






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:
					UpperCamelCase__     =	False


UpperCamelCase__     =	logging.get_logger(__name__)
UpperCamelCase__     =	"ybelkada/fonts"





def       _UpperCamelCase       ():






							"""simple docstring"""





							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       (a__				:Optional[Any]				,     a__				:Optional[Any]				,     a__				:List[Any]    ):






							"""simple docstring"""





							requires_backends(a__				,     ["""torch"""]    )
							_check_torch_version()

							UpperCamelCase__			       =     image_tensor.unsqueeze(0    )
							UpperCamelCase__			       =     torch.nn.functional.unfold(a__				,     (patch_height, patch_width)				,     stride=(patch_height, patch_width)    )
							UpperCamelCase__			       =     patches.reshape(image_tensor.size(0    )				,     image_tensor.size(1    )				,     a__				,     a__				,     -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       (a__				:str				,     a__				:int = 36				,     a__				:str = "black"				,     a__				:str = "white"				,     a__				:int = 5				,     a__				:int = 5				,     a__				:int = 5				,     a__				:int = 5				,     a__				:Optional[bytes] = None				,     a__				:Optional[str] = None				,     ):






							"""simple docstring"""





							requires_backends(a__				,     """vision"""    )
							# Add new lines so that each line is no more than 80 characters.

							UpperCamelCase__			       =     textwrap.TextWrapper(width=80    )
							UpperCamelCase__			       =     wrapper.wrap(text=a__    )
							UpperCamelCase__			       =     """\n""".join(a__    )

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

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

							# 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)				,     a__    )
							UpperCamelCase__			       =     ImageDraw.Draw(a__    )
							draw.text(xy=(left_padding, top_padding)				,     text=a__				,     fill=a__				,     font=a__    )
							return image





def       _UpperCamelCase       (a__				:np.ndarray				,     a__				:str				,     **a__				:Optional[Any]    ):






							"""simple docstring"""





							requires_backends(a__				,     """vision"""    )

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

							UpperCamelCase__			       =     render_text(a__				,     **a__    )
							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(a__    )

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

							return new_image

class     __SCREAMING_SNAKE_CASE   (       _lowercase    ):
							snake_case       : str         =    ["""flattened_patches"""]






							def __init__(  self					,	__lowerCAmelCase = True					,	__lowerCAmelCase = True					,	__lowerCAmelCase = None					,	__lowerCAmelCase = 2048					,	__lowerCAmelCase = False					,	**__lowerCAmelCase					,	):
														super().__init__(**__UpperCamelCase					)
														UpperCamelCase__			       =     patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
														UpperCamelCase__			       =     do_normalize
														UpperCamelCase__			       =     do_convert_rgb
														UpperCamelCase__			       =     max_patches
														UpperCamelCase__			       =     is_vqa






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	**__lowerCAmelCase					):
														requires_backends(self.extract_flattened_patches					,	"""torch"""					)
														_check_torch_version()

														# convert to torch
														UpperCamelCase__			       =     to_channel_dimension_format(__UpperCamelCase					,	ChannelDimension.FIRST					)
														UpperCamelCase__			       =     torch.from_numpy(__UpperCamelCase					)

														UpperCamelCase__       ,       UpperCamelCase__			       =     patch_size["""height"""], patch_size["""width"""]
														UpperCamelCase__       ,       UpperCamelCase__			       =     get_image_size(__UpperCamelCase					)

														# 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					)					,	__UpperCamelCase					)					,	1					)
														UpperCamelCase__			       =     max(min(math.floor(scale * image_width / patch_width					)					,	__UpperCamelCase					)					,	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=__UpperCamelCase					,	antialias=__UpperCamelCase					,	).squeeze(0					)

														# [1, rows, columns, patch_height * patch_width * image_channels]
														UpperCamelCase__			       =     torch_extract_patches(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					)

														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(__UpperCamelCase					).reshape([rows, 1]					).repeat(1					,	__UpperCamelCase					).reshape([rows * columns, 1]					)
														UpperCamelCase__			       =     torch.arange(__UpperCamelCase					).reshape([1, columns]					).repeat(__UpperCamelCase					,	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(__UpperCamelCase					,	[0, 0, 0, max_patches - (rows * columns)]					).float()

														UpperCamelCase__			       =     to_numpy_array(__UpperCamelCase					)

														return result






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	**__lowerCAmelCase					):
														if image.dtype == np.uinta:
																					UpperCamelCase__			       =     image.astype(np.floataa					)

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

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







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = None					,	__lowerCAmelCase = ChannelDimension.FIRST					,	**__lowerCAmelCase					,	):
														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"""					,	__UpperCamelCase					) is not None:
																					raise ValueError("""data_format is not an accepted input as the outputs are """					)

														UpperCamelCase__			       =     make_list_of_images(__UpperCamelCase					)

														if not valid_images(__UpperCamelCase					):
																					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(__UpperCamelCase					) for image in images]

														# All transformations expect numpy arrays.
														UpperCamelCase__			       =     [to_numpy_array(__UpperCamelCase					) 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"""					,	__UpperCamelCase					)
																					UpperCamelCase__			       =     kwargs.pop("""font_path"""					,	__UpperCamelCase					)

																					if isinstance(__UpperCamelCase					,	__UpperCamelCase					):
																												UpperCamelCase__			       =     [header_text] * len(__UpperCamelCase					)

																					UpperCamelCase__			       =     [
																					    render_header(__UpperCamelCase					,	header_text[i]					,	font_bytes=__UpperCamelCase					,	font_path=__UpperCamelCase					)
																					    for i, image in enumerate(__UpperCamelCase					)
																					]

														if do_normalize:
																					UpperCamelCase__			       =     [self.normalize(image=__UpperCamelCase					) for image in images]

														# convert to torch tensor and permute
														UpperCamelCase__			       =     [
														    self.extract_flattened_patches(image=__UpperCamelCase					,	max_patches=__UpperCamelCase					,	patch_size=__UpperCamelCase					)
														    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=__UpperCamelCase					)

														return encoded_outputs











import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
					    GPTNeoXForCausalLM,
					    GPTNeoXForQuestionAnswering,
					    GPTNeoXForSequenceClassification,
					    GPTNeoXForTokenClassification,
					    GPTNeoXModel,
					)

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=13					,	__lowerCAmelCase=7					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=99					,	__lowerCAmelCase=64					,	__lowerCAmelCase=5					,	__lowerCAmelCase=4					,	__lowerCAmelCase=37					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=16					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=3					,	__lowerCAmelCase=4					,	__lowerCAmelCase=None					,	):
														UpperCamelCase__			       =     parent
														UpperCamelCase__			       =     batch_size
														UpperCamelCase__			       =     seq_length
														UpperCamelCase__			       =     is_training
														UpperCamelCase__			       =     use_input_mask
														UpperCamelCase__			       =     use_token_type_ids
														UpperCamelCase__			       =     use_labels
														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     num_hidden_layers
														UpperCamelCase__			       =     num_attention_heads
														UpperCamelCase__			       =     intermediate_size
														UpperCamelCase__			       =     hidden_act
														UpperCamelCase__			       =     hidden_dropout_prob
														UpperCamelCase__			       =     attention_probs_dropout_prob
														UpperCamelCase__			       =     max_position_embeddings
														UpperCamelCase__			       =     type_vocab_size
														UpperCamelCase__			       =     type_sequence_label_size
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     num_labels
														UpperCamelCase__			       =     num_choices
														UpperCamelCase__			       =     scope
														UpperCamelCase__			       =     vocab_size - 1






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

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

														UpperCamelCase__			       =     None
														if self.use_labels:
																					UpperCamelCase__			       =     ids_tensor([self.batch_size, self.seq_length]					,	self.num_labels					)

														UpperCamelCase__			       =     self.get_config()

														return config, input_ids, input_mask, token_labels






							def 			_lowerCamelCase						(  self					):
														return GPTNeoXConfig(
														    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=__lowerCAmelCase					,	initializer_range=self.initializer_range					,	pad_token_id=self.pad_token_id					,	)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.prepare_config_and_inputs()

														UpperCamelCase__			       =     True

														return config, input_ids, input_mask, token_labels






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXModel(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXModel(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForQuestionAnswering(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														self.parent.assertEqual(result.start_logits.shape					,	(self.batch_size, self.seq_length)					)
														self.parent.assertEqual(result.end_logits.shape					,	(self.batch_size, self.seq_length)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForSequenceClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     ids_tensor([self.batch_size]					,	self.type_sequence_label_size					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForTokenClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()

														# first forward pass
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	use_cache=__lowerCAmelCase					)
														UpperCamelCase__			       =     outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														UpperCamelCase__			       =     ids_tensor((self.batch_size, 3)					,	config.vocab_size					)
														UpperCamelCase__			       =     ids_tensor((self.batch_size, 3)					,	vocab_size=2					)

														# append to next input_ids and
														UpperCamelCase__			       =     torch.cat([input_ids, next_tokens]					,	dim=-1					)
														UpperCamelCase__			       =     torch.cat([input_mask, next_mask]					,	dim=-1					)

														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					)
														UpperCamelCase__			       =     output_from_no_past["""hidden_states"""][0]
														UpperCamelCase__			       =     model(
														    __lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	past_key_values=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					,	)["""hidden_states"""][0]

														# select random slice
														UpperCamelCase__			       =     ids_tensor((1,)					,	output_from_past.shape[-1]					).item()
														UpperCamelCase__			       =     output_from_no_past[:, -3:, random_slice_idx].detach()
														UpperCamelCase__			       =     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(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-3					)					)







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.prepare_config_and_inputs()
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     config_and_inputs
														UpperCamelCase__			       =     {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict






@require_torch
class     __SCREAMING_SNAKE_CASE   (       _a				,						_a				,						_a				,						unittest.TestCase    ):
							snake_case       : Optional[Any]         =    (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case       : Union[str, Any]         =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
							snake_case       : Dict         =    (
							    {
							        """feature-extraction""": GPTNeoXModel,
							        """question-answering""": GPTNeoXForQuestionAnswering,
							        """text-classification""": GPTNeoXForSequenceClassification,
							        """text-generation""": GPTNeoXForCausalLM,
							        """token-classification""": GPTNeoXForTokenClassification,
							        """zero-shot""": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case       : Tuple         =    False
							snake_case       : Dict         =    False
							snake_case       : Tuple         =    False
							snake_case       : Any         =    False






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     GPTNeoXModelTester(self					)
														UpperCamelCase__			       =     ConfigTester(self					,	config_class=__lowerCAmelCase					,	hidden_size=64					,	num_attention_heads=8					)






							def 			_lowerCamelCase						(  self					):
														self.config_tester.run_common_tests()






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														# This regression test was failing with PyTorch < 1.3
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()

														UpperCamelCase__			       =     None

														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)






							@unittest.skip(reason="""Feed forward chunking is not implemented"""					)
							def 			_lowerCamelCase						(  self					):
														pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__			       =     ids_tensor([1, 10]					,	config.vocab_size					)
														UpperCamelCase__			       =     ids_tensor([1, int(config.max_position_embeddings * 1.5					)]					,	config.vocab_size					)

														set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
														UpperCamelCase__			       =     GPTNeoXModel(__lowerCAmelCase					)
														original_model.to(__lowerCAmelCase					)
														original_model.eval()
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).last_hidden_state

														set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
														UpperCamelCase__			       =     {"""type""": scaling_type, """factor""": 10.0}
														UpperCamelCase__			       =     GPTNeoXModel(__lowerCAmelCase					)
														scaled_model.to(__lowerCAmelCase					)
														scaled_model.eval()
														UpperCamelCase__			       =     scaled_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     scaled_model(__lowerCAmelCase					).last_hidden_state

														# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
														# maximum sequence length, so the outputs for the short input should match.
														if scaling_type == "dynamic":
																					self.assertTrue(torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-5					)					)
														else:
																					self.assertFalse(torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-5					)					)

														# The output should be different for long inputs
														self.assertFalse(torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-5					)					)






@require_torch
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							@slow
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)
														for checkpointing in [True, False]:
																					UpperCamelCase__			       =     GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)

																					if checkpointing:
																												model.gradient_checkpointing_enable()
																					else:
																												model.gradient_checkpointing_disable()
																					model.to(__lowerCAmelCase					)

																					UpperCamelCase__			       =     tokenizer("""My favorite food is"""					,	return_tensors="""pt"""					).to(__lowerCAmelCase					)
																					# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
																					# See: https://github.com/huggingface/transformers/pull/24193
																					UpperCamelCase__			       =     """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

																					UpperCamelCase__			       =     model.generate(**__lowerCAmelCase					,	do_sample=__lowerCAmelCase					,	max_new_tokens=20					)
																					UpperCamelCase__			       =     tokenizer.batch_decode(__lowerCAmelCase					)[0]

																					self.assertEqual(__lowerCAmelCase					,	__lowerCAmelCase					)




