
"""simple docstring"""




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers import AutoModelForCausalLM

@require_torch
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
          lowercase       :							Union[str, Any]									=				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(A_							)
          lowercase       :							Union[str, Any]									=				-1

          lowercase       :							Optional[int]									=				ids_tensor((1, 5)      ,       vocab_size=model.config.vocab_size							).to(A_							)
          lowercase       :							Tuple									=				model.generate(A_      ,       max_new_tokens=10      ,       do_sample=A_							)
          lowercase       :							int									=				tokenizer.decode(greedy_ids[0]							)

          with CaptureStdout() as cs:
                 lowercase       :							Union[str, Any]									=				TextStreamer(A_							)
                 model.generate(A_      ,       max_new_tokens=10      ,       do_sample=A_      ,       streamer=A_							)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          lowercase       :							Dict									=				cs.out[:-1]

          self.assertEqual(A_      ,       A_							)
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
          lowercase       :							Dict									=				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(A_							)
          lowercase       :							Union[str, Any]									=				-1

          lowercase       :							Union[str, Any]									=				ids_tensor((1, 5)      ,       vocab_size=model.config.vocab_size							).to(A_							)
          lowercase       :							List[str]									=				model.generate(A_      ,       max_new_tokens=10      ,       do_sample=A_							)
          lowercase       :							Any									=				tokenizer.decode(greedy_ids[0]							)

          lowercase       :							Union[str, Any]									=				TextIteratorStreamer(A_							)
          lowercase       :							Dict									=				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          lowercase       :							Dict									=				Thread(target=model.generate      ,       kwargs=A_							)
          thread.start()
          lowercase       :							Optional[int]									=				""
          for new_text in streamer:
                 streamer_text += new_text

          self.assertEqual(A_      ,       A_							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
          lowercase       :							List[Any]									=				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(A_							)
          lowercase       :							List[str]									=				-1

          lowercase       :							str									=				ids_tensor((1, 5)      ,       vocab_size=model.config.vocab_size							).to(A_							)
          lowercase       :							str									=				model.generate(A_      ,       max_new_tokens=10      ,       do_sample=A_							)
          lowercase       :							int									=				greedy_ids[:, input_ids.shape[1] :]
          lowercase       :							Dict									=				tokenizer.decode(new_greedy_ids[0]							)

          with CaptureStdout() as cs:
                 lowercase       :							Optional[Any]									=				TextStreamer(A_      ,       skip_prompt=A_							)
                 model.generate(A_      ,       max_new_tokens=10      ,       do_sample=A_      ,       streamer=A_							)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          lowercase       :							Any									=				cs.out[:-1]

          self.assertEqual(A_      ,       A_							)
   def      __magic_name__     (     self							):
          # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
          # with actual models -- the dummy models' tokenizers are not aligned with their models, and
          # `skip_special_tokens=True` has no effect on them
          lowercase       :							Optional[int]									=				AutoTokenizer.from_pretrained("distilgpt2"							)
          lowercase       :							Optional[Any]									=				AutoModelForCausalLM.from_pretrained("distilgpt2"							).to(A_							)
          lowercase       :							int									=				-1

          lowercase       :							List[str]									=				torch.ones((1, 5)      ,       device=A_							).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
                 lowercase       :							Union[str, Any]									=				TextStreamer(A_      ,       skip_special_tokens=A_							)
                 model.generate(A_      ,       max_new_tokens=1      ,       do_sample=A_      ,       streamer=A_							)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          lowercase       :							int									=				cs.out[:-1]  # Remove the final "\n"
          lowercase       :							str									=				tokenizer(A_      ,       return_tensors="pt"							)
          self.assertEqual(streamer_text_tokenized.input_ids.shape      ,       (1, 1)							)

   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
          lowercase       :							Optional[Any]									=				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(A_							)
          lowercase       :							Union[str, Any]									=				-1

          lowercase       :							Tuple									=				ids_tensor((1, 5)      ,       vocab_size=model.config.vocab_size							).to(A_							)
          lowercase       :							Dict									=				TextIteratorStreamer(A_      ,       timeout=0.0_0_1							)
          lowercase       :							Optional[int]									=				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          lowercase       :							Optional[Any]									=				Thread(target=model.generate      ,       kwargs=A_							)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(A_							):
                 lowercase       :							int									=				""
                 for new_text in streamer:
                        streamer_text += new_text






"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       ['''image_processor''', '''tokenizer''']
							__a					 =       '''LayoutLMv2ImageProcessor'''
							__a					 =       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




							def __init__(     self:    Any     ,							A_:    Optional[Any]=None     ,							A_:    Dict=None     ,							**A_:    Any   ):
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""     ,							A_     ,							)
													__lowerCamelCase          =      kwargs.pop("""feature_extractor"""   )

										__lowerCamelCase          =      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""   )
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""   )

										super().__init__(A_     ,							A_   )




							def __call__(     self:    List[str]     ,							A_:    Any     ,							A_:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,							A_:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,							A_:    Union[List[List[int]], List[List[List[int]]]] = None     ,							A_:    Optional[Union[List[int], List[List[int]]]] = None     ,							A_:    bool = True     ,							A_:    Union[bool, str, PaddingStrategy] = False     ,							A_:    Union[bool, str, TruncationStrategy] = None     ,							A_:    Optional[int] = None     ,							A_:    int = 0     ,							A_:    Optional[int] = None     ,							A_:    Optional[bool] = None     ,							A_:    Optional[bool] = None     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = False     ,							A_:    bool = True     ,							A_:    Optional[Union[str, TensorType]] = None     ,							**A_:    Any     ,							):
										# verify input
										if self.image_processor.apply_ocr and (boxes is not None):
													raise ValueError(
													    """You cannot provide bounding boxes """
													    """if you initialized the image processor with apply_ocr set to True."""   )

										if self.image_processor.apply_ocr and (word_labels is not None):
													raise ValueError(
													    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

										if return_overflowing_tokens is True and return_offsets_mapping is False:
													raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

										# first, apply the image processor
										__lowerCamelCase          =      self.image_processor(images=A_     ,							return_tensors=A_   )

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
													if isinstance(A_     ,							A_   ):
																__lowerCamelCase          =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
													__lowerCamelCase          =      features["""words"""]

										__lowerCamelCase          =      self.tokenizer(
										    text=text if text is not None else features["""words"""]     ,							text_pair=text_pair if text_pair is not None else None     ,							boxes=boxes if boxes is not None else features["""boxes"""]     ,							word_labels=A_     ,							add_special_tokens=A_     ,							padding=A_     ,							truncation=A_     ,							max_length=A_     ,							stride=A_     ,							pad_to_multiple_of=A_     ,							return_token_type_ids=A_     ,							return_attention_mask=A_     ,							return_overflowing_tokens=A_     ,							return_special_tokens_mask=A_     ,							return_offsets_mapping=A_     ,							return_length=A_     ,							verbose=A_     ,							return_tensors=A_     ,							**A_     ,							)

										# add pixel values
										__lowerCamelCase          =      features.pop("""pixel_values"""   )
										if return_overflowing_tokens is True:
													__lowerCamelCase          =      self.get_overflowing_images(A_     ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
										__lowerCamelCase          =      images

										return encoded_inputs




							def   __a   (     self:    Optional[Any]     ,							A_:    Any     ,							A_:    Tuple   ):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__lowerCamelCase          =      []
										for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]   )

										if len(A_   ) != len(A_   ):
													raise ValueError(
													    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
													    f' {len(A_   )} and {len(A_   )}'   )

										return images_with_overflow




							def   __a   (     self:    Optional[Any]     ,							*A_:    str     ,							**A_:    Dict   ):
										return self.tokenizer.batch_decode(*A_     ,							**A_   )




							def   __a   (     self:    Union[str, Any]     ,							*A_:    Optional[Any]     ,							**A_:    Tuple   ):
										return self.tokenizer.decode(*A_     ,							**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										return ["input_ids", "bbox", "attention_mask", "image"]




							@property
							def   __a   (     self:    Optional[Any]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,							A_     ,							)
										return self.image_processor_class




							@property
							def   __a   (     self:    List[Any]   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,							A_     ,							)
										return self.image_processor




