


import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
 import textwrap

 from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
 import torch

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 __a	 =      False


__a	 =      logging.get_logger(__name__)
__a	 =      """ybelkada/fonts"""
def 	_UpperCamelCase					(    )    ->Any:
   if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
      raise ImportError(
          F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
          """Pix2StructImageProcessor. Please upgrade torch.""" )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   requires_backends(UpperCamelCase__	,    ["""torch"""] )
   _check_torch_version()

   UpperCAmelCase      					=  image_tensor.unsqueeze(0 )
   UpperCAmelCase      					=  torch.nn.functional.unfold(UpperCamelCase__	,    (patch_height, patch_width)	,    stride=(patch_height, patch_width) )
   UpperCAmelCase      					=  patches.reshape(image_tensor.size(0 )	,    image_tensor.size(1 )	,    UpperCamelCase__	,    UpperCamelCase__	,    -1 )
   UpperCAmelCase      					=  patches.permute(0	,    4	,    2	,    3	,    1 ).reshape(
       image_tensor.size(2 ) // patch_height	,    image_tensor.size(3 ) // patch_width	,    image_tensor.size(1 ) * patch_height * patch_width	,    )
   return patches.unsqueeze(0 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ = 3_6	,    lowerCAmelCase_ = "black"	,    lowerCAmelCase_ = "white"	,    lowerCAmelCase_ = 5	,    lowerCAmelCase_ = 5	,    lowerCAmelCase_ = 5	,    lowerCAmelCase_ = 5	,    lowerCAmelCase_ = None	,    lowerCAmelCase_ = None	,    )    ->int:
   requires_backends(UpperCamelCase__	,    """vision""" )
   # Add new lines so that each line is no more than 80 characters.

   UpperCAmelCase      					=  textwrap.TextWrapper(width=8_0 )
   UpperCAmelCase      					=  wrapper.wrap(text=UpperCamelCase__ )
   UpperCAmelCase      					=  '''\n'''.join(UpperCamelCase__ )

   if font_bytes is not None and font_path is None:
      UpperCAmelCase      					=  io.BytesIO(UpperCamelCase__ )
   elif font_path is not None:
      UpperCAmelCase      					=  font_path
   else:
      UpperCAmelCase      					=  hf_hub_download(UpperCamelCase__	,    """Arial.TTF""" )
   UpperCAmelCase      					=  ImageFont.truetype(UpperCamelCase__	,    encoding="""UTF-8"""	,    size=UpperCamelCase__ )

   # Use a temporary canvas to determine the width and height in pixels when
   # rendering the text.
   UpperCAmelCase      					=  ImageDraw.Draw(Image.new("""RGB"""	,    (1, 1)	,    UpperCamelCase__ ) )
   UpperCAmelCase      					=  temp_draw.textbbox((0, 0)	,    UpperCamelCase__	,    UpperCamelCase__ )

   # Create the actual image with a bit of padding around the text.
   UpperCAmelCase      					=  text_width + left_padding + right_padding
   UpperCAmelCase      					=  text_height + top_padding + bottom_padding
   UpperCAmelCase      					=  Image.new("""RGB"""	,    (image_width, image_height)	,    UpperCamelCase__ )
   UpperCAmelCase      					=  ImageDraw.Draw(UpperCamelCase__ )
   draw.text(xy=(left_padding, top_padding)	,    text=UpperCamelCase__	,    fill=UpperCamelCase__	,    font=UpperCamelCase__ )
   return image
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    **lowerCAmelCase_ )    ->int:
   requires_backends(UpperCamelCase__	,    """vision""" )

   # Convert to PIL image if necessary
   UpperCAmelCase      					=  to_pil_image(UpperCamelCase__ )

   UpperCAmelCase      					=  render_text(UpperCamelCase__	,    **UpperCamelCase__ )
   UpperCAmelCase      					=  max(header_image.width	,    image.width )

   UpperCAmelCase      					=  int(image.height * (new_width / image.width) )
   UpperCAmelCase      					=  int(header_image.height * (new_width / header_image.width) )

   UpperCAmelCase      					=  Image.new("""RGB"""	,    (new_width, new_height + new_header_height)	,    """white""" )
   new_image.paste(header_image.resize((new_width, new_header_height) )	,    (0, 0) )
   new_image.paste(image.resize((new_width, new_height) )	,    (0, new_header_height) )

   # Convert back to the original framework if necessary
   UpperCAmelCase      					=  to_numpy_array(UpperCamelCase__ )

   if infer_channel_dimension_format(UpperCamelCase__ ) == ChannelDimension.LAST:
      UpperCAmelCase      					=  to_channel_dimension_format(UpperCamelCase__	,    ChannelDimension.LAST )

   return new_image







class 	__lowercase					(	UpperCamelCase_	):
      UpperCamelCase										=							['''flattened_patches''']





      def __init__(		self    :       Optional[int]       ,							__lowerCamelCase    :       Tuple = True       ,							__lowerCamelCase    :       List[Any] = True       ,							__lowerCamelCase    :       Dict = None       ,							__lowerCamelCase    :       Optional[Any] = 2_0_4_8       ,							__lowerCamelCase    :       str = False       ,							**__lowerCamelCase    :       str       ,							)     -> None:






         """simple docstring"""







         super().__init__(**__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase      					=  patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
         UpperCAmelCase      					=  do_normalize
         UpperCAmelCase      					=  do_convert_rgb
         UpperCAmelCase      					=  max_patches
         UpperCAmelCase      					=  is_vqa





      def 							_lowercase					(		self    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       List[Any]       ,							**__lowerCamelCase    :       Any							)     -> np.ndarray:






         """simple docstring"""







         requires_backends(self.extract_flattened_patches       ,							"""torch"""							)
         _check_torch_version()

         # convert to torch
         UpperCAmelCase      					=  to_channel_dimension_format(__SCREAMING_SNAKE_CASE       ,							ChannelDimension.FIRST							)
         UpperCAmelCase      					=  torch.from_numpy(__SCREAMING_SNAKE_CASE							)

         UpperCAmelCase      					=  patch_size['''height'''], patch_size['''width''']
         UpperCAmelCase      					=  get_image_size(__SCREAMING_SNAKE_CASE							)

         # maximize scale s.t.
         UpperCAmelCase      					=  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)							)
         UpperCAmelCase      					=  max(min(math.floor(scale * image_height / patch_height							)       ,							__SCREAMING_SNAKE_CASE							)       ,							1							)
         UpperCAmelCase      					=  max(min(math.floor(scale * image_width / patch_width							)       ,							__SCREAMING_SNAKE_CASE							)       ,							1							)
         UpperCAmelCase      					=  max(num_feasible_rows * patch_height       ,							1							)
         UpperCAmelCase      					=  max(num_feasible_cols * patch_width       ,							1							)

         UpperCAmelCase      					=  torch.nn.functional.interpolate(
             image.unsqueeze(0							)       ,							size=(resized_height, resized_width)       ,							mode="""bilinear"""       ,							align_corners=__SCREAMING_SNAKE_CASE       ,							antialias=__SCREAMING_SNAKE_CASE       ,							).squeeze(0							)

         # [1, rows, columns, patch_height * patch_width * image_channels]
         UpperCAmelCase      					=  torch_extract_patches(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE							)

         UpperCAmelCase      					=  patches.shape
         UpperCAmelCase      					=  patches_shape[1]
         UpperCAmelCase      					=  patches_shape[2]
         UpperCAmelCase      					=  patches_shape[3]

         # [rows * columns, patch_height * patch_width * image_channels]
         UpperCAmelCase      					=  patches.reshape([rows * columns, depth]							)

         # [rows * columns, 1]
         UpperCAmelCase      					=  torch.arange(__SCREAMING_SNAKE_CASE							).reshape([rows, 1]							).repeat(1       ,							__SCREAMING_SNAKE_CASE							).reshape([rows * columns, 1]							)
         UpperCAmelCase      					=  torch.arange(__SCREAMING_SNAKE_CASE							).reshape([1, columns]							).repeat(__SCREAMING_SNAKE_CASE       ,							1							).reshape([rows * columns, 1]							)

         # Offset by 1 so the ids do not contain zeros, which represent padding.
         row_ids += 1
         col_ids += 1

         # Prepare additional patch features.
         # [rows * columns, 1]
         UpperCAmelCase      					=  row_ids.to(torch.floataa							)
         UpperCAmelCase      					=  col_ids.to(torch.floataa							)

         # [rows * columns, 2 + patch_height * patch_width * image_channels]
         UpperCAmelCase      					=  torch.cat([row_ids, col_ids, patches]       ,							-1							)

         # [max_patches, 2 + patch_height * patch_width * image_channels]
         UpperCAmelCase      					=  torch.nn.functional.pad(__SCREAMING_SNAKE_CASE       ,							[0, 0, 0, max_patches - (rows * columns)]							).float()

         UpperCAmelCase      					=  to_numpy_array(__SCREAMING_SNAKE_CASE							)

         return result





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Tuple = None       ,							**__lowerCamelCase    :       Union[str, Any]							)     -> np.ndarray:






         """simple docstring"""







         if image.dtype == np.uinta:
            UpperCAmelCase      					=  image.astype(np.floataa							)

         # take mean across the whole `image`
         UpperCAmelCase      					=  np.mean(__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase      					=  np.std(__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase      					=  max(__SCREAMING_SNAKE_CASE       ,							1.0 / math.sqrt(np.prod(image.shape							)							)							)

         return normalize(__SCREAMING_SNAKE_CASE       ,							mean=__SCREAMING_SNAKE_CASE       ,							std=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE							)





      def 							_lowercase					(		self    :       str       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       List[str] = None       ,							__lowerCamelCase    :       Dict = None       ,							__lowerCamelCase    :       List[Any] = None       ,							__lowerCamelCase    :       List[str] = None       ,							__lowerCamelCase    :       str = None       ,							__lowerCamelCase    :       Any = None       ,							__lowerCamelCase    :       List[str] = ChannelDimension.FIRST       ,							**__lowerCamelCase    :       int       ,							)     -> ImageInput:






         """simple docstring"""







         UpperCAmelCase      					=  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase      					=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
         UpperCAmelCase      					=  patch_size if patch_size is not None else self.patch_size
         UpperCAmelCase      					=  max_patches if max_patches is not None else self.max_patches
         UpperCAmelCase      					=  self.is_vqa

         if kwargs.get("""data_format"""       ,							__SCREAMING_SNAKE_CASE							) is not None:
            raise ValueError("""data_format is not an accepted input as the outputs are """							)

         UpperCAmelCase      					=  make_list_of_images(__SCREAMING_SNAKE_CASE							)

         if not valid_images(__SCREAMING_SNAKE_CASE							):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
            UpperCAmelCase      					=  [convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

         # All transformations expect numpy arrays.
         UpperCAmelCase      					=  [to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

         if is_vqa:
            if header_text is None:
               raise ValueError("""A header text must be provided for VQA models."""							)
            UpperCAmelCase      					=  kwargs.pop("""font_bytes"""       ,							__SCREAMING_SNAKE_CASE							)
            UpperCAmelCase      					=  kwargs.pop("""font_path"""       ,							__SCREAMING_SNAKE_CASE							)

            if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE							):
               UpperCAmelCase      					=  [header_text] * len(__SCREAMING_SNAKE_CASE							)

            UpperCAmelCase      					=  [
                render_header(__SCREAMING_SNAKE_CASE       ,							header_text[i]       ,							font_bytes=__SCREAMING_SNAKE_CASE       ,							font_path=__SCREAMING_SNAKE_CASE							)
                for i, image in enumerate(__SCREAMING_SNAKE_CASE							)
            ]

         if do_normalize:
            UpperCAmelCase      					=  [self.normalize(image=__SCREAMING_SNAKE_CASE							) for image in images]

         # convert to torch tensor and permute
         UpperCAmelCase      					=  [
             self.extract_flattened_patches(image=__SCREAMING_SNAKE_CASE       ,							max_patches=__SCREAMING_SNAKE_CASE       ,							patch_size=__SCREAMING_SNAKE_CASE							)
             for image in images
         ]

         # create attention mask in numpy
         UpperCAmelCase      					=  [(image.sum(axis=-1							) != 0).astype(np.floataa							) for image in images]

         UpperCAmelCase      					=  BatchFeature(
             data={"""flattened_patches""": images, """attention_mask""": attention_masks}       ,							tensor_type=__SCREAMING_SNAKE_CASE							)

         return encoded_outputs





import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptTokenizer,
    )
    from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=1_3    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=9_9    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=5    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=3_7    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=5_1_2    ,    __SCREAMING_SNAKE_CASE=1_6    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=None    ,    )	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					parent
           UpperCamelCase__     : str											=					batch_size
           UpperCamelCase__     : List[Any]											=					seq_length
           UpperCamelCase__     : List[Any]											=					is_training
           UpperCamelCase__     : Any											=					use_input_mask
           UpperCamelCase__     : Dict											=					use_token_type_ids
           UpperCamelCase__     : List[str]											=					use_labels
           UpperCamelCase__     : Optional[int]											=					vocab_size
           UpperCamelCase__     : Union[str, Any]											=					hidden_size
           UpperCamelCase__     : int											=					num_hidden_layers
           UpperCamelCase__     : Union[str, Any]											=					num_attention_heads
           UpperCamelCase__     : Union[str, Any]											=					intermediate_size
           UpperCamelCase__     : Optional[int]											=					hidden_act
           UpperCamelCase__     : Optional[Any]											=					hidden_dropout_prob
           UpperCamelCase__     : Optional[Any]											=					attention_probs_dropout_prob
           UpperCamelCase__     : Union[str, Any]											=					max_position_embeddings
           UpperCamelCase__     : Optional[Any]											=					type_vocab_size
           UpperCamelCase__     : int											=					type_sequence_label_size
           UpperCamelCase__     : Tuple											=					initializer_range
           UpperCamelCase__     : Optional[Any]											=					num_labels
           UpperCamelCase__     : List[str]											=					num_choices
           UpperCamelCase__     : Union[str, Any]											=					scope






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size	)

           UpperCamelCase__     : Tuple											=					None
           if self.use_input_mask:
                 UpperCamelCase__     : List[str]											=					random_attention_mask([self.batch_size, self.seq_length]	)

           UpperCamelCase__     : int											=					None
           if self.use_token_type_ids:
                 UpperCamelCase__     : Dict											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.type_vocab_size	)

           UpperCamelCase__     : Union[str, Any]											=					None
           UpperCamelCase__     : int											=					None
           UpperCamelCase__     : Tuple											=					None
           if self.use_labels:
                 UpperCamelCase__     : Any											=					ids_tensor([self.batch_size]    ,    self.type_sequence_label_size	)
                 UpperCamelCase__     : Optional[int]											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.num_labels	)
                 UpperCamelCase__     : int											=					ids_tensor([self.batch_size]    ,    self.num_choices	)

           UpperCamelCase__     : Dict											=					self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           return BioGptConfig(
               vocab_size=self.vocab_size    ,    hidden_size=self.hidden_size    ,    num_hidden_layers=self.num_hidden_layers    ,    num_attention_heads=self.num_attention_heads    ,    intermediate_size=self.intermediate_size    ,    hidden_act=self.hidden_act    ,    hidden_dropout_prob=self.hidden_dropout_prob    ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,    max_position_embeddings=self.max_position_embeddings    ,    type_vocab_size=self.type_vocab_size    ,    is_decoder=__SCREAMING_SNAKE_CASE    ,    initializer_range=self.initializer_range    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    )	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()

           # create attention mask
           UpperCamelCase__     : Optional[int]											=					torch.ones(input_ids.shape    ,    dtype=torch.long    ,    device=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					self.seq_length // 2
           UpperCamelCase__     : List[str]											=					0

           # first forward pass
           UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	).to_tuple()

           # create hypothetical next token and extent to next_input_ids
           UpperCamelCase__     : Any											=					ids_tensor((self.batch_size, 1)    ,    config.vocab_size	)

           # change a random masked slice from input_ids
           UpperCamelCase__     : Tuple											=					ids_tensor((1,)    ,    __SCREAMING_SNAKE_CASE	).item() + 1
           UpperCamelCase__     : List[str]											=					ids_tensor((self.batch_size, 1)    ,    config.vocab_size	).squeeze(-1	)
           UpperCamelCase__     : int											=					random_other_next_tokens

           # append to next input_ids and attn_mask
           UpperCamelCase__     : Any											=					torch.cat([input_ids, next_tokens]    ,    dim=-1	)
           UpperCamelCase__     : List[Any]											=					torch.cat(
               [attn_mask, torch.ones((attn_mask.shape[0], 1)    ,    dtype=torch.long    ,    device=__SCREAMING_SNAKE_CASE	)]    ,    dim=1    ,    )

           # get two different outputs
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']

           # select random slice
           UpperCamelCase__     : Optional[int]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Tuple											=					output_from_no_past[:, -1, random_slice_idx].detach()
           UpperCamelCase__     : str											=					output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	).to(__SCREAMING_SNAKE_CASE	).eval()

           UpperCamelCase__     : Union[str, Any]											=					torch.ones(input_ids.shape    ,    dtype=torch.long    ,    device=__SCREAMING_SNAKE_CASE	)

           # first forward pass
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    use_cache=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__					,UpperCamelCase__     : List[Any]											=					outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__     : List[str]											=					ids_tensor((self.batch_size, 3)    ,    config.vocab_size	)
           UpperCamelCase__     : Any											=					ids_tensor((self.batch_size, 3)    ,    2	)

           # append to next input_ids and
           UpperCamelCase__     : Optional[Any]											=					torch.cat([input_ids, next_tokens]    ,    dim=-1	)
           UpperCamelCase__     : Tuple											=					torch.cat([attention_mask, next_attn_mask]    ,    dim=-1	)

           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE	)[
               '''last_hidden_state'''
           ]

           # select random slice
           UpperCamelCase__     : Union[str, Any]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Union[str, Any]											=					output_from_no_past[:, -3:, random_slice_idx].detach()
           UpperCamelCase__     : Any											=					output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					BioGptForCausalLM(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.loss.shape    ,    ()	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)	)
           result.loss.backward()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers	)
           for key in model.state_dict().keys():
                 if "c_proj" in key and "weight" in key:
                       self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]	) - model_std	)    ,    0.001	)
                       self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)    ,    0.01	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.num_labels
           UpperCamelCase__     : Any											=					BioGptForTokenClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.num_labels)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.prepare_config_and_inputs()
           (
               (
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,
           )     : List[str]											=					config_and_inputs
           UpperCamelCase__     : Optional[Any]											=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_torch
class 					_lowerCamelCase			(   UpperCamelCase_						,				UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		 =	(BioGptForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(
         {
             '''feature-extraction''': BioGptModel,
             '''text-classification''': BioGptForSequenceClassification,
             '''text-generation''': BioGptForCausalLM,
             '''token-classification''': BioGptForTokenClassification,
             '''zero-shot''': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_		 =	False






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptModelTester(self	)
           UpperCamelCase__     : Dict											=					ConfigTester(self    ,    config_class=__SCREAMING_SNAKE_CASE    ,    hidden_size=3_7	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           self.config_tester.run_common_tests()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 UpperCamelCase__     : Optional[Any]											=					type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*__SCREAMING_SNAKE_CASE    ,    gradient_checkpointing=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)

           UpperCamelCase__     : Optional[Any]											=					'''left'''

           # Define PAD Token = EOS Token = 50256
           UpperCamelCase__     : Optional[int]											=					tokenizer.eos_token
           UpperCamelCase__     : List[str]											=					model.config.eos_token_id

           # use different length sentences to test batching
           UpperCamelCase__     : Optional[int]											=					[
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           UpperCamelCase__     : Dict											=					tokenizer(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''    ,    padding=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					model.generate(
               input_ids=__SCREAMING_SNAKE_CASE    ,    attention_mask=inputs['''attention_mask'''].to(__SCREAMING_SNAKE_CASE	)    ,    )

           UpperCamelCase__     : int											=					tokenizer(sentences[0]    ,    return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					model.generate(input_ids=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Tuple											=					inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
           UpperCamelCase__     : List[Any]											=					tokenizer(sentences[1]    ,    return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					model.generate(input_ids=__SCREAMING_SNAKE_CASE    ,    max_length=model.config.max_length - num_paddings	)

           UpperCamelCase__     : Any											=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.decode(output_non_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					tokenizer.decode(output_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Any											=					[
               '''Hello, my dog is a little bit bigger than a little bit.''',
               '''Today, I have a good idea of how to use the information''',
           ]
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    [non_padded_sentence, padded_sentence]	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__     : Optional[int]											=					BioGptModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Dict											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : int											=					3
           UpperCamelCase__     : Tuple											=					input_dict['''input_ids''']
           UpperCamelCase__     : List[Any]											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					ids_tensor([self.model_tester.batch_size]    ,    self.model_tester.type_sequence_label_size	)
           UpperCamelCase__     : Optional[int]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(result.logits.shape    ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : List[str]											=					3
           UpperCamelCase__     : List[Any]											=					'''multi_label_classification'''
           UpperCamelCase__     : List[str]											=					input_dict['''input_ids''']
           UpperCamelCase__     : Tuple											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					ids_tensor(
               [self.model_tester.batch_size, config.num_labels]    ,    self.model_tester.type_sequence_label_size	).to(torch.float	)
           UpperCamelCase__     : List[str]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(result.logits.shape    ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : Union[str, Any]											=					torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]	)
           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE	)[0]

           UpperCamelCase__     : Optional[Any]											=					4_2_3_8_4

           UpperCamelCase__     : List[Any]											=					torch.Size((1, 5, vocab_size)	)
           self.assertEqual(output.shape    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					torch.tensor(
               [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]	)

           self.assertTrue(torch.allclose(output[:, :3, :3]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : List[Any]											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)

           torch.manual_seed(0	)
           UpperCamelCase__     : Dict											=					tokenizer('''COVID-19 is'''    ,    return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					model.generate(
               **__SCREAMING_SNAKE_CASE    ,    min_length=1_0_0    ,    max_length=1_0_2_4    ,    num_beams=5    ,    early_stopping=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : int											=					tokenizer.decode(output_ids[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					(
               '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
               ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
               ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
               ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
               ''' more than 800,000 deaths.'''
           )
           self.assertEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)


