




import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCamelCase							:						Union[str, Any]					       =  logging.get_logger(__name__)

lowerCamelCase							:						int					       =  OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

lowerCamelCase							:						Optional[int]					       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			str	):


      '''simple docstring'''



      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  lowerCamelCase_							=       model_type_to_module_name(lowercase	)

                  lowerCamelCase_							=       importlib.import_module(f""".{module_name}"""      ,				'transformers.models'	)
                  try:
                        return getattr(lowercase      ,				lowercase	)
                  except AttributeError:
                        continue

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(lowercase      ,				'__name__'      ,				lowercase	) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      lowerCamelCase_							=       importlib.import_module('transformers'	)
      if hasattr(lowercase      ,				lowercase	):
            return getattr(lowercase      ,				lowercase	)

      return None

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, os.PathLike]      ,				lowercase      :			Optional[Union[str, os.PathLike]] = None      ,				lowercase      :			bool = False      ,				lowercase      :			bool = False      ,				lowercase      :			Optional[Dict[str, str]] = None      ,				lowercase      :			Optional[Union[bool, str]] = None      ,				lowercase      :			Optional[str] = None      ,				lowercase      :			bool = False      ,				**lowercase      :			str      ,				):


      '''simple docstring'''



      lowerCamelCase_							=       get_file_from_repo(
          lowercase      ,				lowercase      ,				cache_dir=lowercase      ,				force_download=lowercase      ,				resume_download=lowercase      ,				proxies=lowercase      ,				use_auth_token=lowercase      ,				revision=lowercase      ,				local_files_only=lowercase      ,				)
      if resolved_config_file is None:
            logger.info(
                'Could not locate the image processor configuration file, will try to use the model config instead.'	)
            return {}

      with open(lowercase      ,				encoding='utf-8'	) as reader:
            return json.load(lowercase	)







class     A:



 '''simple docstring'''


 def __init__(							self   :    Optional[Any]    )  ->      Dict:

       """simple docstring"""






       raise EnvironmentError(
           'AutoImageProcessor is designed to be instantiated '
           'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'    )

 @classmethod
 @replace_list_option_in_docstrings(A_    )
 def 							a__					(							cls   :    List[Any]		,				A_   :    str		,				**A_   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       kwargs.pop('config'		,				A_    )
       lowerCamelCase_							=       kwargs.pop('trust_remote_code'		,				A_    )
       lowerCamelCase_							=       True

       lowerCamelCase_	,							lowerCamelCase_							=       ImageProcessingMixin.get_image_processor_dict(A_		,				**A_    )
       lowerCamelCase_							=       config_dict.get('image_processor_type'		,				A_    )
       lowerCamelCase_							=       None
       if "AutoImageProcessor" in config_dict.get('auto_map'		,				{}    ):
             lowerCamelCase_							=       config_dict['auto_map']['AutoImageProcessor']

       # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
       # and if so, infer the image processor class from there.
       if image_processor_class is None and image_processor_auto_map is None:
             lowerCamelCase_							=       config_dict.pop('feature_extractor_type'		,				A_    )
             if feature_extractor_class is not None:
                   logger.warning(
                       'Could not find image processor class in the image processor config or the model config. Loading'
                       ' based on pattern matching with the model\'s feature extractor configuration.'    )
                   lowerCamelCase_							=       feature_extractor_class.replace('FeatureExtractor'		,				'ImageProcessor'    )
             if "AutoFeatureExtractor" in config_dict.get('auto_map'		,				{}    ):
                   lowerCamelCase_							=       config_dict['auto_map']['AutoFeatureExtractor']
                   lowerCamelCase_							=       feature_extractor_auto_map.replace('FeatureExtractor'		,				'ImageProcessor'    )
                   logger.warning(
                       'Could not find image processor auto map in the image processor config or the model config.'
                       ' Loading based on pattern matching with the model\'s feature extractor configuration.'    )

        # If we don't find the image processor class in the image processor config, let's try the model config.
       if image_processor_class is None and image_processor_auto_map is None:
             if not isinstance(A_		,				A_    ):
                   lowerCamelCase_							=       AutoConfig.from_pretrained(A_		,				**A_    )
             # It could be in `config.image_processor_type``
             lowerCamelCase_							=       getattr(A_		,				'image_processor_type'		,				A_    )
             if hasattr(A_		,				'auto_map'    ) and "AutoImageProcessor" in config.auto_map:
                   lowerCamelCase_							=       config.auto_map['AutoImageProcessor']

       if image_processor_class is not None:
             lowerCamelCase_							=       image_processor_class_from_name(A_    )

       lowerCamelCase_							=       image_processor_auto_map is not None
       lowerCamelCase_							=       image_processor_class is not None or type(A_    ) in IMAGE_PROCESSOR_MAPPING
       lowerCamelCase_							=       resolve_trust_remote_code(
           A_		,				A_		,				A_		,				A_    )

       if has_remote_code and trust_remote_code:
             lowerCamelCase_							=       get_class_from_dynamic_module(
                 A_		,				A_		,				**A_    )
             lowerCamelCase_							=       kwargs.pop('code_revision'		,				A_    )
             if os.path.isdir(A_    ):
                   image_processor_class.register_for_auto_class()
             return image_processor_class.from_dict(A_		,				**A_    )
       elif image_processor_class is not None:
             return image_processor_class.from_dict(A_		,				**A_    )
       # Last try: we use the IMAGE_PROCESSOR_MAPPING.
       elif type(A_    ) in IMAGE_PROCESSOR_MAPPING:
             lowerCamelCase_							=       IMAGE_PROCESSOR_MAPPING[type(A_    )]
             return image_processor_class.from_dict(A_		,				**A_    )

       raise ValueError(
           f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
           f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
           f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()    )}"""    )





 @staticmethod
 def 							a__					(							A_   :    Any		,				A_   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       IMAGE_PROCESSOR_MAPPING.register(A_		,				A_    )








"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     snake_case   					=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				List[Any]:
  if attention_mask is None:
    _snake_case   	=      np.where(input_ids != config.pad_token_id  ,						1  ,						0	)
  if decoder_attention_mask is None:
    _snake_case   	=      np.where(decoder_input_ids != config.pad_token_id  ,						1  ,						0	)
  if head_mask is None:
    _snake_case   	=      np.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]=1_3							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	Optional[int]=9_9							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Tuple=2							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	Optional[int]=4							,       __lowerCamelCase    :	Tuple="gelu"							,       __lowerCamelCase    :	int=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	Optional[Any]=1							,       __lowerCamelCase    :	Dict=0							,       __lowerCamelCase    :	List[str]=0.0_2							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      eos_token_id
     _snake_case   	=      pad_token_id
     _snake_case   	=      bos_token_id
     _snake_case   	=      initializer_range



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size				)							,       3							,       self.vocab_size				)
     _snake_case   	=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,       dtype=np.intaa				))							,       -1				)

     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)

     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=self.hidden_size							,       encoder_layers=self.num_hidden_layers							,       decoder_layers=self.num_hidden_layers							,       encoder_attention_heads=self.num_attention_heads							,       decoder_attention_heads=self.num_attention_heads							,       encoder_ffn_dim=self.intermediate_size							,       decoder_ffn_dim=self.intermediate_size							,       dropout=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       eos_token_id=self.eos_token_id							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       initializer_range=self.initializer_range							,       use_cache=__lowerCamelCase							,       )
     _snake_case   	=      prepare_blenderbot_inputs_dict(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.prepare_config_and_inputs()
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,       dtype='''i4'''				)

     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )
     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
         ]							,       axis=-1							,       )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )

     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )
     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_attention_mask=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)



@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  99



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]							,       dtype=np.intaa							,       )

     _snake_case   	=      input_ids.shape[0]
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=2_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=3_2							,       decoder_ffn_dim=3_2							,       max_position_embeddings=4_8							,       eos_token_id=2							,       pad_token_id=1							,       bos_token_id=0							,       )
     return config, input_ids, batch_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      self._get_config_and_data()
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase				)
     _snake_case   	=      (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=1_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=8							,       decoder_ffn_dim=8							,       max_position_embeddings=4_8							,       )
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase							,       decoder_input_ids=__lowerCamelCase				)
     _snake_case   	=      (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,       dtype=np.intaa				)
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape							,       input_ids.shape				)
     self.assertEqual(__lowerCamelCase							,       n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]							,       2				).all()				)



@require_flax
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase,__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  True
   A__		:	int           =  (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__		:	List[Any]           =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      FlaxBlenderbotModelTester(self				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)
         _snake_case   	=      model_class(__lowerCamelCase				)

         @jax.jit
         def encode_jitted(__lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       **__lowerCamelCase    :	Union[str, Any]				):
           return model.encode(input_ids=__lowerCamelCase							,       attention_mask=__lowerCamelCase				)

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      model_class(__lowerCamelCase				)
         _snake_case   	=      model.encode(inputs_dict['''input_ids''']							,       inputs_dict['''attention_mask''']				)

         _snake_case   	=      {
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(__lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):
           return model.decode(
               decoder_input_ids=__lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       encoder_outputs=__lowerCamelCase							,       )

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_class_name in self.all_model_classes:
       _snake_case   	=      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _snake_case   	=      np.ones((1, 1)				) * model.config.eos_token_id
       _snake_case   	=      model(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)





   @unittest.skipUnless(jax_device != '''cpu'''							,       '''3B test too slow on CPU.'''				)
   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
     _snake_case   	=      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

     _snake_case   	=      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''							,       from_pt=__lowerCamelCase				)
     _snake_case   	=      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

     _snake_case   	=      ['''Sam''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       return_tensors='''jax'''				)

     _snake_case   	=      model.generate(**__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      '''Sam is a great name. It means "sun" in Gaelic.'''

     _snake_case   	=      tokenizer.batch_decode(__lowerCamelCase							,       **__lowerCamelCase				)
     assert generated_txt[0].strip() == tgt_text





