






"""simple docstring"""







import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__a       :	Union[str, Any]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.''')
										raise

					if not is_sharded:
										a__          =  os.path.abspath(lowerCamelCase_)
										logger.info(f'Loading PyTorch weights from {pt_path}')

										a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')
										logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.')

										a__          =  convert_pytorch_state_dict_to_flax(lowerCamelCase_ ,     lowerCamelCase_)
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										a__          =  convert_pytorch_sharded_state_dict_to_flax(lowerCamelCase_ ,     lowerCamelCase_)
					return flax_state_dict





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     ):

					def is_key_or_prefix_key_in_dict(lowerCamelCase_) -> bool:
										return len(set(lowerCamelCase_) & {key, (model_prefix,) + key}) > 0

					# layer norm
					a__          =  pt_tuple_key[:-1] + ('''scale''',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCamelCase_):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					a__          =  pt_tuple_key[:-1] + ('''mean''',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCamelCase_):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					a__          =  pt_tuple_key[:-1] + ('''var''',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase_):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					a__          =  pt_tuple_key[:-1] + ('''embedding''',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase_):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					a__          =  pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCamelCase_):
										a__          =  pt_tensor.transpose(2 ,     3 ,     1 ,     0)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					a__          =  pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase_):
										a__          =  pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

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

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					a__          =  None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										a__          =  pt_tuple_key[-2] + '''_g'''
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										a__          =  pt_tuple_key[-2] + '''_v'''
					if name is not None:
										a__          =  pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					# convert pytorch tensor to numpy
					a__          =  {k: v.numpy() for k, v in pt_state_dict.items()}

					a__          =  flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										a__          =  flax_model.params['''params''']
					else:
										a__          =  flax_model.params
					a__          =  flatten_dict(lowerCamelCase_)

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										a__          =  flatten_dict(flax_model.params['''batch_stats'''])
										random_flax_state_dict.update(lowerCamelCase_)

					a__          =  {}

					a__          =  (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('''.''')[0] for k in pt_state_dict.keys()}
					)
					a__          =  (model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('''.''')[0] for k in pt_state_dict.keys()}
					)

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										a__          =  tuple(pt_key.split('''.'''))

										# remove base model prefix if necessary
										a__          =  pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															a__          =  pt_tuple_key[1:]

										# Correctly rename weight parameters
										a__       ,a__          =  rename_key_and_reshape_tensor(
										    lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										# add model prefix if necessary
										a__          =  (model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															a__          =  (model_prefix,) + flax_key

										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}.')

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				a__          =  jnp.asarray(lowerCamelCase_)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(lowerCamelCase_ ,     lowerCamelCase_)
																				continue

															# also add unexpected weight so that warning is thrown
															a__          =  jnp.asarray(lowerCamelCase_)

										else:
															# also add unexpected weight so that warning is thrown
															a__          =  jnp.asarray(lowerCamelCase_)

					return unflatten_dict(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					import torch

					# Load the index
					a__          =  {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										a__          =  torch.load(lowerCamelCase_)
										a__          =  {k: v.numpy() for k, v in pt_state_dict.items()}

										a__          =  flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															a__          =  flax_model.params['''params''']

															a__          =  flatten_dict(lowerCamelCase_)
															random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']))
										else:
															a__          =  flax_model.params
															a__          =  flatten_dict(lowerCamelCase_)

										a__          =  (model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('''.''')[0] for k in pt_state_dict.keys()}
										)
										a__          =  (model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('''.''')[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															a__          =  tuple(pt_key.split('''.'''))

															# remove base model prefix if necessary
															a__          =  pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				a__          =  pt_tuple_key[1:]

															# Correctly rename weight parameters
															a__       ,a__          =  rename_key_and_reshape_tensor(
															    lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
															# add model prefix if necessary
															a__          =  (model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				a__          =  (model_prefix,) + flax_key

															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}.')

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									a__          =  jnp.asarray(lowerCamelCase_)
																									continue
																				if "var" in flax_key[-1]:
																									a__          =  jnp.asarray(lowerCamelCase_)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(lowerCamelCase_ ,     lowerCamelCase_)
																									continue

																				# also add unexpected weight so that warning is thrown
																				a__          =  jnp.asarray(lowerCamelCase_)

															else:
																				# also add unexpected weight so that warning is thrown
																				a__          =  jnp.asarray(lowerCamelCase_)
					return unflatten_dict(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  os.path.abspath(lowerCamelCase_)
					logger.info(f'Loading Flax weights from {flax_checkpoint_path}')

					# import correct flax class
					a__          =  getattr(lowerCamelCase_ ,     '''Flax''' + model.__class__.__name__)

					# load flax weight dict
					with open(lowerCamelCase_ ,     '''rb''') as state_f:
										try:
															a__          =  from_bytes(lowerCamelCase_ ,     state_f.read())
										except UnpicklingError:
															raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ')

					return load_flax_weights_in_pytorch_model(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.''')
										raise

					# check if we have bf16 weights
					a__          =  flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase_: x.dtype == jnp.bfloataa ,     lowerCamelCase_)).values()
					if any(lowerCamelCase_):
										# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
										    '''before loading those in PyTorch model.''')
										a__          =  jax.tree_util.tree_map(
										    lambda lowerCamelCase_: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params ,     lowerCamelCase_)

					a__          =  flatten_dict(lowerCamelCase_)
					a__          =  pt_model.state_dict()

					a__          =  (pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('''.''')[0] for k in pt_model_dict.keys()}
					)
					a__          =  (pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('''.''')[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					a__          =  []
					a__          =  set(pt_model_dict.keys())

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										a__          =  flax_key_tuple[0] == pt_model.base_model_prefix
										a__          =  '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															a__          =  flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															a__          =  (pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCamelCase_) not in pt_model_dict:
															# conv layer
															a__          =  flax_key_tuple[:-1] + ('''weight''',)
															a__          =  jnp.transpose(lowerCamelCase_ ,     (3, 2, 0, 1))
										elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_) not in pt_model_dict:
															# linear layer
															a__          =  flax_key_tuple[:-1] + ('''weight''',)
															a__          =  flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															a__          =  flax_key_tuple[:-1] + ('''weight''',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															a__          =  flax_key_tuple[:-1] + ('''running_mean''',)
										elif "var" in flax_key_tuple[-1]:
															a__          =  flax_key_tuple[:-1] + ('''running_var''',)

										if "batch_stats" in flax_state:
															a__          =  '''.'''.join(flax_key_tuple[1:])  # Remove the params/batch_stats header
										else:
															a__          =  '''.'''.join(lowerCamelCase_)

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										a__          =  {}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															a__          =  key.split('''.''')
															a__          =  None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				a__          =  key_components[-2] + '''_g'''
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				a__          =  key_components[-2] + '''_v'''
															if name is not None:
																				a__          =  key_components[:-3] + [name]
																				a__          =  '''.'''.join(lowerCamelCase_)
																				a__          =  key

										if flax_key in special_pt_names:
															a__          =  special_pt_names[flax_key]

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																				    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.')
															else:
																				# add weight to pytorch dict
																				a__          =  np.asarray(lowerCamelCase_) if not isinstance(lowerCamelCase_ ,     np.ndarray) else flax_tensor
																				a__          =  torch.from_numpy(lowerCamelCase_)
																				# remove from missing keys
																				missing_keys.remove(lowerCamelCase_)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(lowerCamelCase_)

					pt_model.load_state_dict(lowerCamelCase_)

					# re-transform missing_keys to list
					a__          =  list(lowerCamelCase_)

					if len(lowerCamelCase_) > 0:
										logger.warning(
										    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
										    f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
										    f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
										    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
										    f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
										    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
										    ''' FlaxBertForSequenceClassification model).''')
					else:
										logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n')
					if len(lowerCamelCase_) > 0:
										logger.warning(
										    f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
										    f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
										    ''' use it for predictions and inference.''')
					else:
										logger.warning(
										    f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
										    '''If your task is similar to the task the model of the checkpoint was trained on, '''
										    f'you can already use {pt_model.__class__.__name__} for predictions without further training.')

					return pt_model










"""simple docstring"""







import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
__a       :	Optional[int]             = logging.getLogger()

__a       :	Any             = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												os.makedirs(__A				,		exist_ok=__A    )
												a__          =  {'''source''': '''What is love ?''', '''target''': '''life'''}
												a__          =  {'''train''': 12, '''val''': 2, '''test''': 2}
												for split in ["train", "test", "val"]:
																	for field in ["source", "target"]:
																						a__          =  '''\n'''.join([contents[field]] * n_lines[split]    )
																						with open(os.path.join(__A				,		F'{split}.{field}'    )				,		'''w'''    ) as f:
																											f.write(__A    )




							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     str = "pytorch"    ):

												'''simple docstring'''

												a__          =  self.get_auto_remove_tmp_dir()
												a__          =  os.path.join(__A				,		'''output'''    )
												a__          =  os.path.join(__A				,		'''data'''    )
												self._create_dummy_data(data_dir=__A    )

												a__          =  F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

												if gpus > 0:
																	testargs.append(F'--gpus={gpus}'    )
																	if is_apex_available():
																						testargs.append('''--fp16'''    )
												else:
																	testargs.append('''--gpus=0'''    )
																	testargs.append('''--distributed_backend=ddp_cpu'''    )
																	testargs.append('''--num_processes=2'''    )

												a__          =  [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
												execute_subprocess_async(__A				,		env=self.get_env()    )

												a__          =  os.path.join(__A				,		'''metrics.json'''    )
												with open(__A    ) as f:
																	a__          =  json.load(__A    )
												return result




							@require_torch_gpu
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_multi_gpu
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=2    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )




							@require_torch_gpu
							@require_ray
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )





							@require_torch_multi_gpu
							@require_ray
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self._run_finetune(gpus=1				,		distributed_retriever='''ray'''    )
												self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']				,		0.2    )


