





'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference




class  _A (					__lowercase						,					unittest.TestCase		):
		lowercase__:   Dict				     =   ShapEPipeline
		lowercase__:   Tuple				     =   ['''prompt''']
		lowercase__:   str				     =   ['''prompt''']
		lowercase__:   str				     =   [
		    '''num_images_per_prompt''',
		    '''num_inference_steps''',
		    '''generator''',
		    '''latents''',
		    '''guidance_scale''',
		    '''frame_size''',
		    '''output_type''',
		    '''return_dict''',
		]
		lowercase__:   Dict				     =   False

		@property
		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					return 32

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    List[Any]:


					"""simple docstring"""

					return 32

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					return self.time_input_dim * 4

		@property
		def 	lowercase__     (   self					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

					return 8

		@property
		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
					return tokenizer

		@property
		def 	lowercase__     (   self					:   Any			)					->    Any:


					"""simple docstring"""

					torch.manual_seed(0			)
					__snake_case					:      int							      =     CLIPTextConfig(
					    bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=self.text_embedder_hidden_size  ,      projection_dim=self.text_embedder_hidden_size  ,      intermediate_size=37  ,      layer_norm_eps=1E-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=10_00  ,      )
					return CLIPTextModelWithProjection(__magic_name__			)

		@property
		def 	lowercase__     (   self					:   Optional[int]			)					->    Optional[int]:


					"""simple docstring"""

					torch.manual_seed(0			)

					__snake_case					:      Tuple							      =     {
					    """num_attention_heads""": 2,
					    """attention_head_dim""": 16,
					    """embedding_dim""": self.time_input_dim,
					    """num_embeddings""": 32,
					    """embedding_proj_dim""": self.text_embedder_hidden_size,
					    """time_embed_dim""": self.time_embed_dim,
					    """num_layers""": 1,
					    """clip_embed_dim""": self.time_input_dim * 2,
					    """additional_embeddings""": 0,
					    """time_embed_act_fn""": """gelu""",
					    """norm_in_type""": """layer""",
					    """encoder_hid_proj_type""": None,
					    """added_emb_type""": None,
					}

					__snake_case					:      Union[str, Any]							      =     PriorTransformer(**__magic_name__			)
					return model

		@property
		def 	lowercase__     (   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					torch.manual_seed(0			)

					__snake_case					:      Dict							      =     {
					    """param_shapes""": (
					        (self.renderer_dim, 93),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					    ),
					    """d_latent""": self.time_input_dim,
					    """d_hidden""": self.renderer_dim,
					    """n_output""": 12,
					    """background""": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					__snake_case					:      Dict							      =     ShapERenderer(**__magic_name__			)
					return model

		def 	lowercase__     (   self					:   Optional[int]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.dummy_prior
					__snake_case					:      Any							      =     self.dummy_text_encoder
					__snake_case					:      Tuple							      =     self.dummy_tokenizer
					__snake_case					:      int							      =     self.dummy_renderer

					__snake_case					:      Tuple							      =     HeunDiscreteScheduler(
					    beta_schedule="""exp"""  ,      num_train_timesteps=10_24  ,      prediction_type="""sample"""  ,      use_karras_sigmas=__magic_name__  ,      clip_sample=__magic_name__  ,      clip_sample_range=1.0  ,      )
					__snake_case					:      int							      =     {
					    """prior""": prior,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """renderer""": renderer,
					    """scheduler""": scheduler,
					}

					return components

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Any=0			)					->    Union[str, Any]:


					"""simple docstring"""

					if str(__magic_name__			).startswith("""mps"""			):
								__snake_case					:      List[Any]							      =     torch.manual_seed(__magic_name__			)
					else:
								__snake_case					:      Optional[int]							      =     torch.Generator(device=__magic_name__			).manual_seed(__magic_name__			)
					__snake_case					:      str							      =     {
					    """prompt""": """horse""",
					    """generator""": generator,
					    """num_inference_steps""": 1,
					    """frame_size""": 32,
					    """output_type""": """np""",
					}
					return inputs

		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     """cpu"""

					__snake_case					:      Dict							      =     self.get_dummy_components()

					__snake_case					:      Dict							      =     self.pipeline_class(**__magic_name__			)
					__snake_case					:      List[Any]							      =     pipe.to(__magic_name__			)

					pipe.set_progress_bar_config(disable=__magic_name__			)

					__snake_case					:      Optional[Any]							      =     pipe(**self.get_dummy_inputs(__magic_name__			)			)
					__snake_case					:      str							      =     output.images[0]
					__snake_case					:      int							      =     image[0, -3:, -3:, -1]

					assert image.shape == (20, 32, 32, 3)

					__snake_case					:      int							      =     np.array(
					    [
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					        0.00039216,
					    ]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					self._test_inference_batch_consistent(batch_sizes=[1, 2]			)

		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     torch_device == """cpu"""
					__snake_case					:      int							      =     True

					self._test_inference_batch_single_identical(
					    batch_size=2  ,      test_max_difference=__magic_name__  ,      relax_max_difference=__magic_name__  ,      )

		def 	lowercase__     (   self					:   Tuple			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     self.get_dummy_components()
					__snake_case					:      List[str]							      =     self.pipeline_class(**__magic_name__			)
					__snake_case					:      List[Any]							      =     pipe.to(__magic_name__			)
					pipe.set_progress_bar_config(disable=__magic_name__			)

					__snake_case					:      str							      =     1
					__snake_case					:      Optional[int]							      =     2

					__snake_case					:      int							      =     self.get_dummy_inputs(__magic_name__			)

					for key in inputs.keys():
								if key in self.batch_params:
											__snake_case					:      List[str]							      =     batch_size * [inputs[key]]

					__snake_case					:      Optional[Any]							      =     pipe(**__magic_name__  ,      num_images_per_prompt=__magic_name__			)[0]

					assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[int]:


					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/shap_e/test_shap_e_np_out.npy"""			)
					__snake_case					:      List[Any]							      =     ShapEPipeline.from_pretrained("""openai/shap-e"""			)
					__snake_case					:      Optional[int]							      =     pipe.to(__magic_name__			)
					pipe.set_progress_bar_config(disable=__magic_name__			)

					__snake_case					:      Optional[int]							      =     torch.Generator(device=__magic_name__			).manual_seed(0			)

					__snake_case					:      Dict							      =     pipe(
					    """a shark"""  ,      generator=__magic_name__  ,      guidance_scale=15.0  ,      num_inference_steps=64  ,      frame_size=64  ,      output_type="""np"""  ,      ).images[0]

					assert images.shape == (20, 64, 64, 3)

					assert_mean_pixel_difference(__magic_name__  ,      __magic_name__			)









'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 					_a						(       _lowerCamelCase ) ->				Union[str, Any]:



			"""simple docstring"""


			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						__snake_case					:      List[str]							      =     k.replace(_lowerCamelCase    ,	_lowerCamelCase )

			if k.startswith("""encoder""" ):
						__snake_case					:      Optional[int]							      =     k.replace(""".attn"""    ,	""".self_attn""" )
						__snake_case					:      Tuple							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      List[str]							      =     k.replace("""norm2"""    ,	"""final_layer_norm""" )
			elif k.startswith("""decoder""" ):
						__snake_case					:      List[Any]							      =     k.replace("""norm1"""    ,	"""self_attn_layer_norm""" )
						__snake_case					:      str							      =     k.replace("""norm2"""    ,	"""encoder_attn_layer_norm""" )
						__snake_case					:      Optional[int]							      =     k.replace("""norm3"""    ,	"""final_layer_norm""" )
			return k







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						__snake_case					:      Optional[Any]							      =     sd.pop(_lowerCamelCase )
						__snake_case					:      List[str]							      =     k.replace("""layernorm_embedding"""    ,	"""layer_norm""" )
						assert new_k not in sd
						__snake_case					:      Union[str, Any]							      =     v


__UpperCamelCase					    =	["START"]







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )
			__snake_case					:      Dict							      =     model["""model"""]
			__snake_case					:      Optional[int]							      =     BlenderbotConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     BlenderbotForConditionalGeneration(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     m.model.state_dict().keys()
			__snake_case					:      int							      =     []
			__snake_case					:      Union[str, Any]							      =     {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						__snake_case					:      Optional[int]							      =     rename_state_dict_key(_lowerCamelCase )
						if new_k not in valid_keys:
									failures.append([k, new_k] )
						else:
									__snake_case					:      str							      =     v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(_lowerCamelCase )
			m.model.load_state_dict(_lowerCamelCase    ,	strict=_lowerCamelCase )
			m.half()
			m.save_pretrained(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
	parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
	parser.add_argument(
	    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
	)
	__UpperCamelCase					    =	parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


