






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   __A					=	argparse.ArgumentParser(
       description=(
           "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
           " Distillation"
       )
   )
   parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
   parser.add_argument("--model_name", default="roberta-large", type=str)
   parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
   parser.add_argument("--vocab_transform", action="store_true")
   __A					=	parser.parse_args()

   if args.model_type == "roberta":
      __A					=	RobertaForMaskedLM.from_pretrained(args.model_name)
      __A					=	"roberta"
   elif args.model_type == "gpt2":
      __A					=	GPTaLMHeadModel.from_pretrained(args.model_name)
      __A					=	"transformer"

   __A					=	model.state_dict()
   __A					=	{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         __A					=	state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         __A					=	F"""{prefix}.embeddings.{w}.weight"""
         __A					=	state_dict[param_name]
      for w in ["weight", "bias"]:
         __A					=	F"""{prefix}.embeddings.LayerNorm.{w}"""
         __A					=	state_dict[param_name]

    # Transformer Blocks #
   __A					=	0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               __A					=	state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         __A					=	state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               __A					=	state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         __A					=	state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            __A					=	state_dict[F"""lm_head.dense.{w}"""]
            __A					=	state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         __A					=	state_dict[F"""{prefix}.ln_f.{w}"""]
      __A					=	state_dict["lm_head.weight"]

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)






'''simple docstring'''





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Optional[int]			,				snake_case__		:  int			,				snake_case__		:  Optional[Any]=7			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=18			,				snake_case__		:  Dict=30			,				snake_case__		:  Optional[int]=4_00			,				snake_case__		:  str=True			,				snake_case__		:  str=None			,				snake_case__		:  Optional[int]=True			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							size if size is not None else {"height": 18, "width": 18}
   UpperCAmelCase__      :					List[str]				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					List[str]				       =							num_channels
   UpperCAmelCase__      :					List[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							min_resolution
   UpperCAmelCase__      :					Optional[int]				       =							max_resolution
   UpperCAmelCase__      :					Any				       =							do_resize
   UpperCAmelCase__      :					Any				       =							size
   UpperCAmelCase__      :					int				       =							do_normalize







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return {
       # here we create 2 clusters for the sake of simplicity
       "clusters": np.asarray(
           [
               [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
               [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
           ] ),
       "do_resize": self.do_resize,
       "size": self.size,
       "do_normalize": self.do_normalize,
   }






@require_torch
@require_vision
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							ImageGPTImageProcessor if is_vision_available() else None







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							ImageGPTImageProcessingTester(self )







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.image_processor_tester.prepare_image_processor_dict()







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



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(snake_case__			,				"clusters" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_resize" ) )
   self.assertTrue(hasattr(snake_case__			,				"size" ) )
   self.assertTrue(hasattr(snake_case__			,				"do_normalize" ) )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							self.image_processing_class.from_dict(self.image_processor_dict )
   self.assertEqual(image_processor.size			,				{"height": 18, "width": 18} )

   UpperCAmelCase__      :					Union[str, Any]				       =							self.image_processing_class.from_dict(self.image_processor_dict			,				size=42 )
   self.assertEqual(image_processor.size			,				{"height": 42, "width": 42} )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )
   UpperCAmelCase__      :					List[str]				       =							json.loads(image_processor.to_json_string() )
   for key, value in self.image_processor_dict.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				obj[key] ) )
     else:
       self.assertEqual(obj[key]			,				snake_case__ )







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



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCAmelCase__      :					Union[str, Any]				       =							os.path.join(snake_case__			,				"image_processor.json" )
     image_processor_first.to_json_file(snake_case__ )
     UpperCAmelCase__      :					Any				       =							self.image_processing_class.from_json_file(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Union[str, Any]				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.image_processing_class(**self.image_processor_dict )

   with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self.image_processing_class.from_pretrained(snake_case__ ).to_dict()

   UpperCAmelCase__      :					Tuple				       =							image_processor_first.to_dict()
   for key, value in image_processor_first.items():
     if key == "clusters":
       self.assertTrue(np.array_equal(snake_case__			,				image_processor_second[key] ) )
     else:
       self.assertEqual(image_processor_first[key]			,				snake_case__ )







 @unittest.skip("ImageGPT requires clusters at initialization" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







def snake_case_     (					):
  UpperCAmelCase__      :					List[str]				       =							load_dataset("hf-internal-testing/fixtures_image_utils"   ,       split="test"		)

  UpperCAmelCase__      :					Tuple				       =							Image.open(dataset[4]["file"]		)
  UpperCAmelCase__      :					List[Any]				       =							Image.open(dataset[5]["file"]		)

  UpperCAmelCase__      :					Optional[Any]				       =							[imagea, imagea]

  return images






@require_vision
@require_torch
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

   UpperCAmelCase__      :					Union[str, Any]				       =							prepare_images()

   # test non-batched
   UpperCAmelCase__      :					Optional[Any]				       =							image_processing(images[0]			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(1, 10_24) )

   UpperCAmelCase__      :					Union[str, Any]				       =							[3_06, 1_91, 1_91]
   self.assertEqual(encoding.input_ids[0, :3].tolist()			,				snake_case__ )

   # test batched
   UpperCAmelCase__      :					List[Any]				       =							image_processing(snake_case__			,				return_tensors="pt" )

   self.assertIsInstance(encoding.input_ids			,				torch.LongTensor )
   self.assertEqual(encoding.input_ids.shape			,				(2, 10_24) )

   UpperCAmelCase__      :					List[str]				       =							[3_03, 13, 13]
   self.assertEqual(encoding.input_ids[1, -3:].tolist()			,				snake_case__ )



