



"""simple docstring"""




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[dict, list, tuple, torch.Tensor]    )  ->       List[Tuple[int, ...]]:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   []
     if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
          for v in tree.values():
               shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE__    )    )
     elif isinstance(SCREAMING_SNAKE_CASE__						,  (list, tuple)    ):
          for t in tree:
               shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE__    )    )
     elif isinstance(SCREAMING_SNAKE_CASE__						,  torch.Tensor    ):
          shapes.append(tree.shape    )
     else:
          raise ValueError("Not supported"    )

     return shapes

@torch.jit.ignore
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Tuple[int, ...]    )  ->       Tuple[int, ...]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   []
     for d in reversed(SCREAMING_SNAKE_CASE__    ):
          idx.append(flat_idx % d    )
          _UpperCAmelCase      :     Optional[Any]        =   flat_idx // d

     return tuple(reversed(SCREAMING_SNAKE_CASE__    )    )

@torch.jit.ignore
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Sequence[int]						,  SCREAMING_SNAKE_CASE__     :						Sequence[int]						,  SCREAMING_SNAKE_CASE__     :						Sequence[int]						,  SCREAMING_SNAKE_CASE__     :						Optional[Sequence[bool]] = None						,  SCREAMING_SNAKE_CASE__     :						Optional[Sequence[bool]] = None						,  )  ->       List[Tuple[slice, ...]]:





     '''simple docstring'''

     def reduce_edge_list(SCREAMING_SNAKE_CASE__     :						List[bool]    ) -> None:
          _UpperCAmelCase      :     List[Any]        =   True
          for i in range(len(SCREAMING_SNAKE_CASE__    )    ):
               _UpperCAmelCase      :     List[str]        =   -1 * (i + 1)
               l[reversed_idx] &= tally
               _UpperCAmelCase      :     Tuple        =   l[reversed_idx]

     if start_edges is None:
          _UpperCAmelCase      :     Union[str, Any]        =   [s == 0 for s in start]
          reduce_edge_list(SCREAMING_SNAKE_CASE__    )
     if end_edges is None:
          _UpperCAmelCase      :     Tuple        =   [e == (d - 1) for e, d in zip(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )]
          reduce_edge_list(SCREAMING_SNAKE_CASE__    )

     # Base cases. Either start/end are empty and we're done, or the final,
     # one-dimensional tensor can be simply sliced
     if len(SCREAMING_SNAKE_CASE__    ) == 0:
          return [()]
     elif len(SCREAMING_SNAKE_CASE__    ) == 1:
          return [(slice(start[0]						,  end[0] + 1    ),)]

     _UpperCAmelCase      :     int        =   []
     _UpperCAmelCase      :     Optional[Any]        =   []

     # Dimensions common to start and end can be selected directly
     for s, e in zip(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
          if s == e:
               path_list.append(slice(SCREAMING_SNAKE_CASE__						,  s + 1    )    )
          else:
               break

     _UpperCAmelCase      :     Dict        =   tuple(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   len(SCREAMING_SNAKE_CASE__    )

     # start == end, and we're done
     if divergence_idx == len(SCREAMING_SNAKE_CASE__    ):
          return [path]

     def upper() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          _UpperCAmelCase      :     List[Any]        =   start[divergence_idx]
          return tuple(
              path + (slice(SCREAMING_SNAKE_CASE__						,  sdi + 1    ),) + s
              for s in _get_minimal_slice_set(
                  start[divergence_idx + 1 :]						,  [d - 1 for d in dims[divergence_idx + 1 :]]						,  dims[divergence_idx + 1 :]						,  start_edges=start_edges[divergence_idx + 1 :]						,  end_edges=[True for _ in end_edges[divergence_idx + 1 :]]						,  )    )

     def lower() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          _UpperCAmelCase      :     Dict        =   end[divergence_idx]
          return tuple(
              path + (slice(SCREAMING_SNAKE_CASE__						,  edi + 1    ),) + s
              for s in _get_minimal_slice_set(
                  [0 for _ in start[divergence_idx + 1 :]]						,  end[divergence_idx + 1 :]						,  dims[divergence_idx + 1 :]						,  start_edges=[True for _ in start_edges[divergence_idx + 1 :]]						,  end_edges=end_edges[divergence_idx + 1 :]						,  )    )

     # If both start and end are at the edges of the subtree rooted at
     # divergence_idx, we can just select the whole subtree at once
     if start_edges[divergence_idx] and end_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]						,  end[divergence_idx] + 1    ),)    )
     # If just start is at the edge, we can grab almost all of the subtree,
     # treating only the ragged bottom edge as an edge case
     elif start_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]						,  end[divergence_idx]    ),)    )
          slices.extend(lower()    )
     # Analogous to the previous case, but the top is ragged this time
     elif end_edges[divergence_idx]:
          slices.extend(upper()    )
          slices.append(path + (slice(start[divergence_idx] + 1						,  end[divergence_idx] + 1    ),)    )
     # If both sides of the range are ragged, we need to handle both sides
     # separately. If there's contiguous meat in between them, we can index it
     # in one big chunk
     else:
          slices.extend(upper()    )
          _UpperCAmelCase      :     Any        =   end[divergence_idx] - start[divergence_idx]
          if middle_ground > 1:
               slices.append(path + (slice(start[divergence_idx] + 1						,  end[divergence_idx]    ),)    )
          slices.extend(lower()    )

     return slices

@torch.jit.ignore
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						torch.Tensor						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       torch.Tensor:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   t.shape[:no_batch_dims]
     _UpperCAmelCase      :     Tuple        =   list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )    )
     # _get_minimal_slice_set is inclusive
     _UpperCAmelCase      :     Optional[Any]        =   list(_flat_idx_to_idx(flat_end - 1						,  SCREAMING_SNAKE_CASE__    )    )

     # Get an ordered list of slices to perform
     _UpperCAmelCase      :     List[Any]        =   _get_minimal_slice_set(
         SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  )

     _UpperCAmelCase      :     str        =   [t[s] for s in slices]

     return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]    ) for s in sliced_tensors]    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Callable						,  SCREAMING_SNAKE_CASE__     :						Dict[str, Any]						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						bool = False						,  SCREAMING_SNAKE_CASE__     :						Any = None						,  SCREAMING_SNAKE_CASE__     :						bool = False						,  )  ->       Any:





     '''simple docstring'''

     if not (len(SCREAMING_SNAKE_CASE__    ) > 0):
          raise ValueError("Must provide at least one input"    )

     _UpperCAmelCase      :     List[Any]        =   [shape[:no_batch_dims] for shape in _fetch_dims(SCREAMING_SNAKE_CASE__    )]
     _UpperCAmelCase      :     str        =   tuple([max(SCREAMING_SNAKE_CASE__    ) for s in zip(*SCREAMING_SNAKE_CASE__    )]    )

     def _prep_inputs(SCREAMING_SNAKE_CASE__     :						torch.Tensor    ) -> torch.Tensor:
          if not low_mem:
               if not sum(t.shape[:no_batch_dims]    ) == no_batch_dims:
                    _UpperCAmelCase      :     int        =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
               _UpperCAmelCase      :     List[str]        =   t.reshape(-1						,  *t.shape[no_batch_dims:]    )
          else:
               _UpperCAmelCase      :     Union[str, Any]        =   t.expand(orig_batch_dims + t.shape[no_batch_dims:]    )
          return t

     _UpperCAmelCase      :     Optional[Any]        =   tensor_tree_map(_prep_inputs						,  SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Tuple        =   None
     if _out is not None:
          _UpperCAmelCase      :     List[Any]        =   tensor_tree_map(lambda SCREAMING_SNAKE_CASE__    : t.view([-1] + list(t.shape[no_batch_dims:]    )    )						,  _out    )

     _UpperCAmelCase      :     Optional[Any]        =   1
     for d in orig_batch_dims:
          flat_batch_dim *= d

     _UpperCAmelCase      :     Any        =   flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

     def _select_chunk(SCREAMING_SNAKE_CASE__     :						torch.Tensor    ) -> torch.Tensor:
          return t[i : i + chunk_size] if t.shape[0] != 1 else t

     _UpperCAmelCase      :     List[str]        =   0
     _UpperCAmelCase      :     Optional[int]        =   prepped_outputs
     for _ in range(SCREAMING_SNAKE_CASE__    ):
          # Chunk the input
          if not low_mem:
               _UpperCAmelCase      :     Union[str, Any]        =   _select_chunk
          else:
               _UpperCAmelCase      :     List[Any]        =   partial(
                   _chunk_slice						,  flat_start=SCREAMING_SNAKE_CASE__						,  flat_end=min(SCREAMING_SNAKE_CASE__						,  i + chunk_size    )						,  no_batch_dims=len(SCREAMING_SNAKE_CASE__    )						,  )

          _UpperCAmelCase      :     str        =   tensor_tree_map(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

          # Run the layer on the chunk
          _UpperCAmelCase      :     Dict        =   layer(**SCREAMING_SNAKE_CASE__    )

          # Allocate space for the output
          if out is None:
               _UpperCAmelCase      :     Optional[Any]        =   tensor_tree_map(lambda SCREAMING_SNAKE_CASE__    : t.new_zeros((flat_batch_dim,) + t.shape[1:]    )						,  SCREAMING_SNAKE_CASE__    )

          # Put the chunk in its pre-allocated space
          if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):

               def assign(SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    ) -> None:
                    for k, v in da.items():
                         if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
                              assign(SCREAMING_SNAKE_CASE__						,  da[k]    )
                         else:
                              if _add_into_out:
                                   v[i : i + chunk_size] += da[k]
                              else:
                                   _UpperCAmelCase      :     Union[str, Any]        =   da[k]

               assign(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          elif isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
               for xa, xa in zip(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
                    if _add_into_out:
                         xa[i : i + chunk_size] += xa
                    else:
                         _UpperCAmelCase      :     List[Any]        =   xa
          elif isinstance(SCREAMING_SNAKE_CASE__						,  torch.Tensor    ):
               if _add_into_out:
                    out[i : i + chunk_size] += output_chunk
               else:
                    _UpperCAmelCase      :     Union[str, Any]        =   output_chunk
          else:
               raise ValueError("Not supported"    )

          i += chunk_size

     _UpperCAmelCase      :     List[str]        =   tensor_tree_map(lambda SCREAMING_SNAKE_CASE__    : t.view(orig_batch_dims + t.shape[1:]    )						,  SCREAMING_SNAKE_CASE__    )

     return out






class      UpperCAmelCase_  :



      def __init__(					self					:     Optional[Any]					,     A					:     int = 5_1_2					,     ):
           _UpperCAmelCase      :     List[str]        =   max_chunk_size
           _UpperCAmelCase      :     Optional[int]        =   None
           _UpperCAmelCase      :     Optional[Any]        =   None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Callable					,     A					:     tuple					,     A					:     int						):
           logging.info("Tuning chunk size..."						)

           if min_chunk_size >= self.max_chunk_size:
                return min_chunk_size

           _UpperCAmelCase      :     Optional[Any]        =   [2**l for l in range(int(math.log(self.max_chunk_size					,     2						)						) + 1						)]
           _UpperCAmelCase      :     Any        =   [c for c in candidates if c > min_chunk_size]
           _UpperCAmelCase      :     Dict        =   [min_chunk_size] + candidates
           candidates[-1] += 4

           def test_chunk_size(A					:     int						) -> bool:
                try:
                     with torch.no_grad():
                          fn(*__UpperCAmelCase					,     chunk_size=__UpperCAmelCase						)
                     return True
                except RuntimeError:
                     return False

           _UpperCAmelCase      :     Union[str, Any]        =   0
           _UpperCAmelCase      :     List[Any]        =   len(__UpperCAmelCase						) - 1
           while i > min_viable_chunk_size_index:
                _UpperCAmelCase      :     List[Any]        =   test_chunk_size(candidates[i]						)
                if not viable:
                     _UpperCAmelCase      :     Union[str, Any]        =   (min_viable_chunk_size_index + i) // 2
                else:
                     _UpperCAmelCase      :     str        =   i
                     _UpperCAmelCase      :     Dict        =   (i + len(__UpperCAmelCase						) - 1) // 2

           return candidates[min_viable_chunk_size_index]



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Iterable					,     A					:     Iterable						):
           _UpperCAmelCase      :     int        =   True
           for aa, aa in zip(__UpperCAmelCase					,     __UpperCAmelCase						):
                assert type(__UpperCAmelCase						) == type(__UpperCAmelCase						)
                if isinstance(__UpperCAmelCase					,     (list, tuple)						):
                     consistent &= self._compare_arg_caches(__UpperCAmelCase					,     __UpperCAmelCase						)
                elif isinstance(__UpperCAmelCase					,     __UpperCAmelCase						):
                     _UpperCAmelCase      :     Union[str, Any]        =   [v for _, v in sorted(aa.items()					,     key=lambda A						: x[0]						)]
                     _UpperCAmelCase      :     Optional[Any]        =   [v for _, v in sorted(aa.items()					,     key=lambda A						: x[0]						)]
                     consistent &= self._compare_arg_caches(__UpperCAmelCase					,     __UpperCAmelCase						)
                else:
                     consistent &= aa == aa

           return consistent



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     Callable					,     A					:     tuple					,     A					:     int					,     ):
           _UpperCAmelCase      :     Union[str, Any]        =   True
           _UpperCAmelCase      :     Optional[int]        =   tree_map(lambda A						: a.shape if isinstance(__UpperCAmelCase					,     torch.Tensor						) else a					,     __UpperCAmelCase					,     __UpperCAmelCase						)
           if self.cached_arg_data is not None:
                # If args have changed shape/value, we need to re-tune
                assert len(self.cached_arg_data						) == len(__UpperCAmelCase						)
                _UpperCAmelCase      :     Tuple        =   self._compare_arg_caches(self.cached_arg_data					,     __UpperCAmelCase						)
           else:
                # Otherwise, we can reuse the precomputed value
                _UpperCAmelCase      :     List[Any]        =   False

           if not consistent:
                _UpperCAmelCase      :     Optional[int]        =   self._determine_favorable_chunk_size(
                    __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase					,     )
                _UpperCAmelCase      :     Dict        =   arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size








"""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 CLIPImageProcessor, CLIPProcessor






@require_vision
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase      :     Union[str, Any]        =   ["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      :     List[Any]        =   dict(zip(A					,     range(len(A						)						)						)						)
           _UpperCAmelCase      :     Union[str, Any]        =   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase      :     Optional[int]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     Optional[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Optional[Any]        =   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[str]        =   {
               "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      :     Any        =   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					:     List[Any]					,     **A					:     Union[str, Any]						):
           return CLIPTokenizer.from_pretrained(self.tmpdirname					,     **A						)



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



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



      def 						snake_case_ (					self					:     Optional[int]						):
           shutil.rmtree(self.tmpdirname						)



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

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

           return image_inputs



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

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_slow.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor.from_pretrained(self.tmpdirname					,     use_fast=A						)

           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_fast.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[int]        =   CLIPProcessor.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					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=self.get_tokenizer()					,     image_processor=self.get_image_processor()						)
           processor.save_pretrained(self.tmpdirname						)

           _UpperCAmelCase      :     Any        =   self.get_tokenizer(bos_token="(BOS)"					,     eos_token="(EOS)"						)
           _UpperCAmelCase      :     Any        =   self.get_image_processor(do_normalize=A					,     padding_value=1.0						)

           _UpperCAmelCase      :     Any        =   CLIPProcessor.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					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.get_image_processor()
           _UpperCAmelCase      :     List[str]        =   self.get_tokenizer()

           _UpperCAmelCase      :     Any        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Dict        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[int]        =   image_processor(A					,     return_tensors="np"						)
           _UpperCAmelCase      :     Any        =   processor(images=A					,     return_tensors="np"						)

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



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Optional[int]        =   "lower newer"

           _UpperCAmelCase      :     Union[str, Any]        =   processor(text=A						)

           _UpperCAmelCase      :     Optional[int]        =   tokenizer(A						)

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



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

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

           _UpperCAmelCase      :     Tuple        =   "lower newer"
           _UpperCAmelCase      :     Union[str, 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					:     int						):
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

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

           _UpperCAmelCase      :     List[str]        =   processor.batch_decode(A						)
           _UpperCAmelCase      :     int        =   tokenizer.batch_decode(A						)

           self.assertListEqual(A					,     A						)



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

           _UpperCAmelCase      :     int        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   "lower newer"
           _UpperCAmelCase      :     int        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[Any]        =   processor(text=A					,     images=A						)

           self.assertListEqual(list(inputs.keys()						)					,     processor.model_input_names						)
