import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 	_a  (							A_							,		A_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					IFImgaImgPipeline
	__SCREAMING_SNAKE_CASE              =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
	__SCREAMING_SNAKE_CASE              =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__SCREAMING_SNAKE_CASE              =					PipelineTesterMixin.required_optional_params - {'latents'}


	def __lowerCAmelCase      (   self       ):
		return self._get_dummy_components()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):

		if str(snake_case_       ).startswith("mps"       ):
			_lowercase   =torch.manual_seed(snake_case_       )
		else:
			_lowercase   =torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

		_lowercase   =floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(snake_case_       )       ).to(snake_case_       )

		_lowercase   ={
		    "prompt": "A painting of a squirrel eating a burger",
		    "image": image,
		    "generator": generator,
		    "num_inference_steps": 2,
		    "output_type": "numpy",
		}

		return inputs


	def __lowerCAmelCase      (   self       ):
		self._test_save_load_optional_components()


	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()       ,  reason="XFormers attention is only available with CUDA and `xformers` installed"       ,  )
	def __lowerCAmelCase      (   self       ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )


	@unittest.skipIf(torch_device != "cuda"       ,  reason="float16 requires CUDA"       )
	def __lowerCAmelCase      (   self       ):
		super().test_save_load_floataa(expected_max_diff=1e-1       )


	@unittest.skipIf(torch_device != "cuda"       ,  reason="float16 requires CUDA"       )
	def __lowerCAmelCase      (   self       ):
		super().test_floataa_inference(expected_max_diff=1e-1       )


	def __lowerCAmelCase      (   self       ):
		self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )


	def __lowerCAmelCase      (   self       ):
		self._test_save_load_local()


	def __lowerCAmelCase      (   self       ):
		self._test_inference_batch_single_identical(
		    expected_max_diff=1e-2       ,  )










"""simple docstring"""



import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							GPTNeoConfig(
     hidden_size=config_json["n_embd"]     ,					num_layers=config_json["n_layer"]     ,					num_heads=config_json["n_head"]     ,					attention_types=config_json["attention_types"]     ,					max_position_embeddings=config_json["n_positions"]     ,					resid_dropout=config_json["res_dropout"]     ,					embed_dropout=config_json["embed_dropout"]     ,					attention_dropout=config_json["attn_dropout"]     ,					)
 print(f"""Building PyTorch model from configuration: {config}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_gpt_neo(lowercase_     ,					lowercase_     ,					lowercase_		)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained mesh-tf model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





