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








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))





