
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        _SCREAMING_SNAKE_CASE	( snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					ShapEPipeline
	lowerCAmelCase__   =					['prompt']
	lowerCAmelCase__   =					['prompt']
	lowerCAmelCase__   =					[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	lowerCAmelCase__   =					False



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return 32



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					return 32



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					return self.time_input_dim * 4



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					return 8



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
					return tokenizer



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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=1000		,    )
					return CLIPTextModelWithProjection(lowerCamelCase_					)



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					torch.manual_seed(0					)

					lowerCamelCase_         =     {
					    "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,
					}

					lowerCamelCase_         =     PriorTransformer(**lowerCamelCase_					)
					return model



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Tuple:
					torch.manual_seed(0					)

					lowerCamelCase_         =     {
					    "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,
					    ),
					}
					lowerCamelCase_         =     ShapERenderer(**lowerCamelCase_					)
					return model



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     self.dummy_prior
					lowerCamelCase_         =     self.dummy_text_encoder
					lowerCamelCase_         =     self.dummy_tokenizer
					lowerCamelCase_         =     self.dummy_renderer

					lowerCamelCase_         =     HeunDiscreteScheduler(
					    beta_schedule="exp"		,    num_train_timesteps=1024		,    prediction_type="sample"		,    use_karras_sigmas=lowerCamelCase_		,    clip_sample=lowerCamelCase_		,    clip_sample_range=1.0		,    )
					lowerCamelCase_         =     {
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			Union[str, Any]:
					if str(lowerCamelCase_					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowerCamelCase_					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)
					lowerCamelCase_         =     {
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 32,
					    "output_type": "np",
					}
					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     "cpu"

					lowerCamelCase_         =     self.get_dummy_components()

					lowerCamelCase_         =     self.pipeline_class(**lowerCamelCase_					)
					lowerCamelCase_         =     pipe.to(lowerCamelCase_					)

					pipe.set_progress_bar_config(disable=lowerCamelCase_					)

					lowerCamelCase_         =     pipe(**self.get_dummy_inputs(lowerCamelCase_					)					)
					lowerCamelCase_         =     output.images[0]
					lowerCamelCase_         =     image[0, -3:, -3:, -1]

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

					lowerCamelCase_         =     np.array(
					    [
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					        0.0_0_0_3_9_2_1_6,
					    ]					)

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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     torch_device == "cpu"
					lowerCamelCase_         =     True

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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowerCamelCase_					)
					lowerCamelCase_         =     pipe.to(lowerCamelCase_					)
					pipe.set_progress_bar_config(disable=lowerCamelCase_					)

					lowerCamelCase_         =     1
					lowerCamelCase_         =     2

					lowerCamelCase_         =     self.get_dummy_inputs(lowerCamelCase_					)

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

					lowerCamelCase_         =     pipe(**lowerCamelCase_		,    num_images_per_prompt=lowerCamelCase_					)[0]

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




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"					)
					lowerCamelCase_         =     ShapEPipeline.from_pretrained("openai/shap-e"					)
					lowerCamelCase_         =     pipe.to(lowerCamelCase_					)
					pipe.set_progress_bar_config(disable=lowerCamelCase_					)

					lowerCamelCase_         =     torch.Generator(device=lowerCamelCase_					).manual_seed(0					)

					lowerCamelCase_         =     pipe(
					    "a shark"		,    generator=lowerCamelCase_		,    guidance_scale=1_5.0		,    num_inference_steps=64		,    frame_size=64		,    output_type="np"		,    ).images[0]

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

					assert_mean_pixel_difference(lowerCamelCase_		,    lowerCamelCase_					)





"""simple docstring"""


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase													={
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		list(s_dict.keys()				)
      for key in keys:
            A				  =		r""".*/layers_(\d+)"""
            A				  =		key
            if re.match(_a     ,   _a				):
                  A				  =		re.sub(r"""layers_(\d+)"""     ,   r"""block/\1/layer"""     ,   _a				)

            A				  =		r"""(encoder|decoder)\/"""

            if re.match(_a     ,   _a				):
                  A				  =		re.match(_a     ,   _a				).groups()
                  if groups[0] == "encoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/1/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/1/layer_norm/"""     ,   _a				)

                  elif groups[0] == "decoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/2/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/2/layer_norm/"""     ,   _a				)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        A				  =		new_key.replace(_a     ,   _a				)

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()				):
            if "expert" in key:
                  A				  =		s_dict[key].shape[0]
                  A				  =		s_dict[key]
                  for idx in range(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}



def   _A					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

      A				  =		re.findall(r"""(.*) = ([0-9.]*)"""     ,   _a				)
      A				  =		{}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  A				  =		float(_a				) if """.""" in value else int(_a				)

      A				  =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,   _a				)[0]
      A				  =		str(activation[1]				)

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		unflatten_dict(_a     ,   sep="""/"""				)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(_a     ,   _a				)

      print(f'Save PyTorch model to {pytorch_dump_path}'				)
      pt_model.save_pretrained(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  UpperCAmelCase													=parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )




