

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roformer.modeling_flax_roformer import (
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
    )


class        a__  (							unittest.TestCase  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase=13      ,    _UpperCamelCase=7      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=99      ,    _UpperCamelCase=32      ,    _UpperCamelCase=5      ,    _UpperCamelCase=4      ,    _UpperCamelCase=37      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=16      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=4      ,    ):





      """simple docstring"""





      _lowercase      :				int      					=       parent
      _lowercase      :				Optional[int]      					=       batch_size
      _lowercase      :				Dict      					=       seq_length
      _lowercase      :				int      					=       is_training
      _lowercase      :				List[Any]      					=       use_attention_mask
      _lowercase      :				Any      					=       use_token_type_ids
      _lowercase      :				Tuple      					=       use_labels
      _lowercase      :				Optional[int]      					=       vocab_size
      _lowercase      :				Dict      					=       hidden_size
      _lowercase      :				List[str]      					=       num_hidden_layers
      _lowercase      :				List[str]      					=       num_attention_heads
      _lowercase      :				List[str]      					=       intermediate_size
      _lowercase      :				Tuple      					=       hidden_act
      _lowercase      :				List[str]      					=       hidden_dropout_prob
      _lowercase      :				Tuple      					=       attention_probs_dropout_prob
      _lowercase      :				Optional[int]      					=       max_position_embeddings
      _lowercase      :				Tuple      					=       type_vocab_size
      _lowercase      :				Union[str, Any]      					=       type_sequence_label_size
      _lowercase      :				Union[str, Any]      					=       initializer_range
      _lowercase      :				Optional[int]      					=       num_choices


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size  )

      _lowercase      :				Union[str, Any]      					=       None
      if self.use_attention_mask:
         _lowercase      :				int      					=       random_attention_mask([self.batch_size, self.seq_length]  )

      _lowercase      :				str      					=       None
      if self.use_token_type_ids:
         _lowercase      :				Dict      					=       ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size  )

      _lowercase      :				List[Any]      					=       RoFormerConfig(
          vocab_size=self.vocab_size      ,    hidden_size=self.hidden_size      ,    num_hidden_layers=self.num_hidden_layers      ,    num_attention_heads=self.num_attention_heads      ,    intermediate_size=self.intermediate_size      ,    hidden_act=self.hidden_act      ,    hidden_dropout_prob=self.hidden_dropout_prob      ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,    max_position_embeddings=self.max_position_embeddings      ,    type_vocab_size=self.type_vocab_size      ,    is_decoder=_UpperCamelCase      ,    initializer_range=self.initializer_range      ,    )

      return config, input_ids, token_type_ids, attention_mask






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.prepare_config_and_inputs()
      _lowercase      :				Union[str, Any]      					=       config_and_inputs
      _lowercase      :				str      					=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
      return config, inputs_dict




@require_flax
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							True

   _SCREAMING_SNAKE_CASE   :					List[str]     	=							(
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       FlaxRoFormerModelTester(self  )






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for model_class_name in self.all_model_classes:
         _lowercase      :				str      					=       model_class_name.from_pretrained("junnyu/roformer_chinese_small"      ,    from_pt=_UpperCamelCase  )
         _lowercase      :				Tuple      					=       model(np.ones((1, 1)  )  )
         self.assertIsNotNone(_UpperCamelCase  )




@require_flax
class        a__  (							unittest.TestCase  ):


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"  )
      _lowercase      :				Union[str, Any]      					=       jnp.array([[0, 1, 2, 3, 4, 5]]  )
      _lowercase      :				int      					=       model(_UpperCamelCase  )[0]

      _lowercase      :				Dict      					=       50000

      _lowercase      :				Any      					=       (1, 6, vocab_size)
      self.assertEqual(output.shape      ,    _UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       jnp.array(
          [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]  )

      self.assertTrue(jnp.allclose(output[:, :3, :3]      ,    _UpperCamelCase      ,    atol=1E-4  )  )









'''simple docstring'''
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'OwlViTImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				Optional[int]      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				str      					=       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__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="max_length"      ,    _UpperCamelCase="np"      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and query_images is None and images is None:
         raise ValueError(
             "You have to specify at least one text or query image or image. All three cannot be none."  )

      if text is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not isinstance(text[0]      ,    _UpperCamelCase  )):
            _lowercase      :				int      					=       [self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )]

         elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and isinstance(text[0]      ,    _UpperCamelCase  ):
            _lowercase      :				str      					=       []

            # Maximum number of queries across batch
            _lowercase      :				str      					=       max([len(_UpperCamelCase  ) for t in text]  )

            # Pad all batch samples to max number of text queries
            for t in text:
               if len(_UpperCamelCase  ) != max_num_queries:
                  _lowercase      :				List[Any]      					=       t + [" "] * (max_num_queries - len(_UpperCamelCase  ))

               _lowercase      :				Tuple      					=       self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )
               encodings.append(_UpperCamelCase  )
         else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

         if return_tensors == "np":
            _lowercase      :				List[Any]      					=       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				Optional[Any]      					=       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "jax" and is_flax_available():
            import jax.numpy as jnp

            _lowercase      :				Union[str, Any]      					=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				int      					=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "pt" and is_torch_available():
            import torch

            _lowercase      :				int      					=       torch.cat([encoding["input_ids"] for encoding in encodings]      ,    dim=0  )
            _lowercase      :				Dict      					=       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,    dim=0  )

         elif return_tensors == "tf" and is_tf_available():
            import tensorflow as tf

            _lowercase      :				Optional[int]      					=       tf.stack([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				List[str]      					=       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         else:
            raise ValueError("Target return tensor type could not be returned"  )

         _lowercase      :				Optional[int]      					=       BatchEncoding()
         _lowercase      :				List[Any]      					=       input_ids
         _lowercase      :				Dict      					=       attention_mask

      if query_images is not None:
         _lowercase      :				int      					=       BatchEncoding()
         _lowercase      :				Any      					=       self.image_processor(
             _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  ).pixel_values
         _lowercase      :				Any      					=       query_pixel_values

      if images is not None:
         _lowercase      :				str      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				List[Any]      					=       image_features.pixel_values
         return encoding
      elif query_images is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None or query_images is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor






