


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
		import tensorflow as tf


__A	=logging.get_logger(__name__)


@dataclass
class 		_snake_case					(     UpperCAmelCase_	):
					lowerCAmelCase      :int   =					[
					    '''no_inference''',
					    '''no_cuda''',
					    '''no_tpu''',
					    '''no_speed''',
					    '''no_memory''',
					    '''no_env_print''',
					    '''no_multi_process''',
					]




					def __init__(      self							,							**_lowerCamelCase):
								for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
														UpperCAmelCase__  :					str        =     deprecated_arg[3:]
														UpperCAmelCase__  :					List[str]        =     not kwargs.pop(_lowercase)
														logger.warning(
														    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
														    f''' {positive_arg}={kwargs[positive_arg]}''')
								UpperCAmelCase__  :					Dict        =     kwargs.pop("""tpu_name"""							,							self.tpu_name)
								UpperCAmelCase__  :					Optional[int]        =     kwargs.pop("""device_idx"""							,							self.device_idx)
								UpperCAmelCase__  :					Union[str, Any]        =     kwargs.pop("""eager_mode"""							,							self.eager_mode)
								UpperCAmelCase__  :					Union[str, Any]        =     kwargs.pop("""use_xla"""							,							self.use_xla)
								super().__init__(**_lowercase)

					lowerCAmelCase      :List[Any]   =					field(
					    default=UpperCAmelCase_       ,   metadata={'''help''': '''Name of TPU'''}       ,   )
					lowerCAmelCase      :int   =					field(
					    default=0       ,   metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}       ,   )
					lowerCAmelCase      :Optional[Any]   =					field(default=UpperCAmelCase_       ,   metadata={'''help''': '''Benchmark models in eager model.'''}	)
					lowerCAmelCase      :Tuple   =					field(
					    default=UpperCAmelCase_       ,   metadata={
					        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
					    }       ,   )




					@cached_property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								UpperCAmelCase__  :					Optional[Any]        =     None
								if self.tpu:
											try:
														if self.tpu_name:
																	UpperCAmelCase__  :					Dict        =     tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
														else:
																	UpperCAmelCase__  :					Optional[Any]        =     tf.distribute.cluster_resolver.TPUClusterResolver()
											except ValueError:
														UpperCAmelCase__  :					Any        =     None
								return tpu




					@cached_property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								if self.is_tpu:
											tf.config.experimental_connect_to_cluster(self._setup_tpu)
											tf.tpu.experimental.initialize_tpu_system(self._setup_tpu)

											UpperCAmelCase__  :					Optional[Any]        =     tf.distribute.TPUStrategy(self._setup_tpu)
								else:
											# currently no multi gpu is allowed
											if self.is_gpu:
														# TODO: Currently only single GPU is supported
														tf.config.set_visible_devices(self.gpu_list[self.device_idx]							,							"""GPU""")
														UpperCAmelCase__  :					Any        =     tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''')
											else:
														tf.config.set_visible_devices([]							,							"""GPU""")  # disable GPU
														UpperCAmelCase__  :					int        =     tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''')

								return strategy




					@property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								return self._setup_tpu is not None




					@property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								return self._setup_strategy




					@property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								return tf.config.list_physical_devices("""GPU""")




					@property
					def 				snake_case__						(      self):
								requires_backends(self							,							["""tf"""])
								if self.cuda:
											return len(self.gpu_list)
								return 0




					@property
					def 				snake_case__						(      self):
								return self.n_gpu > 0



'''simple docstring'''



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)