code: from typing import Optional, Tuple
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from configuration_hybrid_clip import HybridCLIPConfig
  from flax.core.frozen_dict import FrozenDict
  
  from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
  from transformers.modeling_flax_utils import FlaxPreTrainedModel
  from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
  from transformers.utils import logging
  
  
  __A   :     Optional[int]      =			logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A				(							nn.Module ):
      lowerCAmelCase_							:		Tuple						=							42
      lowerCAmelCase_							:		Optional[int]						=							jnp.floataa
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			int    			=      self.config.text_config
          lowerCAmelCase				:			Optional[Any]    			=      self.config.vision_config
  
          lowerCAmelCase				:			int    			=      self.config.projection_dim
          lowerCAmelCase				:			Dict    			=      text_config.hidden_size
          lowerCAmelCase				:			Union[str, Any]    			=      vision_config.hidden_size
  
          lowerCAmelCase				:			Tuple    			=      FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
          lowerCAmelCase				:			Optional[int]    			=      FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,				__UpperCamelCase    ).module_class
  
          lowerCAmelCase				:			List[str]    			=      text_module(__UpperCamelCase    ,				dtype=self.dtype    )
          lowerCAmelCase				:			Any    			=      vision_module(__UpperCamelCase    ,				dtype=self.dtype    )
  
          lowerCAmelCase				:			Optional[int]    			=      nn.Dense(
              self.projection_dim    ,				dtype=self.dtype    ,				kernel_init=jax.nn.initializers.normal(0.02    )    ,				use_bias=__UpperCamelCase    ,				)
          lowerCAmelCase				:			str    			=      nn.Dense(
              self.projection_dim    ,				dtype=self.dtype    ,				kernel_init=jax.nn.initializers.normal(0.02    )    ,				use_bias=__UpperCamelCase    ,				)
          lowerCAmelCase				:			List[str]    			=      self.param('logit_scale'    ,				jax.nn.initializers.ones    ,				[]    )
  
  
  
  
  
  
      def __call__(	self			:    Dict    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    Any=None    ,				UpperCAmelCase_			:    Any=None    ,				UpperCAmelCase_			:    List[Any] = True    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    Any=None    ,				UpperCAmelCase_			:    Union[str, Any]=None    ,				):
          lowerCAmelCase				:			Optional[int]    			=      return_dict if return_dict is not None else self.config.return_dict
  
          lowerCAmelCase				:			Tuple    			=      self.vision_model(
              pixel_values=__UpperCamelCase    ,				deterministic=__UpperCamelCase    ,				output_attentions=__UpperCamelCase    ,				output_hidden_states=__UpperCamelCase    ,				return_dict=__UpperCamelCase    ,				)
  
          lowerCAmelCase				:			Any    			=      self.text_model(
              input_ids=__UpperCamelCase    ,				attention_mask=__UpperCamelCase    ,				token_type_ids=__UpperCamelCase    ,				position_ids=__UpperCamelCase    ,				deterministic=__UpperCamelCase    ,				output_attentions=__UpperCamelCase    ,				output_hidden_states=__UpperCamelCase    ,				return_dict=__UpperCamelCase    ,				)
  
          lowerCAmelCase				:			str    			=      vision_outputs[1]
          lowerCAmelCase				:			List[Any]    			=      self.visual_projection(__UpperCamelCase    )
  
          lowerCAmelCase				:			List[str]    			=      text_outputs[1]
          lowerCAmelCase				:			Dict    			=      self.text_projection(__UpperCamelCase    )
  
          # normalized features
          lowerCAmelCase				:			Tuple    			=      image_embeds / jnp.linalg.norm(__UpperCamelCase    ,				axis=-1    ,				keepdims=__UpperCamelCase    )
          lowerCAmelCase				:			Dict    			=      text_embeds / jnp.linalg.norm(__UpperCamelCase    ,				axis=-1    ,				keepdims=__UpperCamelCase    )
  
          # cosine similarity as logits
          lowerCAmelCase				:			Optional[Any]    			=      jnp.exp(self.logit_scale    )
          lowerCAmelCase				:			Any    			=      jnp.matmul(__UpperCamelCase    ,				image_embeds.T    ) * logit_scale
          lowerCAmelCase				:			Union[str, Any]    			=      logits_per_text.T
  
          if not return_dict:
              return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
  
          return FlaxCLIPOutput(
              logits_per_image=__UpperCamelCase    ,				logits_per_text=__UpperCamelCase    ,				text_embeds=__UpperCamelCase    ,				image_embeds=__UpperCamelCase    ,				text_model_output=__UpperCamelCase    ,				vision_model_output=__UpperCamelCase    ,				)
  
  
  
  
  
  
  class    __A				(							UpperCAmelCase_ ):
      lowerCAmelCase_							:		Tuple						=							HybridCLIPConfig
      lowerCAmelCase_							:		Any						=							FlaxHybridCLIPModule
  
  
  
  
  
  
      def __init__(	self			:    Dict    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Optional[Any] = None    ,				UpperCAmelCase_			:    Optional[int] = 0    ,				UpperCAmelCase_			:    Tuple = jnp.floataa    ,				**UpperCAmelCase_			:    Tuple    ,				):
          if input_shape is None:
              lowerCAmelCase				:			int    			=      ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))
  
          lowerCAmelCase				:			Tuple    			=      self.module_class(config=__UpperCamelCase    ,				dtype=__UpperCamelCase    ,				**__UpperCamelCase    )
          super().__init__(__UpperCamelCase    ,				__UpperCamelCase    ,				input_shape=__UpperCamelCase    ,				seed=__UpperCamelCase    ,				dtype=__UpperCamelCase    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    Optional[int] = None    ):
          # init input tensor
          lowerCAmelCase				:			int    			=      jnp.zeros(input_shape[0]    ,				dtype='i4'    )
          lowerCAmelCase				:			int    			=      jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase    ).shape[-1]    )    ,				input_shape[0]    )
          lowerCAmelCase				:			Union[str, Any]    			=      jnp.ones_like(__UpperCamelCase    )
          lowerCAmelCase				:			Union[str, Any]    			=      jnp.ones_like(__UpperCamelCase    )
  
          lowerCAmelCase				:			Dict    			=      jax.random.normal(__UpperCamelCase    ,				input_shape[1]    )
  
          lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      jax.random.split(__UpperCamelCase    )
          lowerCAmelCase				:			List[Any]    			=      {'params': params_rng, 'dropout': dropout_rng}
  
          return self.module.init(__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    )["params"]
  
  
  
  
  
  
      def __call__(	self			:    Dict    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]=None    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    str=None    ,				UpperCAmelCase_			:    Dict = None    ,				UpperCAmelCase_			:    str = None    ,				UpperCAmelCase_			:    List[Any] = False    ,				UpperCAmelCase_			:    List[str] = None    ,				UpperCAmelCase_			:    List[Any] = None    ,				UpperCAmelCase_			:    Dict = None    ,				):
          lowerCAmelCase				:			Tuple    			=      output_attentions if output_attentions is not None else self.config.output_attentions
          lowerCAmelCase				:			int    			=      (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          lowerCAmelCase				:			Tuple    			=      return_dict if return_dict is not None else self.config.return_dict
  
          if position_ids is None:
              lowerCAmelCase				:			List[str]    			=      jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase    ).shape[-1]    )    ,				input_ids.shape    )
  
          if token_type_ids is None:
              lowerCAmelCase				:			Any    			=      jnp.zeros_like(__UpperCamelCase    )
  
          if attention_mask is None:
              lowerCAmelCase				:			Any    			=      jnp.ones_like(__UpperCamelCase    )
  
          # Handle any PRNG if needed
          lowerCAmelCase				:			List[Any]    			=      {}
          if dropout_rng is not None:
              lowerCAmelCase				:			str    			=      dropout_rng
  
          return self.module.apply(
              {'params': params or self.params}    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype=jnp.floataa    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				not train    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase    ,				rngs=__UpperCamelCase    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Tuple=None    ,				UpperCAmelCase_			:    Any=None    ,				UpperCAmelCase_			:    Dict=None    ,				UpperCAmelCase_			:    str = None    ,				UpperCAmelCase_			:    str = None    ,				UpperCAmelCase_			:    Tuple=False    ,				):
          if position_ids is None:
              lowerCAmelCase				:			Any    			=      jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase    ).shape[-1]    )    ,				input_ids.shape    )
  
          if token_type_ids is None:
              lowerCAmelCase				:			Optional[Any]    			=      jnp.zeros_like(__UpperCamelCase    )
  
          if attention_mask is None:
              lowerCAmelCase				:			int    			=      jnp.ones_like(__UpperCamelCase    )
  
          # Handle any PRNG if needed
          lowerCAmelCase				:			Any    			=      {}
          if dropout_rng is not None:
              lowerCAmelCase				:			Optional[Any]    			=      dropout_rng
  
          def _get_features(UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    List[str]    ):
              lowerCAmelCase				:			int    			=      module.text_model(
                  input_ids=__UpperCamelCase    ,				attention_mask=__UpperCamelCase    ,				position_ids=__UpperCamelCase    ,				token_type_ids=__UpperCamelCase    ,				deterministic=__UpperCamelCase    ,				)
              lowerCAmelCase				:			Any    			=      text_outputs[1]
              lowerCAmelCase				:			int    			=      module.text_projection(__UpperCamelCase    )
              return text_features
  
          return self.module.apply(
              {'params': params or self.params}    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				jnp.array(__UpperCamelCase    ,				dtype='i4'    )    ,				not train    ,				method=_get_features    ,				rngs=__UpperCamelCase    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Tuple = None    ,				UpperCAmelCase_			:    Tuple = None    ,				UpperCAmelCase_			:    List[str]=False    ):
          lowerCAmelCase				:			Optional[int]    			=      {}
          if dropout_rng is not None:
              lowerCAmelCase				:			int    			=      dropout_rng
  
          def _get_features(UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    int    ):
              lowerCAmelCase				:			Any    			=      module.vision_model(pixel_values=__UpperCamelCase    ,				deterministic=__UpperCamelCase    )
              lowerCAmelCase				:			List[Any]    			=      vision_outputs[1]  # pooled_output
              lowerCAmelCase				:			Tuple    			=      module.visual_projection(__UpperCamelCase    )
              return image_features
  
          return self.module.apply(
              {'params': params or self.params}    ,				jnp.array(__UpperCamelCase    ,				dtype=jnp.floataa    )    ,				not train    ,				method=_get_features    ,				rngs=__UpperCamelCase    ,				)
  
  
  
  
  
  
      @classmethod
      def 	lowercase__ (	cls			:    Dict    ,				UpperCAmelCase_			:    Dict = None    ,				UpperCAmelCase_			:    Union[str, Any] = None    ,				*UpperCAmelCase_			:    int    ,				**UpperCAmelCase_			:    int    ,				):
          lowerCAmelCase				:			Any    			=      {
              argument[len('text_'    ) :]: value for argument, value in kwargs.items() if argument.startswith('text_'    )
          }
  
          lowerCAmelCase				:			Dict    			=      {
              argument[len('vision_'    ) :]: value for argument, value in kwargs.items() if argument.startswith('vision_'    )
          }
  
          # remove text, vision kwargs from kwargs
          for key in kwargs_text.keys():
              del kwargs["text_" + key]
          for key in kwargs_vision.keys():
              del kwargs["vision_" + key]
  
          # Load and initialize the text and vision model
          lowerCAmelCase				:			Any    			=      kwargs_text.pop('model'    ,				__UpperCamelCase    )
          if text_model is None:
              assert (
                  text_model_name_or_path is not None
              ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
              from transformers import FlaxAutoModel
  
              if "config" not in kwargs_text:
                  from transformers import AutoConfig
  
                  lowerCAmelCase				:			Any    			=      AutoConfig.from_pretrained(__UpperCamelCase    )
                  lowerCAmelCase				:			str    			=      text_config
  
              lowerCAmelCase				:			List[str]    			=      FlaxAutoModel.from_pretrained(__UpperCamelCase    ,				*__UpperCamelCase    ,				**__UpperCamelCase    )
  
          lowerCAmelCase				:			Any    			=      kwargs_vision.pop('model'    ,				__UpperCamelCase    )
          if vision_model is None:
              assert (
                  vision_model_name_or_path is not None
              ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
              from transformers import FlaxAutoModel
  
              if "config" not in kwargs_vision:
                  from transformers import AutoConfig
  
                  lowerCAmelCase				:			Optional[Any]    			=      AutoConfig.from_pretrained(__UpperCamelCase    )
                  lowerCAmelCase				:			Optional[Any]    			=      vision_config
  
              lowerCAmelCase				:			Any    			=      FlaxAutoModel.from_pretrained(__UpperCamelCase    ,				*__UpperCamelCase    ,				**__UpperCamelCase    )
  
          # instantiate config with corresponding kwargs
          lowerCAmelCase				:			Optional[Any]    			=      kwargs.pop('dtype'    ,				jnp.floataa    )
          lowerCAmelCase				:			Union[str, Any]    			=      HybridCLIPConfig.from_text_vision_configs(text_model.config    ,				vision_model.config    ,				**__UpperCamelCase    )
  
          # init model
          lowerCAmelCase				:			Dict    			=      cls(__UpperCamelCase    ,				*__UpperCamelCase    ,				dtype=__UpperCamelCase    ,				**__UpperCamelCase    )
  
          if vision_config.model_type == "clip":
              lowerCAmelCase				:			Any    			=      vision_model.params['vision_model']
              lowerCAmelCase				:			Tuple    			=      vision_model.params['visual_projection']['kernel']
          else:
              lowerCAmelCase				:			Any    			=      vision_model.params
  
          lowerCAmelCase				:			Optional[Any]    			=      text_model.params
  
          return model
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  