import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()

class        UpperCamelCase						(				__a	,	unittest.TestCase				):
						a__		:Tuple      =			PriorTransformer
						a__		:Tuple      =			'hidden_states'





						@property
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Any  =		4
												UpperCamelCase_		:							Union[str, Any]  =		8
												UpperCamelCase_		:							Tuple  =		7

												UpperCamelCase_		:							str  =		floats_tensor((batch_size, embedding_dim)						).to(__lowerCAmelCase						)

												UpperCamelCase_		:							Dict  =		floats_tensor((batch_size, embedding_dim)						).to(__lowerCAmelCase						)
												UpperCamelCase_		:							Tuple  =		floats_tensor((batch_size, num_embeddings, embedding_dim)						).to(__lowerCAmelCase						)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}





						def        A_							(self			,     __UpperCamelCase=0						)				->				str:
												torch.manual_seed(__lowerCAmelCase						)
												UpperCamelCase_		:							List[str]  =		4
												UpperCamelCase_		:							int  =		8
												UpperCamelCase_		:							List[str]  =		7

												UpperCamelCase_		:							List[Any]  =		torch.randn((batch_size, embedding_dim)						).to(__lowerCAmelCase						)

												UpperCamelCase_		:							Optional[int]  =		torch.randn((batch_size, embedding_dim)						).to(__lowerCAmelCase						)
												UpperCamelCase_		:							int  =		torch.randn((batch_size, num_embeddings, embedding_dim)						).to(__lowerCAmelCase						)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}





						@property
						def        A_							(self						)				->				Union[str, Any]:
												return (4, 8)





						@property
						def        A_							(self						)				->				Optional[int]:
												return (4, 8)





						def        A_							(self						)				->				Any:
												UpperCamelCase_		:							Any  =		{
												    """num_attention_heads""": 2,
												    """attention_head_dim""": 4,
												    """num_layers""": 2,
												    """embedding_dim""": 8,
												    """num_embeddings""": 7,
												    """additional_embeddings""": 4,
												}
												UpperCamelCase_		:							str  =		self.dummy_input
												return init_dict, inputs_dict





						def        A_							(self						)				->				int:
												UpperCamelCase_,UpperCamelCase_		:							str  =		PriorTransformer.from_pretrained(
												    """hf-internal-testing/prior-dummy"""			,     output_loading_info=__lowerCAmelCase						)
												self.assertIsNotNone(__lowerCAmelCase						)
												self.assertEqual(len(loading_info["""missing_keys"""]						)			,     0						)

												model.to(__lowerCAmelCase						)
												UpperCamelCase_		:							str  =		model(**self.dummy_input						)[0]

												assert hidden_states is not None, "Make sure output is not None"





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		self.prepare_init_args_and_inputs_for_common()

												UpperCamelCase_		:							Union[str, Any]  =		self.model_class(**__lowerCAmelCase						)
												UpperCamelCase_		:							Any  =		inspect.signature(model.forward						)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase_		:							Any  =		[*signature.parameters.keys()]

												UpperCamelCase_		:							Tuple  =		["""hidden_states""", """timestep"""]
												self.assertListEqual(arg_names[:2]			,     __lowerCAmelCase						)





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							Dict  =		PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""						)
												UpperCamelCase_		:							Any  =		model.to(__lowerCAmelCase						)

												if hasattr(__lowerCAmelCase			,     """set_default_attn_processor"""						):
																		model.set_default_attn_processor()

												UpperCamelCase_		:							str  =		self.get_dummy_seed_input()

												with torch.no_grad():
																		UpperCamelCase_		:							List[str]  =		model(**__lowerCAmelCase						)[0]

												UpperCamelCase_		:							Dict  =		output[0, :5].flatten().cpu()
												print(__lowerCAmelCase						)

												# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
												# the expected output slices are not the same for CPU and GPU.
												UpperCamelCase_		:							int  =		torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]						)
												self.assertTrue(torch_all_close(__lowerCAmelCase			,     __lowerCAmelCase			,     rtol=1E-2						)						)


@slow
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=1			,     __UpperCamelCase=768			,     __UpperCamelCase=77			,     __UpperCamelCase=0						)				->				List[Any]:
												torch.manual_seed(__lowerCAmelCase						)
												UpperCamelCase_		:							Tuple  =		batch_size
												UpperCamelCase_		:							str  =		embedding_dim
												UpperCamelCase_		:							Optional[Any]  =		num_embeddings

												UpperCamelCase_		:							str  =		torch.randn((batch_size, embedding_dim)						).to(__lowerCAmelCase						)

												UpperCamelCase_		:							Tuple  =		torch.randn((batch_size, embedding_dim)						).to(__lowerCAmelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.randn((batch_size, num_embeddings, embedding_dim)						).to(__lowerCAmelCase						)

												return {
												    "hidden_states": hidden_states,
												    "timestep": 2,
												    "proj_embedding": proj_embedding,
												    "encoder_hidden_states": encoder_hidden_states,
												}





						def        A_							(self						)				->				Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
						        [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
						        # fmt: on
						    ]						)
						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""			,     subfolder="""prior"""						)
												model.to(__lowerCAmelCase						)
												UpperCamelCase_		:							Tuple  =		self.get_dummy_seed_input(seed=__lowerCAmelCase						)

												with torch.no_grad():
																		UpperCamelCase_		:							Optional[int]  =		model(**__lowerCAmelCase						)[0]

												assert list(sample.shape						) == [1, 768]

												UpperCamelCase_		:							List[str]  =		sample[0, :8].flatten().cpu()
												print(__lowerCAmelCase						)
												UpperCamelCase_		:							List[str]  =		torch.tensor(__lowerCAmelCase						)

												assert torch_all_close(__lowerCAmelCase			,     __lowerCAmelCase			,     atol=1E-3						)







"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[int]="attention"						)					->       str:
      '''simple docstring'''


      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowercase								=      params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Union[str, Any]=False						)					->       str:
      '''simple docstring'''


      if split_mlp_wi:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowercase								=      (wi_a, wi_a)
      else:
            lowercase								=      params[f'{prefix}/layers_{i}/mlp/wi/kernel']

      lowercase								=      params[f'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:int						)					->       Any:
      '''simple docstring'''


      return params[f'{prefix}/layers_{i}/{layer_name}/scale']


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						*, lowerCAmelCase__	:int   ,						lowerCAmelCase__	:bool						)					->       List[str]:
      '''simple docstring'''


      lowercase								=      traverse_util.flatten_dict(variables["""target"""]						)
      lowercase								=      {"""/""".join(lowerCAmelCase__						): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowercase								=      """encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""   ,						lowerCAmelCase__						)

      lowercase								=      collections.OrderedDict()

      # Shared embeddings.
      lowercase								=      old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(lowerCAmelCase__						):
            # Block i, layer 0 (Self Attention).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_attention_layer_norm"""						)
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""attention"""						)
            lowercase								=      layer_norm
            lowercase								=      k.T
            lowercase								=      o.T
            lowercase								=      q.T
            lowercase								=      v.T

            # Block i, layer 1 (MLP).
            lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						"""pre_mlp_layer_norm"""						)
            lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""encoder"""   ,						lowerCAmelCase__						)
            lowercase								=      layer_norm
            if split_mlp_wi:
                  lowercase								=      wi[0].T
                  lowercase								=      wi[1].T
            else:
                  lowercase								=      wi.T
            lowercase								=      wo.T

      lowercase								=      old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      lowercase								=      old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase__						):
                  # Block i, layer 0 (Self Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_self_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""self_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 1 (Cross Attention).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_cross_attention_layer_norm"""						)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      tax_attention_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""encoder_decoder_attention"""						)
                  lowercase								=      layer_norm
                  lowercase								=      k.T
                  lowercase								=      o.T
                  lowercase								=      q.T
                  lowercase								=      v.T

                  # Block i, layer 2 (MLP).
                  lowercase								=      tax_layer_norm_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						"""pre_mlp_layer_norm"""						)
                  lowercase				,				lowercase								=      tax_mlp_lookup(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"""decoder"""   ,						lowerCAmelCase__						)
                  lowercase								=      layer_norm
                  if split_mlp_wi:
                        lowercase								=      wi[0].T
                        lowercase								=      wi[1].T
                  else:
                        lowercase								=      wi.T
                  lowercase								=      wo.T

            lowercase								=      old["""decoder/decoder_norm/scale"""]
            lowercase								=      old[
                """decoder/relpos_bias/rel_embedding"""
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  lowercase								=      old["""decoder/logits_dense/kernel"""].T

      return new


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:bool						)					->       int:
      '''simple docstring'''


      lowercase								=      collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            lowercase								=      state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowercase								=      state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""						)
                  lowercase								=      state_dict["""shared.weight"""]

      return state_dict


def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:List[Any]						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      checkpoints.load_tax_checkpoint(lowerCAmelCase__						)
      lowercase								=      convert_tax_to_pytorch(lowerCAmelCase__   ,						num_layers=config.num_layers   ,						is_encoder_only=lowerCAmelCase__						)
      lowercase								=      make_state_dict(lowerCAmelCase__   ,						lowerCAmelCase__						)
      model.load_state_dict(lowerCAmelCase__   ,						strict=lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Union[str, Any]   ,						lowerCAmelCase__	:bool = False						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      TaConfig.from_json_file(lowerCAmelCase__						)
      print(f'Building PyTorch model from configuration: {config}'						)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            lowercase								=      TaEncoderModel(lowerCAmelCase__						)
      else:
            lowercase								=      TaForConditionalGeneration(lowerCAmelCase__						)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

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

      # Verify that we can load the checkpoint.
      model.from_pretrained(lowerCAmelCase__						)
      print("""Done"""						)


if __name__ == "__main__":
       __lowerCAmelCase       :			List[Any]   	=argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       __lowerCAmelCase       :			List[Any]   	=parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )



