCase						)

													else:
																			raise ValueError(F"""Model type {model_type} does not support conditional text generation"""						)

							else:
													lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)

							if self.model.config.model_type == "git" and prompt is None:
													lowercase        =							None

							return model_inputs







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Union[str, Any]:
							'''simple docstring'''






							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["""input_ids"""] ,				_lowerCAmelCase						)
							    and all(x is None for x in model_inputs["""input_ids"""]						)
							):
													lowercase        =							None

							if generate_kwargs is None:
													lowercase        =							{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							lowercase        =							model_inputs.pop(self.model.main_input_name						)
							lowercase        =							self.model.generate(_lowerCAmelCase ,				**_lowerCAmelCase ,				**_lowerCAmelCase						)
							return model_outputs







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							[]
							for output_ids in model_outputs:
													lowercase        =							{
													    """generated_text""": self.tokenizer.decode(
													        _lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase ,				)
													}
													records.append(_lowerCAmelCase						)
							return records


