
"""simple docstring"""

import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
class    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self      ,				lowercase_ = False							)  ->    Optional[int]:
          UpperCAmelCase  	=    1_0

          if only_cross_attention:
              UpperCAmelCase  	=    1_2
          else:
              # when only cross attention is not set, the cross attention dim must be the same as the query dim
              UpperCAmelCase  	=    query_dim

          return {
              "query_dim": query_dim,
              "cross_attention_dim": cross_attention_dim,
              "heads": 2,
              "dim_head": 4,
              "added_kv_proj_dim": 6,
              "norm_num_groups": 1,
              "only_cross_attention": only_cross_attention,
              "processor": AttnAddedKVProcessor(),
          }




      def 			a_					(							self      ,				lowercase_      ,				lowercase_							)  ->    List[str]:
          UpperCAmelCase  	=    2

          UpperCAmelCase  	=    torch.rand(lowercase_      ,				lowercase_      ,				3      ,				2							)
          UpperCAmelCase  	=    torch.rand(lowercase_      ,				4      ,				lowercase_							)
          UpperCAmelCase  	=    None

          return {
              "hidden_states": hidden_states,
              "encoder_hidden_states": encoder_hidden_states,
              "attention_mask": attention_mask,
          }




      def 			a_					(							self							)  ->    List[str]:
          torch.manual_seed(0							)

          UpperCAmelCase  	=    self.get_constructor_arguments(only_cross_attention=lowercase_							)
          UpperCAmelCase  	=    Attention(**lowercase_							)

          self.assertTrue(attn.to_k is not None							)
          self.assertTrue(attn.to_v is not None							)

          UpperCAmelCase  	=    self.get_forward_arguments(
              query_dim=constructor_args['query_dim']      ,				added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

          UpperCAmelCase  	=    attn(**lowercase_							)

          # only self attention

          torch.manual_seed(0							)

          UpperCAmelCase  	=    self.get_constructor_arguments(only_cross_attention=lowercase_							)
          UpperCAmelCase  	=    Attention(**lowercase_							)

          self.assertTrue(attn.to_k is None							)
          self.assertTrue(attn.to_v is None							)

          UpperCAmelCase  	=    self.get_forward_arguments(
              query_dim=constructor_args['query_dim']      ,				added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

          UpperCAmelCase  	=    attn(**lowercase_							)

          self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)

class    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self							)  ->    List[Any]:
          UpperCAmelCase  	=    DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'      ,				safety_checker=lowercase_							)

          UpperCAmelCase  	=    pipe(
              'foo'      ,				num_inference_steps=2      ,				generator=torch.Generator('cpu'							).manual_seed(0							)      ,				output_type='np'      ,				).images

          # the initial conversion succeeds
          UpperCAmelCase  	=    DiffusionPipeline.from_pretrained(
              'hf-internal-testing/tiny-stable-diffusion-pipe'      ,				device_map='sequential'      ,				safety_checker=lowercase_							)

          UpperCAmelCase  	=    pipe(
              'foo'      ,				num_inference_steps=2      ,				generator=torch.Generator('cpu'							).manual_seed(0							)      ,				output_type='np'      ,				).images

          with tempfile.TemporaryDirectory() as tmpdir:
              # save the converted model
              pipe.save_pretrained(lowercase_							)

              # can also load the converted weights
              UpperCAmelCase  	=    DiffusionPipeline.from_pretrained(lowercase_      ,				device_map='sequential'      ,				safety_checker=lowercase_							)

          UpperCAmelCase  	=    pipe(
              'foo'      ,				num_inference_steps=2      ,				generator=torch.Generator('cpu'							).manual_seed(0							)      ,				output_type='np'      ,				).images

          self.assertTrue(np.allclose(lowercase_      ,				lowercase_      ,				atol=1E-5							)							)
          self.assertTrue(np.allclose(lowercase_      ,				lowercase_      ,				atol=1E-5							)							)










import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




