
'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _a				(_lowerCamelCase):





							"""simple docstring"""




							SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
							SCREAMING_SNAKE_CASE      =  'CLIPImageProcessor'
							SCREAMING_SNAKE_CASE      =  ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')






							def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Tuple:
										_SCREAMING_SNAKE_CASE      =				None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,							A__       ,							)
													_SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

										_SCREAMING_SNAKE_CASE      =				image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""      )
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""      )

										super().__init__(A__       ,							A__      )






							def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			List[str]:

										if text is None and images is None:
													raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

										if text is not None:
													_SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

										if images is not None:
													_SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

										if text is not None and images is not None:
													_SCREAMING_SNAKE_CASE      =				image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






							def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[int]:
										return self.tokenizer.batch_decode(*A__       ,							**A__      )






							def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
										return self.tokenizer.decode(*A__       ,							**A__      )






							@property
							def        UpperCamelCase				(							self      )    ->			Dict:
										_SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
										_SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )








'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
     import jax.numpy as jnp

if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch

class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params""": {
                  """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
                  """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
                  """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
              }
          }
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params.summarization.length_penalty""": 1.0,
              """task_specific_params.summarization.max_length""": 1_28,
              """task_specific_params.summarization.min_length""": 12,
              """task_specific_params.summarization.num_beams""": 4,
              """task_specific_params.summarization_cnn.length_penalty""": 2.0,
              """task_specific_params.summarization_cnn.max_length""": 1_42,
              """task_specific_params.summarization_cnn.min_length""": 56,
              """task_specific_params.summarization_cnn.num_beams""": 4,
              """task_specific_params.summarization_xsum.length_penalty""": 1.0,
              """task_specific_params.summarization_xsum.max_length""": 62,
              """task_specific_params.summarization_xsum.min_length""": 11,
              """task_specific_params.summarization_xsum.num_beams""": 6,
          }

          self.assertEqual(flatten_dict(A__      )       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							x.transpose()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							x.transpose((1, 2, 0)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							np.asarray(transpose(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							np.asarray(transpose(A__       ,							axes=(1, 2, 0)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.reshape(A__       ,							(4, 3)      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.reshape(A__       ,							(12, 5)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.asarray(reshape(A__       ,							(4, 3)      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.asarray(reshape(A__       ,							(12, 5)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.squeeze(A__      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.squeeze(A__       ,							axis=2      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.asarray(squeeze(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.asarray(squeeze(A__       ,							axis=2      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.expand_dims(A__       ,							axis=1      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.asarray(expand_dims(A__       ,							axis=1      )      )      )      )






