





'''simple docstring'''




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  lowercase__   :				str      =   False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor


class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=7						,     _UpperCAmelCase=3						,     _UpperCAmelCase=18						,     _UpperCAmelCase=30						,     _UpperCAmelCase=400						,     _UpperCAmelCase=None						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     ):

          '''simple docstring'''






          __A						:			List[str]								=	size if size is not None else {'height': 20, 'width': 20}
          __A						:			Any								=	parent
          __A						:			Optional[int]								=	batch_size
          __A						:			List[Any]								=	num_channels
          __A						:			Optional[Any]								=	image_size
          __A						:			int								=	min_resolution
          __A						:			str								=	max_resolution
          __A						:			Optional[int]								=	size
          __A						:			int								=	do_normalize
          __A						:			Tuple								=	do_convert_rgb
          __A						:			List[str]								=	[512, 1024, 2048, 4096]
          __A						:			str								=	patch_size if patch_size is not None else {'height': 16, 'width': 16}


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          __A						:			str								=	Image.open(requests.get(_UpperCAmelCase						,     stream=_UpperCAmelCase).raw).convert('RGB')
          return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,		reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' ,		)
@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	PixaStructImageProcessor if is_vision_available() else None


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	PixaStructImageProcessingTester(self)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCAmelCase						,     'do_normalize'))
          self.assertTrue(hasattr(_UpperCAmelCase						,     'do_convert_rgb'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	self.image_processor_tester.prepare_dummy_image()

          __A						:			Optional[Any]								=	self.image_processing_class(**self.image_processor_dict)
          __A						:			List[str]								=	2048

          __A						:			Any								=	image_processor(_UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,     torch.tensor(0.0606)						,     atol=1e-3						,     rtol=1e-3))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          __A						:			int								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,     Image.Image)

          # Test not batched input
          __A						:			int								=	(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __A						:			Dict								=	image_processor(
                   image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (1, max_patch, expected_hidden_dim)						,     )

               # Test batched
               __A						:			str								=	image_processor(
                   _UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          __A						:			List[str]								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,     Image.Image)

          # Test not batched input
          __A						:			Optional[int]								=	(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          __A						:			List[str]								=	True

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			Union[str, Any]								=	image_processor(
                        image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches

               __A						:			int								=	'Hello'

               __A						:			Dict								=	image_processor(
                   image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase						,     header_text=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (1, max_patch, expected_hidden_dim)						,     )

               # Test batched
               __A						:			Tuple								=	image_processor(
                   _UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase						,     header_text=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          __A						:			Any								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_UpperCAmelCase						,     numpify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,     np.ndarray)

          __A						:			str								=	(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __A						:			Optional[int]								=	image_processor(
                   image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (1, max_patch, expected_hidden_dim)						,     )

               # Test batched
               __A						:			Union[str, Any]								=	image_processor(
                   _UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          __A						:			str								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_UpperCAmelCase						,     torchify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,     torch.Tensor)

          # Test not batched input
          __A						:			str								=	(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __A						:			Optional[Any]								=	image_processor(
                   image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (1, max_patch, expected_hidden_dim)						,     )

               # Test batched
               __A						:			List[Any]								=	image_processor(
                   _UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,     )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,		reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' ,		)
@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	PixaStructImageProcessor if is_vision_available() else None


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	PixaStructImageProcessingTester(self						,     num_channels=4)
          __A						:			Optional[int]								=	3


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCAmelCase						,     'do_normalize'))
          self.assertTrue(hasattr(_UpperCAmelCase						,     'do_convert_rgb'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          __A						:			Dict								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase						,     Image.Image)

          # Test not batched input
          __A						:			Any								=	(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __A						:			Optional[int]								=	image_processor(
                   image_inputs[0]						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (1, max_patch, expected_hidden_dim)						,     )

               # Test batched
               __A						:			Any								=	image_processor(
                   _UpperCAmelCase						,     return_tensors='pt'						,     max_patches=_UpperCAmelCase).flattened_patches
               self.assertEqual(
                   encoded_images.shape						,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,     )






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)