





from pickle import UnpicklingError

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

from ..utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	try:
		with open(__a   , 'rb'   ) as flax_state_f:
			snake_case_		:			Optional[int]											=	from_bytes(__a   , flax_state_f.read()   )
	except UnpicklingError as e:
		try:
			with open(__a   ) as f:
				if f.read().startswith('version'   ):
					raise OSError(
					    'You seem to have cloned a repository without having git-lfs installed. Please'
					    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
					    ' folder you cloned.'   )
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """   )

	return load_flax_weights_in_pytorch_model(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    'Loading 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
	snake_case_		:			str											=	flatten_dict(jax.tree_util.tree_map(lambda __a   : x.dtype == jnp.bfloataa   , __a   )   ).values()
	if any(__a   ):
		# convert all weights to fp32 if they 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.'   )
		snake_case_		:			List[str]											=	jax.tree_util.tree_map(
		    lambda __a   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params   , __a   )

	snake_case_		:			int											=	''

	snake_case_		:			Union[str, Any]											=	flatten_dict(__a   , sep='.'   )
	snake_case_		:			List[str]											=	pt_model.state_dict()

	# keep track of unexpected & missing keys
	snake_case_		:			List[str]											=	[]
	snake_case_		:			Any											=	set(pt_model_dict.keys()   )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		snake_case_		:			List[Any]											=	flax_key_tuple.split('.'   )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			snake_case_		:			int											=	flax_key_tuple_array[:-1] + ['weight']
			snake_case_		:			Optional[Any]											=	jnp.transpose(__a   , (3, 2, 0, 1)   )
		elif flax_key_tuple_array[-1] == "kernel":
			snake_case_		:			Union[str, Any]											=	flax_key_tuple_array[:-1] + ['weight']
			snake_case_		:			Dict											=	flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			snake_case_		:			int											=	flax_key_tuple_array[:-1] + ['weight']

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(__a   ):
				snake_case_		:			Optional[int]											=	(
				    flax_key_tuple_string.replace('_0'   , '.0'   )
				    .replace('_1'   , '.1'   )
				    .replace('_2'   , '.2'   )
				    .replace('_3'   , '.3'   )
				    .replace('_4'   , '.4'   )
				    .replace('_5'   , '.5'   )
				    .replace('_6'   , '.6'   )
				    .replace('_7'   , '.7'   )
				    .replace('_8'   , '.8'   )
				    .replace('_9'   , '.9'   )
				)

		snake_case_		:			Dict											=	'.'.join(__a   )

		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
				snake_case_		:			int											=	np.asarray(__a   ) if not isinstance(__a   , np.ndarray   ) else flax_tensor
				snake_case_		:			Dict											=	torch.from_numpy(__a   )
				# remove from missing keys
				missing_keys.remove(__a   )
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(__a   )

	pt_model.load_state_dict(__a   )

	# re-transform missing_keys to list
	snake_case_		:			Tuple											=	list(__a   )

	if len(__a   ) > 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).'   )
	if len(__a   ) > 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.'   )

	return pt_model








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE      	=		Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# customize the test name generator function as we want both params to appear in the sub-test
	# name, as by default it shows only the first param
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	self.run_trainer(
							    stage=_A		,   model_name=_A		,   eval_steps=_A		,   num_train_epochs=1		,   distributed=_A		,   fpaa=_A		,   )

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

							if fpaa:
								args.extend(['--fp16']	)

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_A		,   env=self.get_env()	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

