
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__	:  Any      =logging.get_logger(__name__)

UpperCAmelCase__	:  List[str]      ={
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}






class UpperCAmelCase     (		a__     ):





   '''simple docstring'''
   __UpperCamelCase	:					Dict				       =					"gpt2"
   __UpperCamelCase	:					Optional[int]				       =					["past_key_values"]
   __UpperCamelCase	:					Any				       =					{
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }


   def __init__(      self  :							Tuple    ,    lowerCAmelCase_  :							Optional[int]=5_0_2_5_7    ,    lowerCAmelCase_  :							List[Any]=1_0_2_4    ,    lowerCAmelCase_  :							str=7_6_8    ,    lowerCAmelCase_  :							Any=1_2    ,    lowerCAmelCase_  :							Dict=1_2    ,    lowerCAmelCase_  :							str=None    ,    lowerCAmelCase_  :							List[Any]="gelu_new"    ,    lowerCAmelCase_  :							Any=0.1    ,    lowerCAmelCase_  :							Union[str, Any]=0.1    ,    lowerCAmelCase_  :							Tuple=0.1    ,    lowerCAmelCase_  :							str=1e-5    ,    lowerCAmelCase_  :							List[Any]=0.02    ,    lowerCAmelCase_  :							Optional[Any]="cls_index"    ,    lowerCAmelCase_  :							List[Any]=True    ,    lowerCAmelCase_  :							Any=None    ,    lowerCAmelCase_  :							List[str]=True    ,    lowerCAmelCase_  :							Optional[int]=0.1    ,    lowerCAmelCase_  :							Optional[Any]=True    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							List[str]=5_0_2_5_6    ,    lowerCAmelCase_  :							Any=5_0_2_5_6    ,    lowerCAmelCase_  :							Tuple=False    ,    lowerCAmelCase_  :							Optional[int]=False    ,    **lowerCAmelCase_  :							str    ,    ):

      """simple docstring"""
      _A:						int	    = vocab_size
      _A:						List[str]	    = n_positions
      _A:						Any	    = n_embd
      _A:						Optional[Any]	    = n_layer
      _A:						Optional[int]	    = n_head
      _A:						Optional[Any]	    = n_inner
      _A:						str	    = activation_function
      _A:						Any	    = resid_pdrop
      _A:						Tuple	    = embd_pdrop
      _A:						Any	    = attn_pdrop
      _A:						Dict	    = layer_norm_epsilon
      _A:						Tuple	    = initializer_range
      _A:						str	    = summary_type
      _A:						int	    = summary_use_proj
      _A:						Union[str, Any]	    = summary_activation
      _A:						List[str]	    = summary_first_dropout
      _A:						List[str]	    = summary_proj_to_labels
      _A:						Dict	    = scale_attn_weights
      _A:						List[Any]	    = use_cache
      _A:						Optional[Any]	    = scale_attn_by_inverse_layer_idx
      _A:						int	    = reorder_and_upcast_attn

      _A:						Union[str, Any]	    = bos_token_id
      _A:						Optional[int]	    = eos_token_id

      super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_    ,    eos_token_id=SCREAMING_SNAKE_CASE_    ,    **SCREAMING_SNAKE_CASE_  )






class UpperCAmelCase     (		a__     ):





   '''simple docstring'''


   def __init__(      self  :							str    ,    lowerCAmelCase_  :							Union[str, Any]    ,    lowerCAmelCase_  :							Any = "default"    ,    lowerCAmelCase_  :							Any = None    ,    lowerCAmelCase_  :							Any = False    ,    ):

      """simple docstring"""
      super().__init__(SCREAMING_SNAKE_CASE_    ,    task=SCREAMING_SNAKE_CASE_    ,    patching_specs=SCREAMING_SNAKE_CASE_    ,    use_past=SCREAMING_SNAKE_CASE_  )
      if not getattr(self._config    ,    '''pad_token_id'''    ,    SCREAMING_SNAKE_CASE_  ):
         # TODO: how to do that better?
         _A:						Optional[Any]	    = 0


   @property
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
      if self.use_past:
         self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_    ,    direction='''inputs'''  )
         _A:						str	    = {0: 'batch', 1: 'past_sequence + sequence'}
      else:
         _A:						List[str]	    = {0: 'batch', 1: 'sequence'}

      return common_inputs


   @property
   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return self._config.n_layer


   @property
   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return self._config.n_head


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							int = -1    ,    lowerCAmelCase_  :							Optional[int] = -1    ,    lowerCAmelCase_  :							Union[str, Any] = False    ,    lowerCAmelCase_  :							str = None    ,    ):

      """simple docstring"""
      _A:						Optional[int]	    = super(SCREAMING_SNAKE_CASE_    ,    self  ).generate_dummy_inputs(
          SCREAMING_SNAKE_CASE_    ,    batch_size=SCREAMING_SNAKE_CASE_    ,    seq_length=SCREAMING_SNAKE_CASE_    ,    is_pair=SCREAMING_SNAKE_CASE_    ,    framework=SCREAMING_SNAKE_CASE_  )

      # We need to order the input in the way they appears in the forward()
      _A:						str	    = OrderedDict({'''input_ids''': common_inputs['''input_ids''']}  )

      # Need to add the past_keys
      if self.use_past:
         if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''  )
         else:
            import torch

            _A:						str	    = common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            _A:						str	    = seqlen + 2
            _A:						int	    = (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            _A:						List[str]	    = [
                (torch.zeros(SCREAMING_SNAKE_CASE_  ), torch.zeros(SCREAMING_SNAKE_CASE_  )) for _ in range(self.num_layers  )
            ]

      _A:						int	    = common_inputs['attention_mask']
      if self.use_past:
         _A:						int	    = ordered_inputs['attention_mask'].dtype
         _A:						Optional[Any]	    = torch.cat(
             [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_    ,    SCREAMING_SNAKE_CASE_    ,    dtype=SCREAMING_SNAKE_CASE_  )]    ,    dim=1  )

      return ordered_inputs


   @property
   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return 1_3





import unittest

import numpy as np

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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def __init__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]=7    ,    lowerCAmelCase_  :							Dict=3    ,    lowerCAmelCase_  :							Optional[Any]=1_8    ,    lowerCAmelCase_  :							Union[str, Any]=3_0    ,    lowerCAmelCase_  :							int=4_0_0    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							Dict=1 / 2_5_5    ,    lowerCAmelCase_  :							Union[str, Any]=True    ,    lowerCAmelCase_  :							str=8    ,    ):

      """simple docstring"""
      _A:						Optional[Any]	    = parent
      _A:						int	    = batch_size
      _A:						List[str]	    = num_channels
      _A:						Union[str, Any]	    = image_size
      _A:						Optional[int]	    = min_resolution
      _A:						int	    = max_resolution
      _A:						int	    = do_rescale
      _A:						Optional[Any]	    = rescale_factor
      _A:						List[Any]	    = do_pad
      _A:						Optional[int]	    = pad_size


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      return {
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
          "pad_size": self.pad_size,
      }


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[Any]=False    ,    lowerCAmelCase_  :							Tuple=False    ,    lowerCAmelCase_  :							Dict=False  ):

      """simple docstring"""
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         _A:						Dict	    = []
         for i in range(self.batch_size  ):
            image_inputs.append(
                np.random.randint(
                    2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta  )  )
      else:
         _A:						List[str]	    = []
         for i in range(self.batch_size  ):
            _A    ,						_A:						Optional[Any]	    = np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution  )    ,    2  )
            image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta  )  )

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _A:						Union[str, Any]	    = [Image.fromarray(np.moveaxis(lowerCAmelCase_    ,    0    ,    -1  )  ) for x in image_inputs]

      if torchify:
         _A:						List[Any]	    = [torch.from_numpy(lowerCAmelCase_  ) for x in image_inputs]

      return image_inputs






@require_torch
@require_vision
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





   '''simple docstring'''
   __UpperCamelCase	:					int				       =					SwinaSRImageProcessor if is_vision_available() else None


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						int	    = SwinaSRImageProcessingTester(self  )


   @property
   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Any	    = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_rescale'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''rescale_factor'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_pad'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''pad_size'''  )  )


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      _A    ,						_A:						Any	    = get_image_size(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = self.image_processor_tester.pad_size

      _A:						Optional[Any]	    = (old_height // size + 1) * size - old_height
      _A:						int	    = (old_width // size + 1) * size - old_width
      return old_height + pad_height, old_width + pad_width


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[int]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _A:						str	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    Image.Image  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						str	    = self.calculate_expected_size(np.array(image_inputs[0]  )  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Union[str, Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _A:						List[str]	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    numpify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    np.ndarray  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Union[str, Any]	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _A:						Tuple	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    torchify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    torch.Tensor  )

      # Test not batched input
      _A:						List[str]	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Dict	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )



