





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Tuple     		=				"""imagegpt"""
   _UpperCamelCase :						str     		=				["""past_key_values"""]
   _UpperCamelCase :						Union[str, Any]     		=				{
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }





   def __init__( self					,			snake_case=512 + 1					,			snake_case=32 * 32					,			snake_case=512					,			snake_case=24					,			snake_case=8					,			snake_case=None					,			snake_case="quick_gelu"					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=1E-5					,			snake_case=0.02					,			snake_case=True					,			snake_case=True					,			snake_case=False					,			snake_case=False					,			snake_case=False					,			**snake_case					,			)				->  List[Any]:
      """simple docstring"""





      a__	:						int      		=  vocab_size
      a__	:						Union[str, Any]      		=  n_positions
      a__	:						List[str]      		=  n_embd
      a__	:						List[str]      		=  n_layer
      a__	:						List[Any]      		=  n_head
      a__	:						List[str]      		=  n_inner
      a__	:						int      		=  activation_function
      a__	:						Dict      		=  resid_pdrop
      a__	:						List[Any]      		=  embd_pdrop
      a__	:						List[Any]      		=  attn_pdrop
      a__	:						int      		=  layer_norm_epsilon
      a__	:						Optional[int]      		=  initializer_range
      a__	:						List[str]      		=  scale_attn_weights
      a__	:						str      		=  use_cache
      a__	:						str      		=  scale_attn_by_inverse_layer_idx
      a__	:						int      		=  reorder_and_upcast_attn
      a__	:						List[Any]      		=  tie_word_embeddings

      super().__init__(tie_word_embeddings=snake_case					,			**snake_case      )







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict(
          [
              ("input_ids", {0: "batch", 1: "sequence"}),
          ]      )





   def 	_snake_case ( self					,			snake_case					,			snake_case = 1					,			snake_case = -1					,			snake_case = False					,			snake_case = None					,			snake_case = 3					,			snake_case = 32					,			snake_case = 32					,			)				->  Mapping[str, Any]:
      """simple docstring"""





      a__	:						Tuple      		=  self._generate_dummy_images(snake_case					,			snake_case					,			snake_case					,			snake_case      )
      a__	:						Any      		=  dict(preprocessor(images=snake_case					,			return_tensors=snake_case      )      )

      return inputs








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

