'''simple docstring'''

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


A_     :						Optional[int]    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Tuple)			->					List[Any]:
   try:
      with open(__snake_case	,   '''rb''') as flax_state_f:
         lowerCAmelCase_			=      from_bytes(__snake_case	,   flax_state_f.read())
   except UnpicklingError as e:
      try:
         with open(__snake_case) 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(__snake_case	,   __snake_case)






def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Dict)			->					Optional[Any]:

   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
   lowerCAmelCase_			=      flatten_dict(jax.tree_util.tree_map(lambda __snake_case: x.dtype == jnp.bfloataa	,   __snake_case)).values()
   if any(__snake_case):
      # 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.''')
      lowerCAmelCase_			=      jax.tree_util.tree_map(
          lambda __snake_case: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params	,   __snake_case)

   lowerCAmelCase_			=      ''''''

   lowerCAmelCase_			=      flatten_dict(__snake_case	,   sep='''.''')
   lowerCAmelCase_			=      pt_model.state_dict()

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

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      lowerCAmelCase_			=      flax_key_tuple.split('''.''')

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         lowerCAmelCase_			=      flax_key_tuple_array[:-1] + ['''weight''']
         lowerCAmelCase_			=      jnp.transpose(__snake_case	,   (3, 2, 0, 1))
      elif flax_key_tuple_array[-1] == "kernel":
         lowerCAmelCase_			=      flax_key_tuple_array[:-1] + ['''weight''']
         lowerCAmelCase_			=      flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         lowerCAmelCase_			=      flax_key_tuple_array[:-1] + ['''weight''']

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(__snake_case):
            lowerCAmelCase_			=      (
                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''')
            )

      lowerCAmelCase_			=      '''.'''.join(__snake_case)

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

   pt_model.load_state_dict(__snake_case)

   # re-transform missing_keys to list
   lowerCAmelCase_			=      list(__snake_case)

   if len(__snake_case) > 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(__snake_case) > 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






'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['''bs''']    ,     model_result['''ss'''] ):
           lowerCAmelCase_			=      model_result['''result'''][batch_size][sequence_length]
           self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sgugger/tiny-distilbert-classification'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     only_pretrain_model=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     torchscript=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Cant do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     fpaa=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     # set architectures equal to `None`
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Can\'t do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     fpaa=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     save_to_csv=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     inference_time_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' )    ,     train_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' )    ,     inference_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' )    ,     train_time_csv_file=os.path.join(_lowerCamelCase    ,     '''train_time.csv''' )    ,     env_info_csv_file=os.path.join(_lowerCamelCase    ,     '''env.csv''' )    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''env.csv''' ) ).exists() )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase ):
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''sequential''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''cumulative''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''current''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''total''' ) )

     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     log_filename=os.path.join(_lowerCamelCase    ,     '''log.txt''' )    ,     log_print=_lowerCamelCase    ,     trace_memory_line_by_line=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        lowerCAmelCase_			=      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        _check_summary_is_not_empty(result.train_summary )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''log.txt''' ) ).exists() )





