


"""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 _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase="attention"		):







				"""simple docstring"""
				a					=params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				a					=params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				a					=params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
				a					=params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
				return k, o, q, v
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase=False		):







				"""simple docstring"""





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

				a					=params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase		):







				"""simple docstring"""
				return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def _A						(						lowercase	,   *, lowercase	,   lowercase		):







				"""simple docstring"""
				a					=traverse_util.flatten_dict(variables['''target''']		)
				a					={"""/""".join(lowercase__		): v for k, v in old.items()}

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

				a					=collections.OrderedDict()

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

				# Encoder.
				for i in range(lowercase__		):
								# Block i, layer 0 (Self Attention).
								a					=tax_layer_norm_lookup(lowercase__	,   lowercase__	,   '''encoder'''	,   '''pre_attention_layer_norm'''		)
								a					=tax_attention_lookup(lowercase__	,   lowercase__	,   '''encoder'''	,   '''attention'''		)
								a					=layer_norm
								a					=k.T
								a					=o.T
								a					=q.T
								a					=v.T

								# Block i, layer 1 (MLP).
								a					=tax_layer_norm_lookup(lowercase__	,   lowercase__	,   '''encoder'''	,   '''pre_mlp_layer_norm'''		)
								a					=tax_mlp_lookup(lowercase__	,   lowercase__	,   '''encoder'''	,   lowercase__		)
								a					=layer_norm
								if split_mlp_wi:
												a					=wi[0].T
												a					=wi[1].T
								else:
												a					=wi.T
								a					=wo.T

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

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

												# Block i, layer 1 (Cross Attention).
												a					=tax_layer_norm_lookup(lowercase__	,   lowercase__	,   '''decoder'''	,   '''pre_cross_attention_layer_norm'''		)
												a					=tax_attention_lookup(lowercase__	,   lowercase__	,   '''decoder'''	,   '''encoder_decoder_attention'''		)
												a					=layer_norm
												a					=k.T
												a					=o.T
												a					=q.T
												a					=v.T

												# Block i, layer 2 (MLP).
												a					=tax_layer_norm_lookup(lowercase__	,   lowercase__	,   '''decoder'''	,   '''pre_mlp_layer_norm'''		)
												a					=tax_mlp_lookup(lowercase__	,   lowercase__	,   '''decoder'''	,   lowercase__		)
												a					=layer_norm
												if split_mlp_wi:
																a					=wi[0].T
																a					=wi[1].T
												else:
																a					=wi.T
												a					=wo.T

								a					=old["""decoder/decoder_norm/scale"""]
								a					=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:
												a					=old["""decoder/logits_dense/kernel"""].T

				return new
def _A						(						lowercase	,   lowercase		):







				"""simple docstring"""
				a					=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:
								a					=state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												a					=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.'''		)
												a					=state_dict["""shared.weight"""]

				return state_dict
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase		):







				"""simple docstring"""
				a					=checkpoints.load_tax_checkpoint(lowercase__		)
				a					=convert_tax_to_pytorch(lowercase__	,   num_layers=config.num_layers	,   is_encoder_only=lowercase__		)
				a					=make_state_dict(lowercase__	,   lowercase__		)
				model.load_state_dict(lowercase__	,   strict=lowercase__		)
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase = False		):







				"""simple docstring"""
				a					=TaConfig.from_json_file(lowercase__		)
				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:
								a					=TaEncoderModel(lowercase__		)
				else:
								a					=TaForConditionalGeneration(lowercase__		)

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowercase__		)
				print('''Done'''		)


if __name__ == "__main__":
							lowerCamelCase_       :       List[str]      						=  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_       :       int      						=  parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)



"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  42

 # setable values
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  None


 @classmethod
 def 				SCREAMING_SNAKE_CASE							(		cls			,   __A			,   __A			,   __A						) ->					List[str]:
     return cls(common=__A			,   init_noise_sigma=__A			,   timesteps=__A						)






@dataclass
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCAmelCase			 =  42


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return True


 @register_to_config
 def __init__(		self			,   __A = 1000			,   __A = 0.0_001			,   __A = 0.02			,   __A = "linear"			,   __A = None			,   __A = "fixed_small"			,   __A = True			,   __A = "epsilon"			,   __A = jnp.floataa			,   ) ->					List[Any]:
     a					=dtype


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A = None						) ->					DDPMSchedulerState:
     if common is None:
         a					=CommonSchedulerState.create(self						)

     # standard deviation of the initial noise distribution
     a					=jnp.array(1.0			,   dtype=self.dtype						)

     a					=jnp.arange(0			,   self.config.num_train_timesteps						).round()[::-1]

     return DDPMSchedulerState.create(
         common=__A			,   init_noise_sigma=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None						) ->					jnp.ndarray:
     return sample


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = ()						) ->					DDPMSchedulerState:
     a					=self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     a					=(jnp.arange(0			,   __A						) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=None			,   __A=None						) ->					str:
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     a					=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         a					=self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         a					=jnp.clip(__A			,   a_min=1E-2_0						)
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         a					=jnp.log(jnp.clip(__A			,   a_min=1E-2_0						)						)
     elif variance_type == "fixed_large":
         a					=state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         a					=jnp.log(state.common.betas[t]						)
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         a					=variance
         a					=state.common.betas[t]
         a					=(predicted_variance + 1) / 2
         a					=frac * max_log + (1 - frac) * min_log

     return variance


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A = None			,   __A = True			,   ) ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
     a					=timestep

     if key is None:
         a					=jax.random.PRNGKey(0						)

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         a		,				a					=jnp.split(__A			,   sample.shape[1]			,   axis=1						)
     else:
         a					=None

     # 1. compute alphas, betas
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)
     a					=1 - alpha_prod_t
     a					=1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if self.config.prediction_type == "epsilon":
         a					=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         a					=model_output
     elif self.config.prediction_type == "v_prediction":
         a					=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
             ''' for the FlaxDDPMScheduler.'''						)

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         a					=jnp.clip(__A			,   -1			,   1						)

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     a					=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     a					=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     a					=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         a					=jax.random.split(__A			,   num=1						)
         a					=jax.random.normal(__A			,   shape=model_output.shape			,   dtype=self.dtype						)
         return (self._get_variance(__A			,   __A			,   predicted_variance=__A						) ** 0.5) * noise

     a					=jnp.where(t > 0			,   random_variance()			,   jnp.zeros(model_output.shape			,   dtype=self.dtype						)						)

     a					=pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A			,   state=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return add_noise_common(state.common			,   __A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return get_velocity_common(state.common			,   __A			,   __A			,   __A						)





 def __len__(		self						) ->					Optional[int]:
     return self.config.num_train_timesteps