
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  :					Dict						    =							logging.get_logger(__name__)

@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class __snake_case		(      SCREAMING_SNAKE_CASE_     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*a__		,**a__      )
           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None		,snake_case=None		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      {}
           lowercase							:     Union[str, Any]       	=      {}

           if prompt is not None:
                  lowercase							:     List[Any]       	=      prompt

           if generate_kwargs is not None:
                  lowercase							:     List[str]       	=      generate_kwargs
           if max_new_tokens is not None:
                  if "generate_kwargs" not in forward_kwargs:
                         lowercase							:     str       	=      {}
                  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							:     Optional[Any]       	=      max_new_tokens
           return preprocess_params, forward_kwargs, {}
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(a__		,**a__      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      load_image(a__      )

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

                  lowercase							:     Optional[int]       	=      self.model.config.model_type

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

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

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

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

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

           if self.model.config.model_type == "git" and prompt is None:
                  lowercase							:     Union[str, Any]       	=      None

           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







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

           if generate_kwargs is None:
                  lowercase							:     List[str]       	=      {}
           # 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							:     Tuple       	=      model_inputs.pop(self.model.main_input_name      )
           lowercase							:     Union[str, Any]       	=      self.model.generate(a__		,**a__		,**a__      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







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

"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Optional[int]           =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizer
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizerFast
     _UpperCamelCase							:			Dict							=		True
     _UpperCamelCase							:			Tuple							=		True






     def     __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """<pad>"""
       _lowerCAmelCase						:       List[Any]  			=     1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__  )				,     a__  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__  )				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """<s>"""  )
       self.assertEqual(vocab_keys[1]				,     """<pad>"""  )
       self.assertEqual(len(a__  )				,     1008  )






     def     __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size				,     1008  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )

       _lowerCAmelCase						:       Dict  			=     tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__				,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a__  )				,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,     )

       _lowerCAmelCase						:       Any  			=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
       self.assertListEqual(
           a__				,     [
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]				,     )
       _lowerCAmelCase						:       List[str]  			=     tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__				,     [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]				,     )

       _lowerCAmelCase						:       Optional[int]  			=     tokenizer.convert_ids_to_tokens(a__  )
       self.assertListEqual(
           a__				,     [
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]				,     )






     @cached_property
     def     __A  (		self  ):
       return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )






     def     __A  (		self  ):
       with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(a__				,     f.name  )
         _lowerCAmelCase						:       Union[str, Any]  			=     XGLMTokenizer(f.name				,     keep_accents=a__  )
         _lowerCAmelCase						:       List[str]  			=     pickle.dumps(a__  )
       pickle.loads(a__  )






     def     __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:       List[str]  			=     self.get_tokenizer()
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_rust_tokenizer()

       _lowerCAmelCase						:       Tuple  			=     """I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:       List[Any]  			=     tokenizer.tokenize(a__  )
       _lowerCAmelCase						:       Tuple  			=     rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       _lowerCAmelCase						:       str  			=     rust_tokenizer.encode(a__				,     add_special_tokens=a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       int  			=     self.get_rust_tokenizer()
       _lowerCAmelCase						:       Dict  			=     tokenizer.encode(a__  )
       _lowerCAmelCase						:       List[Any]  			=     rust_tokenizer.encode(a__  )
       self.assertListEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """Hello World!"""
       _lowerCAmelCase						:       Optional[int]  			=     [2, 31227, 4447, 35]

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     (
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
       )
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
       # fmt: on

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     {
           """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
           """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
       }  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a__				,     model_name="""facebook/xglm-564M"""				,     padding=a__				,     )




