

import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Union[str, Any]		     =							LDMTextToImagePipeline
			SCREAMING_SNAKE_CASE	:  Tuple		     =							TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			SCREAMING_SNAKE_CASE	:  Tuple		     =							PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			SCREAMING_SNAKE_CASE	:  int		     =							TEXT_TO_IMAGE_BATCH_PARAMS
			SCREAMING_SNAKE_CASE	:  int		     =							False







			def 							snake_case__(	self					:							Optional[Any]				) ->List[str]:
									torch.manual_seed(0				)
									snake_case_  =      UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,  layers_per_block=2				,  sample_size=3_2				,  in_channels=4				,  out_channels=4				,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,  cross_attention_dim=3_2				,  )
									snake_case_  =      DDIMScheduler(
									    beta_start=0.00085				,  beta_end=0.012				,  beta_schedule='''scaled_linear'''				,  clip_sample=_UpperCamelCase				,  set_alpha_to_one=_UpperCamelCase				,  )
									torch.manual_seed(0				)
									snake_case_  =      AutoencoderKL(
									    block_out_channels=(3_2, 6_4)				,  in_channels=3				,  out_channels=3				,  down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')				,  up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')				,  latent_channels=4				,  )
									torch.manual_seed(0				)
									snake_case_  =      CLIPTextConfig(
									    bos_token_id=0				,  eos_token_id=2				,  hidden_size=3_2				,  intermediate_size=3_7				,  layer_norm_eps=1e-05				,  num_attention_heads=4				,  num_hidden_layers=5				,  pad_token_id=1				,  vocab_size=1_0_0_0				,  )
									snake_case_  =      CLIPTextModel(_UpperCamelCase				)
									snake_case_  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

									snake_case_  =      {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vqvae''': vae,
									    '''bert''': text_encoder,
									    '''tokenizer''': tokenizer,
									}
									return components







			def 							snake_case__(	self					:							int				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[Any]=0				) ->Any:
									if str(_UpperCamelCase				).startswith('''mps'''				):
															snake_case_  =      torch.manual_seed(_UpperCamelCase				)
									else:
															snake_case_  =      torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
									snake_case_  =      {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







			def 							snake_case__(	self					:							str				) ->List[Any]:
									snake_case_  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_  =      self.get_dummy_components()
									snake_case_  =      LDMTextToImagePipeline(**_UpperCamelCase				)
									pipe.to(_UpperCamelCase				)
									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

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

									assert image.shape == (1, 1_6, 1_6, 3)
									snake_case_  =      np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3






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







			'''simple docstring'''

			def 							snake_case__(	self					:							Optional[int]				) ->str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]=torch.floataa				,  _UpperCamelCase					:							List[str]=0				) ->Tuple:
									snake_case_  =      torch.manual_seed(_UpperCamelCase				)
									snake_case_  =      np.random.RandomState(_UpperCamelCase				).standard_normal((1, 4, 3_2, 3_2)				)
									snake_case_  =      torch.from_numpy(_UpperCamelCase				).to(device=_UpperCamelCase				,  dtype=_UpperCamelCase				)
									snake_case_  =      {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







			def 							snake_case__(	self					:							Any				) ->int:
									snake_case_  =      LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''				).to(_UpperCamelCase				)
									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      self.get_inputs(_UpperCamelCase				)
									snake_case_  =      pipe(**_UpperCamelCase				).images
									snake_case_  =      image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 2_5_6, 2_5_6, 3)
									snake_case_  =      np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]				)
									snake_case_  =      np.abs(expected_slice - image_slice				).max()
									assert max_diff < 1e-3






@nightly
@require_torch_gpu
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			def 							snake_case__(	self					:							Dict				) ->List[Any]:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Optional[Any]=torch.floataa				,  _UpperCamelCase					:							List[str]=0				) ->Dict:
									snake_case_  =      torch.manual_seed(_UpperCamelCase				)
									snake_case_  =      np.random.RandomState(_UpperCamelCase				).standard_normal((1, 4, 3_2, 3_2)				)
									snake_case_  =      torch.from_numpy(_UpperCamelCase				).to(device=_UpperCamelCase				,  dtype=_UpperCamelCase				)
									snake_case_  =      {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 5_0,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







			def 							snake_case__(	self					:							Optional[int]				) ->List[str]:
									snake_case_  =      LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''				).to(_UpperCamelCase				)
									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      self.get_inputs(_UpperCamelCase				)
									snake_case_  =      pipe(**_UpperCamelCase				).images[0]

									snake_case_  =      load_numpy(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''				)
									snake_case_  =      np.abs(expected_image - image				).max()
									assert max_diff < 1e-3


"""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,
  )




