




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


lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Union[str, Any]				)   ->					Union[str, Any]:
      try:
            with open(__UpperCAmelCase     ,     'rb'				) as flax_state_f:
                  SCREAMING_SNAKE_CASE_      		=     from_bytes(__UpperCAmelCase     ,     flax_state_f.read()				)
      except UnpicklingError as e:
            try:
                  with open(__UpperCAmelCase				) 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(__UpperCAmelCase     ,     __UpperCAmelCase				)





def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]				)   ->					Dict:

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

      SCREAMING_SNAKE_CASE_      		=     ''

      SCREAMING_SNAKE_CASE_      		=     flatten_dict(__UpperCAmelCase     ,     sep='.'				)
      SCREAMING_SNAKE_CASE_      		=     pt_model.state_dict()

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

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

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

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

            SCREAMING_SNAKE_CASE_      		=     '.'.join(__UpperCAmelCase				)

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

      pt_model.load_state_dict(__UpperCAmelCase				)

      # re-transform missing_keys to list
      SCREAMING_SNAKE_CASE_      		=     list(__UpperCAmelCase				)

      if len(__UpperCAmelCase				) > 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(__UpperCAmelCase				) > 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





def    UpperCAmelCase_				( )   ->					list[list[int]]:
      return [list(range(10_00 - i     ,     -10_00 - i     ,     -1				)				) for i in range(10_00				)]


lowerCamelCase__					:  List[Any]      							=				generate_large_matrix()
lowerCamelCase__					:  List[Any]      							=				(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					None:
      assert all(row == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for row in grid				)
      assert all(list(__UpperCAmelCase				) == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for col in zip(*__UpperCAmelCase				)				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[int]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(__UpperCAmelCase				) - 1

      # Edge cases such as no values or all numbers are negative.
      if not array or array[0] < 0:
            return 0

      while right + 1 > left:
            SCREAMING_SNAKE_CASE_      		=     (left + right) // 2
            SCREAMING_SNAKE_CASE_      		=     array[mid]

            # Num must be negative and the index must be greater than or equal to 0.
            if num < 0 and array[mid - 1] >= 0:
                  return mid

            if num >= 0:
                  SCREAMING_SNAKE_CASE_      		=     mid + 1
            else:
                  SCREAMING_SNAKE_CASE_      		=     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
      return len(__UpperCAmelCase				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(grid[0]				)

      for i in range(len(__UpperCAmelCase				)				):
            SCREAMING_SNAKE_CASE_      		=     find_negative_index(grid[i][:bound]				)
            total += bound
      return (len(__UpperCAmelCase				) * len(grid[0]				)) - total

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      return len([number for row in grid for number in row if number < 0]				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      for row in grid:
            for i, number in enumerate(__UpperCAmelCase				):
                  if number < 0:
                        total += len(__UpperCAmelCase				) - i
                        break
      return total

def    UpperCAmelCase_				( )   ->					None:
      from timeit import timeit

      print('Running benchmarks'				)
      SCREAMING_SNAKE_CASE_      		=     (
          'from __main__ import count_negatives_binary_search, '
          'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
      )
      for func in (
          "count_negatives_binary_search",  # took 0.7727 seconds
          "count_negatives_brute_force_with_break",  # took 4.6505 seconds
          "count_negatives_brute_force",  # took 12.8160 seconds
      ):
            SCREAMING_SNAKE_CASE_      		=     timeit(f"{func}(grid=grid)"     ,     setup=__UpperCAmelCase     ,     number=5_00				)
            print(f"{func}() took {time:0.4f} seconds"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()