




"""simple docstring"""





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.generation import (
					    FlaxForcedBOSTokenLogitsProcessor,
					    FlaxForcedEOSTokenLogitsProcessor,
					    FlaxLogitsProcessorList,
					    FlaxMinLengthLogitsProcessor,
					    FlaxTemperatureLogitsWarper,
					    FlaxTopKLogitsWarper,
					    FlaxTopPLogitsWarper,
					)



@require_flax
class   a__					(						unittest.TestCase ):


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       jnp.ones((batch_size, length)) / length
						return scores


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       None
						__UpperCAmelCase						:			int												=       20

						__UpperCAmelCase						:			int												=       self._get_uniform_logits(batch_size=2   ,	length=UpperCamelCase_)

						# tweak scores to not be uniform anymore
						__UpperCAmelCase						:			List[Any]												=       scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
						__UpperCAmelCase						:			int												=       scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

						# compute softmax
						__UpperCAmelCase						:			List[str]												=       jax.nn.softmax(UpperCamelCase_   ,	axis=-1)

						__UpperCAmelCase						:			str												=       FlaxTemperatureLogitsWarper(temperature=0.5)
						__UpperCAmelCase						:			Tuple												=       FlaxTemperatureLogitsWarper(temperature=1.3)

						__UpperCAmelCase						:			Union[str, Any]												=       jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase_   ,	scores.copy()   ,	cur_len=UpperCamelCase_)   ,	axis=-1)
						__UpperCAmelCase						:			List[Any]												=       jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase_   ,	scores.copy()   ,	cur_len=UpperCamelCase_)   ,	axis=-1)

						# uniform distribution stays uniform
						self.assertTrue(jnp.allclose(probs[0, :]   ,	warped_prob_sharp[0, :]   ,	atol=1e-3))
						self.assertTrue(jnp.allclose(probs[0, :]   ,	warped_prob_smooth[0, :]   ,	atol=1e-3))

						# sharp peaks get higher, valleys get lower
						self.assertLess(probs[1, :].max()   ,	warped_prob_sharp[1, :].max())
						self.assertGreater(probs[1, :].min()   ,	warped_prob_sharp[1, :].min())

						# smooth peaks get lower, valleys get higher
						self.assertGreater(probs[1, :].max()   ,	warped_prob_smooth[1, :].max())
						self.assertLess(probs[1, :].min()   ,	warped_prob_smooth[1, :].min())


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       None
						__UpperCAmelCase						:			Tuple												=       10
						__UpperCAmelCase						:			Any												=       2

						# create ramp distribution
						__UpperCAmelCase						:			Tuple												=       np.broadcast_to(np.arange(UpperCamelCase_)[None, :]   ,	(batch_size, vocab_size)).copy()
						__UpperCAmelCase						:			Dict												=       ramp_logits[1:, : vocab_size // 2] + vocab_size

						__UpperCAmelCase						:			str												=       FlaxTopKLogitsWarper(3)

						__UpperCAmelCase						:			Union[str, Any]												=       top_k_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)

						# check that correct tokens are filtered
						self.assertListEqual(jnp.isinf(scores[0]).tolist()   ,	7 * [True] + 3 * [False])
						self.assertListEqual(jnp.isinf(scores[1]).tolist()   ,	2 * [True] + 3 * [False] + 5 * [True])

						# check special case
						__UpperCAmelCase						:			Tuple												=       5
						__UpperCAmelCase						:			List[Any]												=       FlaxTopKLogitsWarper(top_k=1   ,	filter_value=0.0   ,	min_tokens_to_keep=3)

						__UpperCAmelCase						:			str												=       np.broadcast_to(np.arange(UpperCamelCase_)[None, :]   ,	(batch_size, length)).copy()
						__UpperCAmelCase						:			str												=       top_k_warp_safety_check(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)

						# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
						self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()   ,	[2, 2])


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       None
						__UpperCAmelCase						:			Optional[int]												=       10
						__UpperCAmelCase						:			Dict												=       2

						# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
						__UpperCAmelCase						:			List[Any]												=       np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

						__UpperCAmelCase						:			Optional[Any]												=       FlaxTopPLogitsWarper(0.8)
						__UpperCAmelCase						:			int												=       np.exp(top_p_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_))

						# dist should be filtered to keep min num values so that sum is >= top_p
						# exp (-inf) => 0
						__UpperCAmelCase						:			Optional[Any]												=       np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
						self.assertTrue(np.allclose(UpperCamelCase_   ,	UpperCamelCase_   ,	atol=1e-3))

						# check edge cases with negative and extreme logits
						__UpperCAmelCase						:			Optional[Any]												=       np.broadcast_to(np.arange(UpperCamelCase_)[None, :]   ,	(batch_size, vocab_size)).copy() - (
						    vocab_size // 2
						)

						# make ramp_logits more extreme
						__UpperCAmelCase						:			int												=       ramp_logits[1] * 100.0

						# make sure at least 2 tokens are kept
						__UpperCAmelCase						:			Optional[int]												=       FlaxTopPLogitsWarper(0.9   ,	min_tokens_to_keep=2   ,	filter_value=0.0)
						__UpperCAmelCase						:			int												=       top_p_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)

						# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
						self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()   ,	[3, 2])


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       20
						__UpperCAmelCase						:			Optional[Any]												=       4
						__UpperCAmelCase						:			Union[str, Any]												=       0

						__UpperCAmelCase						:			int												=       FlaxMinLengthLogitsProcessor(min_length=10   ,	eos_token_id=UpperCamelCase_)

						# check that min length is applied at length 5
						__UpperCAmelCase						:			List[Any]												=       ids_tensor((batch_size, 20)   ,	vocab_size=20)
						__UpperCAmelCase						:			int												=       5
						__UpperCAmelCase						:			Any												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			int												=       min_dist_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,	4 * [-float("inf")])

						# check that min length is not applied anymore at length 15
						__UpperCAmelCase						:			List[str]												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Dict												=       15
						__UpperCAmelCase						:			Optional[int]												=       min_dist_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertFalse(jnp.isinf(UpperCamelCase_).any())


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       20
						__UpperCAmelCase						:			Optional[int]												=       4
						__UpperCAmelCase						:			Tuple												=       0

						__UpperCAmelCase						:			Any												=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)

						# check that all scores are -inf except the bos_token_id score
						__UpperCAmelCase						:			Optional[Any]												=       ids_tensor((batch_size, 1)   ,	vocab_size=20)
						__UpperCAmelCase						:			Union[str, Any]												=       1
						__UpperCAmelCase						:			List[Any]												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       logits_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
						self.assertListEqual(scores[:, bos_token_id].tolist()   ,	4 * [0])  # score for bos_token_id shold be zero

						# check that bos_token_id is not forced if current length is greater than 1
						__UpperCAmelCase						:			List[Any]												=       3
						__UpperCAmelCase						:			str												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       logits_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertFalse(jnp.isinf(UpperCamelCase_).any())


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       20
						__UpperCAmelCase						:			List[str]												=       4
						__UpperCAmelCase						:			Optional[Any]												=       0
						__UpperCAmelCase						:			List[Any]												=       5

						__UpperCAmelCase						:			Optional[Any]												=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_)

						# check that all scores are -inf except the eos_token_id when max_length is reached
						__UpperCAmelCase						:			Optional[Any]												=       ids_tensor((batch_size, 4)   ,	vocab_size=20)
						__UpperCAmelCase						:			Optional[int]												=       4
						__UpperCAmelCase						:			int												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			int												=       logits_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
						self.assertListEqual(scores[:, eos_token_id].tolist()   ,	4 * [0])  # score for eos_token_id should be zero

						# check that eos_token_id is not forced if max_length is not reached
						__UpperCAmelCase						:			Dict												=       3
						__UpperCAmelCase						:			Optional[Any]												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Dict												=       logits_processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						self.assertFalse(jnp.isinf(UpperCamelCase_).any())


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       4
						__UpperCAmelCase						:			Optional[Any]												=       10
						__UpperCAmelCase						:			Any												=       15
						__UpperCAmelCase						:			List[str]												=       2
						__UpperCAmelCase						:			Union[str, Any]												=       1
						__UpperCAmelCase						:			Tuple												=       15

						# dummy input_ids and scores
						__UpperCAmelCase						:			Dict												=       ids_tensor((batch_size, sequence_length)   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       input_ids.copy()

						__UpperCAmelCase						:			Dict												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       scores.copy()

						# instantiate all dist processors
						__UpperCAmelCase						:			Union[str, Any]												=       FlaxTemperatureLogitsWarper(temperature=0.5)
						__UpperCAmelCase						:			List[str]												=       FlaxTopKLogitsWarper(3)
						__UpperCAmelCase						:			str												=       FlaxTopPLogitsWarper(0.8)

						# instantiate all logits processors
						__UpperCAmelCase						:			List[Any]												=       FlaxMinLengthLogitsProcessor(min_length=10   ,	eos_token_id=UpperCamelCase_)
						__UpperCAmelCase						:			str												=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)
						__UpperCAmelCase						:			List[Any]												=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_)

						__UpperCAmelCase						:			Any												=       10

						# no processor list
						__UpperCAmelCase						:			List[str]												=       temp_dist_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       top_k_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       top_p_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						__UpperCAmelCase						:			int												=       min_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       bos_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       eos_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)

						# with processor list
						__UpperCAmelCase						:			str												=       FlaxLogitsProcessorList(
						    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
						__UpperCAmelCase						:			Optional[int]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)

						# scores should be equal
						self.assertTrue(jnp.allclose(UpperCamelCase_   ,	UpperCamelCase_   ,	atol=1e-3))

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist()   ,	input_ids_comp.tolist())


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       4
						__UpperCAmelCase						:			str												=       10
						__UpperCAmelCase						:			Optional[int]												=       15
						__UpperCAmelCase						:			int												=       2
						__UpperCAmelCase						:			List[str]												=       1
						__UpperCAmelCase						:			Dict												=       15

						# dummy input_ids and scores
						__UpperCAmelCase						:			Any												=       ids_tensor((batch_size, sequence_length)   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       input_ids.copy()

						__UpperCAmelCase						:			str												=       self._get_uniform_logits(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			int												=       scores.copy()

						# instantiate all dist processors
						__UpperCAmelCase						:			Union[str, Any]												=       FlaxTemperatureLogitsWarper(temperature=0.5)
						__UpperCAmelCase						:			Optional[int]												=       FlaxTopKLogitsWarper(3)
						__UpperCAmelCase						:			str												=       FlaxTopPLogitsWarper(0.8)

						# instantiate all logits processors
						__UpperCAmelCase						:			List[str]												=       FlaxMinLengthLogitsProcessor(min_length=10   ,	eos_token_id=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_)

						__UpperCAmelCase						:			Optional[Any]												=       10

						# no processor list
						def run_no_processor_list(UpperCamelCase_   :     Union[str, Any]   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Dict):
									__UpperCAmelCase						:			Any												=       temp_dist_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									__UpperCAmelCase						:			List[str]												=       top_k_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									__UpperCAmelCase						:			int												=       top_p_warp(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									__UpperCAmelCase						:			int												=       min_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									__UpperCAmelCase						:			int												=       bos_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									__UpperCAmelCase						:			Any												=       eos_dist_proc(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									return scores

						# with processor list
						def run_processor_list(UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     Dict):
									__UpperCAmelCase						:			List[Any]												=       FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
									__UpperCAmelCase						:			Optional[Any]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	cur_len=UpperCamelCase_)
									return scores

						__UpperCAmelCase						:			str												=       jax.jit(UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       jax.jit(UpperCamelCase_)

						__UpperCAmelCase						:			List[Any]												=       jitted_run_no_processor_list(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Dict												=       jitted_run_processor_list(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)

						# scores should be equal
						self.assertTrue(jnp.allclose(UpperCamelCase_   ,	UpperCamelCase_   ,	atol=1e-3))

						# input_ids should never be changed
						self.assertListEqual(input_ids.tolist()   ,	input_ids_comp.tolist())





"""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							=  logging.get_logger(__name__)




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase=False    )	-> Optional[Any]:
      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:
            UpperCAmelCase__			:							Tuple							=    os.path.abspath(lowerCAmelCase    )
            logger.info(F"""Loading PyTorch weights from {pt_path}"""    )

            UpperCAmelCase__			:							Any							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )
            logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters."""    )

            UpperCAmelCase__			:							str							=    convert_pytorch_state_dict_to_flax(lowerCAmelCase			,	lowerCAmelCase    )
      else:
            # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
            UpperCAmelCase__			:							Optional[Any]							=    convert_pytorch_sharded_state_dict_to_flax(lowerCAmelCase			,	lowerCAmelCase    )
      return flax_state_dict




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	)	-> (Tuple[str], np.ndarray):

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

      # layer norm
      UpperCAmelCase__			:							Any							=    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
      UpperCAmelCase__			:							Tuple							=    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
      UpperCAmelCase__			:							Any							=    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
      UpperCAmelCase__			:							Tuple							=    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
      UpperCAmelCase__			:							List[str]							=    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    ):
            UpperCAmelCase__			:							Union[str, Any]							=    pt_tensor.transpose(2			,	3			,	1			,	0    )
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCAmelCase__			:							int							=    pt_tuple_key[:-1] + ("""kernel""",)
      if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCAmelCase    ):
            UpperCAmelCase__			:							str							=    pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

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

      # old PyTorch layer norm bias
      UpperCAmelCase__			:							List[str]							=    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
      UpperCAmelCase__			:							Optional[int]							=    None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            UpperCAmelCase__			:							int							=    pt_tuple_key[-2] + """_g"""
      elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
            UpperCAmelCase__			:							Optional[int]							=    pt_tuple_key[-2] + """_v"""
      if name is not None:
            UpperCAmelCase__			:							List[str]							=    pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Dict:
      # convert pytorch tensor to numpy
      UpperCAmelCase__			:							Optional[int]							=    {k: v.numpy() for k, v in pt_state_dict.items()}

      UpperCAmelCase__			:							Optional[Any]							=    flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers
      if "params" in flax_model.params:
            UpperCAmelCase__			:							Any							=    flax_model.params["""params"""]
      else:
            UpperCAmelCase__			:							Optional[Any]							=    flax_model.params
      UpperCAmelCase__			:							Tuple							=    flatten_dict(lowerCAmelCase    )

      # add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
            UpperCAmelCase__			:							Dict							=    flatten_dict(flax_model.params["""batch_stats"""]    )
            random_flax_state_dict.update(lowerCAmelCase    )

      UpperCAmelCase__			:							List[str]							=    {}

      UpperCAmelCase__			:							str							=    (model_prefix not in flax_model_params) and (
          model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
      )
      UpperCAmelCase__			:							List[str]							=    (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():
            UpperCAmelCase__			:							Optional[Any]							=    tuple(pt_key.split("""."""    )    )

            # remove base model prefix if necessary
            UpperCAmelCase__			:							str							=    pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                  UpperCAmelCase__			:							Optional[int]							=    pt_tuple_key[1:]

            # Correctly rename weight parameters
            UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    rename_key_and_reshape_tensor(
                lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )

            # add model prefix if necessary
            UpperCAmelCase__			:							str							=    (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase__			:							str							=    (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]:
                        UpperCAmelCase__			:							Any							=    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
                  UpperCAmelCase__			:							Union[str, Any]							=    jnp.asarray(lowerCAmelCase    )

            else:
                  # also add unexpected weight so that warning is thrown
                  UpperCAmelCase__			:							Any							=    jnp.asarray(lowerCAmelCase    )

      return unflatten_dict(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      import torch

      # Load the index
      UpperCAmelCase__			:							List[str]							=    {}
      for shard_file in shard_filenames:
            # load using msgpack utils
            UpperCAmelCase__			:							Tuple							=    torch.load(lowerCAmelCase    )
            UpperCAmelCase__			:							Optional[int]							=    {k: v.numpy() for k, v in pt_state_dict.items()}

            UpperCAmelCase__			:							Optional[Any]							=    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:
                  UpperCAmelCase__			:							List[Any]							=    flax_model.params["""params"""]

                  UpperCAmelCase__			:							int							=    flatten_dict(lowerCAmelCase    )
                  random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]    )    )
            else:
                  UpperCAmelCase__			:							Any							=    flax_model.params
                  UpperCAmelCase__			:							List[Any]							=    flatten_dict(lowerCAmelCase    )

            UpperCAmelCase__			:							int							=    (model_prefix not in flax_model_params) and (
                model_prefix in {k.split("""."""    )[0] for k in pt_state_dict.keys()}
            )
            UpperCAmelCase__			:							str							=    (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():
                  UpperCAmelCase__			:							Optional[int]							=    tuple(pt_key.split("""."""    )    )

                  # remove base model prefix if necessary
                  UpperCAmelCase__			:							Dict							=    pt_tuple_key[0] == model_prefix
                  if load_model_with_head_into_base_model and has_base_model_prefix:
                        UpperCAmelCase__			:							Optional[int]							=    pt_tuple_key[1:]

                  # Correctly rename weight parameters
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    rename_key_and_reshape_tensor(
                      lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
                  # add model prefix if necessary
                  UpperCAmelCase__			:							Tuple							=    (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        UpperCAmelCase__			:							Optional[int]							=    (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]:
                              UpperCAmelCase__			:							Any							=    jnp.asarray(lowerCAmelCase    )
                              continue
                        if "var" in flax_key[-1]:
                              UpperCAmelCase__			:							List[str]							=    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
                        UpperCAmelCase__			:							Optional[int]							=    jnp.asarray(lowerCAmelCase    )

                  else:
                        # also add unexpected weight so that warning is thrown
                        UpperCAmelCase__			:							List[str]							=    jnp.asarray(lowerCAmelCase    )
      return unflatten_dict(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							Optional[Any]							=    os.path.abspath(lowerCAmelCase    )
      logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""    )

      # import correct flax class
      UpperCAmelCase__			:							Dict							=    getattr(lowerCAmelCase			,	"""Flax""" + model.__class__.__name__    )

      # load flax weight dict
      with open(lowerCAmelCase			,	"""rb"""    ) as state_f:
            try:
                  UpperCAmelCase__			:							Any							=    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 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[Any]:

      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
      UpperCAmelCase__			:							List[Any]							=    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."""    )
            UpperCAmelCase__			:							Union[str, Any]							=    jax.tree_util.tree_map(
                lambda lowerCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    flatten_dict(lowerCAmelCase    )
      UpperCAmelCase__			:							str							=    pt_model.state_dict()

      UpperCAmelCase__			:							Optional[int]							=    (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()}
      )
      UpperCAmelCase__			:							str							=    (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
      UpperCAmelCase__			:							List[str]							=    []
      UpperCAmelCase__			:							Optional[int]							=    set(pt_model_dict.keys()    )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCAmelCase__			:							Dict							=    flax_key_tuple[0] == pt_model.base_model_prefix
            UpperCAmelCase__			:							List[str]							=    """.""".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:
                  UpperCAmelCase__			:							int							=    flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCAmelCase__			:							List[Any]							=    (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
                  UpperCAmelCase__			:							Union[str, Any]							=    flax_key_tuple[:-1] + ("""weight""",)
                  UpperCAmelCase__			:							str							=    jnp.transpose(lowerCAmelCase			,	(3, 2, 0, 1)    )
            elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase    ) not in pt_model_dict:
                  # linear layer
                  UpperCAmelCase__			:							Optional[Any]							=    flax_key_tuple[:-1] + ("""weight""",)
                  UpperCAmelCase__			:							Union[str, Any]							=    flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  UpperCAmelCase__			:							Any							=    flax_key_tuple[:-1] + ("""weight""",)

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

            if "batch_stats" in flax_state:
                  UpperCAmelCase__			:							int							=    """.""".join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
            else:
                  UpperCAmelCase__			:							Optional[Any]							=    """.""".join(lowerCAmelCase    )

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

            if flax_key in special_pt_names:
                  UpperCAmelCase__			:							Optional[Any]							=    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
                        UpperCAmelCase__			:							Dict							=    np.asarray(lowerCAmelCase    ) if not isinstance(lowerCAmelCase			,	np.ndarray    ) else flax_tensor
                        UpperCAmelCase__			:							Optional[int]							=    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
      UpperCAmelCase__			:							Optional[Any]							=    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

