



"""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


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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		str=False							)    ->  str:





     '''simple docstring'''

     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     :      int														=       os.path.abspath(SCREAMING_SNAKE_CASE__							)
          logger.info(f'Loading PyTorch weights from {pt_path}'							)

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple[str]		,    SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		Dict[str, jnp.ndarray]		,    SCREAMING_SNAKE_CASE__      :		str		,    )    ->  (Tuple[str], np.ndarray):





     '''simple docstring'''

     def is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__      :		Tuple[str]							) -> bool:
          return len(set(SCREAMING_SNAKE_CASE__							) & {key, (model_prefix,) + key}							) > 0

     # layer norm
     _UpperCAmelCase     :      List[str]														=       pt_tuple_key[:-1] + ("scale",)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__							):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer mean
     _UpperCAmelCase     :      List[Any]														=       pt_tuple_key[:-1] + ("mean",)
     if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__							):
          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(SCREAMING_SNAKE_CASE__							):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     _UpperCAmelCase     :      Optional[int]														=       pt_tuple_key[:-1] + ("embedding",)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__							):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     _UpperCAmelCase     :      Dict														=       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(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      int														=       pt_tensor.transpose(2		,    3		,    1		,    0							)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     _UpperCAmelCase     :      Any														=       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      Dict														=       pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     _UpperCAmelCase     :      Union[str, Any]														=       pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

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

     return pt_tuple_key, pt_tensor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       {k: v.numpy() for k, v in pt_state_dict.items()}

     _UpperCAmelCase     :      Optional[int]														=       flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          _UpperCAmelCase     :      List[str]														=       flax_model.params["params"]
     else:
          _UpperCAmelCase     :      Tuple														=       flax_model.params
     _UpperCAmelCase     :      List[str]														=       flatten_dict(SCREAMING_SNAKE_CASE__							)

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          _UpperCAmelCase     :      Union[str, Any]														=       flatten_dict(flax_model.params["batch_stats"]							)
          random_flax_state_dict.update(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      List[str]														=       {}

     _UpperCAmelCase     :      Any														=       (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     :      Dict														=       tuple(pt_key.split("."							)							)

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

          # Correctly rename weight parameters
          _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       rename_key_and_reshape_tensor(
              SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

          # add model prefix if necessary
          _UpperCAmelCase     :      Optional[int]														=       (model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               _UpperCAmelCase     :      Optional[Any]														=       (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     :      List[str]														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
                    continue

               # also add unexpected weight so that warning is thrown
               _UpperCAmelCase     :      Optional[Any]														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)

          else:
               # also add unexpected weight so that warning is thrown
               _UpperCAmelCase     :      Optional[Any]														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)

     return unflatten_dict(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  List[str]:





     '''simple docstring'''

     import torch

     # Load the index
     _UpperCAmelCase     :      Optional[Any]														=       {}
     for shard_file in shard_filenames:
          # load using msgpack utils
          _UpperCAmelCase     :      Dict														=       torch.load(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      List[str]														=       {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     :      Optional[int]														=       flax_model.params["params"]

               _UpperCAmelCase     :      str														=       flatten_dict(SCREAMING_SNAKE_CASE__							)
               random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]							)							)
          else:
               _UpperCAmelCase     :      Optional[Any]														=       flax_model.params
               _UpperCAmelCase     :      str														=       flatten_dict(SCREAMING_SNAKE_CASE__							)

          _UpperCAmelCase     :      Optional[int]														=       (model_prefix not in flax_model_params) and (
              model_prefix in {k.split("."							)[0] for k in pt_state_dict.keys()}
          )
          _UpperCAmelCase     :      Dict														=       (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     :      Dict														=       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     :      List[Any]														=       pt_tuple_key[1:]

               # Correctly rename weight parameters
               _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       rename_key_and_reshape_tensor(
                   SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
               # add model prefix if necessary
               _UpperCAmelCase     :      Optional[Any]														=       (model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    _UpperCAmelCase     :      List[Any]														=       (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     :      int														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)
                         continue
                    if "var" in flax_key[-1]:
                         _UpperCAmelCase     :      Dict														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
                         continue

                    # also add unexpected weight so that warning is thrown
                    _UpperCAmelCase     :      int														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)

               else:
                    # also add unexpected weight so that warning is thrown
                    _UpperCAmelCase     :      Union[str, Any]														=       jnp.asarray(SCREAMING_SNAKE_CASE__							)
     return unflatten_dict(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       os.path.abspath(SCREAMING_SNAKE_CASE__							)
     logger.info(f'Loading Flax weights from {flax_checkpoint_path}'							)

     # import correct flax class
     _UpperCAmelCase     :      Tuple														=       getattr(SCREAMING_SNAKE_CASE__		,    "Flax" + model.__class__.__name__							)

     # load flax weight dict
     with open(SCREAMING_SNAKE_CASE__		,    "rb"							) as state_f:
          try:
               _UpperCAmelCase     :      List[Any]														=       from_bytes(SCREAMING_SNAKE_CASE__		,    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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  List[str]:





     '''simple docstring'''

     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[str]														=       flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__							: x.dtype == jnp.bfloataa		,    SCREAMING_SNAKE_CASE__							)							).values()
     if any(SCREAMING_SNAKE_CASE__							):
          # 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     :      Optional[Any]														=       jax.tree_util.tree_map(
              lambda SCREAMING_SNAKE_CASE__							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       flatten_dict(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Optional[int]														=       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[Any]														=       set(pt_model_dict.keys()							)

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

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

          if "batch_stats" in flax_state:
               _UpperCAmelCase     :      Optional[int]														=       ".".join(flax_key_tuple[1:]							)  # Remove the params/batch_stats header
          else:
               _UpperCAmelCase     :      Union[str, Any]														=       ".".join(SCREAMING_SNAKE_CASE__							)

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

          if flax_key in special_pt_names:
               _UpperCAmelCase     :      List[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     :      List[str]														=       np.asarray(SCREAMING_SNAKE_CASE__							) if not isinstance(SCREAMING_SNAKE_CASE__		,    np.ndarray							) else flax_tensor
                    _UpperCAmelCase     :      Optional[Any]														=       torch.from_numpy(SCREAMING_SNAKE_CASE__							)
                    # remove from missing keys
                    missing_keys.remove(SCREAMING_SNAKE_CASE__							)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(SCREAMING_SNAKE_CASE__							)

     pt_model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     # re-transform missing_keys to list
     _UpperCAmelCase     :      List[Any]														=       list(SCREAMING_SNAKE_CASE__							)

     if len(SCREAMING_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)."							)
     else:
          logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'							)
     if len(SCREAMING_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."							)
     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 os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase     :      List[str]														=       ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase     :      Tuple														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Any														=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase     :      Optional[Any]														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)
           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

           _UpperCAmelCase     :      str														=       [Image.fromarray(np.moveaxis(A	,      0	,      -1		)		) for x in image_inputs]

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       CLIPSegProcessor.from_pretrained(self.tmpdirname		)

           self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()		)
           self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()		)
           self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()		)
           self.assertIsInstance(processor_slow.tokenizer	,      A		)
           self.assertIsInstance(processor_fast.tokenizer	,      A		)

           self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()		)
           self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()		)
           self.assertIsInstance(processor_slow.image_processor	,      A		)
           self.assertIsInstance(processor_fast.image_processor	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.from_pretrained(
               self.tmpdirname	,      bos_token="(BOS)"	,      eos_token="(EOS)"	,      do_normalize=A	,      padding_value=1.0		)

           self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()		)
           self.assertIsInstance(processor.tokenizer	,      A		)

           self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()		)
           self.assertIsInstance(processor.image_processor	,      A		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=A	,      return_tensors="np"		)

           for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1e-2		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["input_ids", "attention_mask", "pixel_values"]		)

           # test if it raises when no input is passed
           with pytest.raises(A		):
                processor()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["pixel_values", "conditional_pixel_values"]		)

           # test if it raises when no input is passed
           with pytest.raises(A		):
                processor()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
