



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 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							RobertaTokenizer
	__A							=							RobertaTokenizerFast
	__A							=							True
	__A							=							{"cls_token": "<s>"}




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

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

						a					     =   os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["vocab_file"]		)
						a					     =   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      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("Hello world!"		,       add_special_tokens=lowerCamelCase_		)		,       [0, 31414, 232, 328, 2]		)
						self.assertListEqual(
						    tokenizer.encode("Hello world! cécé herlolip 418"		,       add_special_tokens=lowerCamelCase_		)		,       [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,       )




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   tokenizer.encode("sequence builders"		,       add_special_tokens=lowerCamelCase_		)
						a					     =   tokenizer.encode("multi-sequence build"		,       add_special_tokens=lowerCamelCase_		)

						a					     =   tokenizer.encode(
						    "sequence builders"		,       add_special_tokens=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		)
						a					     =   tokenizer.encode(
						    "sequence builders"		,       "multi-sequence build"		,       add_special_tokens=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		)

						a					     =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		)
						a					     =   tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		,       lowerCamelCase_		)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizer()

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

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

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

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

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

						a					     =   "Encode <mask> sequence"
						a					     =   "Encode <mask>sequence"

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

																a					     =   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
																a					     =   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, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																self.assertSequenceEqual(tokens_r["input_ids"]		,       [0, 250, 6, 50264, 3823, 487, 21992, 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      UpperCamelCase_       (self		):
						"""simple docstring"""

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

											a					     =   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
											a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

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

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   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)),
																# )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )

																a					     =   self.rust_tokenizer_class.from_pretrained(
																    lowerCamelCase_		,       use_fast=lowerCamelCase_		,       add_prefix_space=lowerCamelCase_		,       trim_offsets=lowerCamelCase_		)
																a					     =   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_		))		,       )





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(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""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      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}

						if prompt is not None:
											a					     =   prompt

						if generate_kwargs is not None:
											a					     =   generate_kwargs
						if max_new_tokens is not None:
											if "generate_kwargs" not in forward_kwargs:
																a					     =   {}
											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"		)
											a					     =   max_new_tokens
						return preprocess_params, forward_kwargs, {}




	def __call__(self		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						a					     =   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."		)

											a					     =   self.model.config.model_type

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

											elif model_type == "pix2struct":
																a					     =   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
																a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)
																a					     =   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:
											a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors=self.framework		)

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

						return model_inputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""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"]		)
						):
											a					     =   None

						if generate_kwargs is None:
											a					     =   {}
						# 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.
						a					     =   model_inputs.pop(self.model.main_input_name		)
						a					     =   self.model.generate(lowerCamelCase_		,       **lowerCamelCase_		,       **lowerCamelCase_		)
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

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