
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	  =					logging.get_logger(__name__)

class 		lowercase_       (       nn.Module				):
 A__						:				HybridCLIPConfig
 A__						:				jnp.dtype         =    jnp.floataa

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.config.text_config
        UpperCamelCase_							       =       self.config.vision_config

        UpperCamelCase_							       =       self.config.projection_dim
        UpperCamelCase_							       =       text_config.hidden_size
        UpperCamelCase_							       =       vision_config.hidden_size

        UpperCamelCase_							       =       FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
        UpperCamelCase_							       =       FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__     ,	__UpperCamelCase     ).module_class

        UpperCamelCase_							       =       text_module(__UpperCamelCase     ,	dtype=self.dtype     )
        UpperCamelCase_							       =       vision_module(__UpperCamelCase     ,	dtype=self.dtype     )

        UpperCamelCase_							       =       nn.Dense(
            self.projection_dim     ,	dtype=self.dtype     ,	kernel_init=jax.nn.initializers.normal(0.02     )     ,	use_bias=__UpperCamelCase     ,	)
        UpperCamelCase_							       =       nn.Dense(
            self.projection_dim     ,	dtype=self.dtype     ,	kernel_init=jax.nn.initializers.normal(0.02     )     ,	use_bias=__UpperCamelCase     ,	)
        UpperCamelCase_							       =       self.param("""logit_scale"""     ,	jax.nn.initializers.ones     ,	[]     )

 def __call__(      self     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase = True     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       return_dict if return_dict is not None else self.config.return_dict

        UpperCamelCase_							       =       self.vision_model(
            pixel_values=__UpperCamelCase     ,	deterministic=__UpperCamelCase     ,	output_attentions=__UpperCamelCase     ,	output_hidden_states=__UpperCamelCase     ,	return_dict=__UpperCamelCase     ,	)

        UpperCamelCase_							       =       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     ,	)

        UpperCamelCase_							       =       vision_outputs[1]
        UpperCamelCase_							       =       self.visual_projection(__UpperCamelCase     )

        UpperCamelCase_							       =       text_outputs[1]
        UpperCamelCase_							       =       self.text_projection(__UpperCamelCase     )

        # normalized features
        UpperCamelCase_							       =       image_embeds / jnp.linalg.norm(__UpperCamelCase     ,	axis=-1     ,	keepdims=__UpperCamelCase     )
        UpperCamelCase_							       =       text_embeds / jnp.linalg.norm(__UpperCamelCase     ,	axis=-1     ,	keepdims=__UpperCamelCase     )

        # cosine similarity as logits
        UpperCamelCase_							       =       jnp.exp(self.logit_scale     )
        UpperCamelCase_							       =       jnp.matmul(__UpperCamelCase     ,	image_embeds.T     ) * logit_scale
        UpperCamelCase_							       =       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 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				Optional[Any]         =    HybridCLIPConfig
 A__						:				Optional[Any]         =    FlaxHybridCLIPModule

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = 0     ,	__UpperCamelCase = jnp.floataa     ,	**__UpperCamelCase     ,	):




        """simple docstring"""


        if input_shape is None:
               UpperCamelCase_							       =       ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

        UpperCamelCase_							       =       self.module_class(config=__UpperCamelCase     ,	dtype=__UpperCamelCase     ,	**__UpperCamelCase     )
        super().__init__(__UpperCamelCase     ,	__UpperCamelCase     ,	input_shape=__UpperCamelCase     ,	seed=__UpperCamelCase     ,	dtype=__UpperCamelCase     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ):




        """simple docstring"""


        UpperCamelCase_							       =       jnp.zeros(input_shape[0]     ,	dtype="""i4"""     )
        UpperCamelCase_							       =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase     ).shape[-1]     )     ,	input_shape[0]     )
        UpperCamelCase_							       =       jnp.ones_like(__UpperCamelCase     )
        UpperCamelCase_							       =       jnp.ones_like(__UpperCamelCase     )

        UpperCamelCase_							       =       jax.random.normal(__UpperCamelCase     ,	input_shape[1]     )

        UpperCamelCase_       ,				UpperCamelCase_							       =       jax.random.split(__UpperCamelCase     )
        UpperCamelCase_							       =       {"""params""": params_rng, """dropout""": dropout_rng}

        return self.module.init(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     )["params"]

 def __call__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = False     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       output_attentions if output_attentions is not None else self.config.output_attentions
        UpperCamelCase_							       =       (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        UpperCamelCase_							       =       return_dict if return_dict is not None else self.config.return_dict

        if position_ids is None:
               UpperCamelCase_							       =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase     ).shape[-1]     )     ,	input_ids.shape     )

        if token_type_ids is None:
               UpperCamelCase_							       =       jnp.zeros_like(__UpperCamelCase     )

        if attention_mask is None:
               UpperCamelCase_							       =       jnp.ones_like(__UpperCamelCase     )

        # Handle any PRNG if needed
        UpperCamelCase_							       =       {}
        if dropout_rng is not None:
               UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase=None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase=False     ,	):




        """simple docstring"""


        if position_ids is None:
               UpperCamelCase_							       =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase     ).shape[-1]     )     ,	input_ids.shape     )

        if token_type_ids is None:
               UpperCamelCase_							       =       jnp.zeros_like(__UpperCamelCase     )

        if attention_mask is None:
               UpperCamelCase_							       =       jnp.ones_like(__UpperCamelCase     )

        # Handle any PRNG if needed
        UpperCamelCase_							       =       {}
        if dropout_rng is not None:
               UpperCamelCase_							       =       dropout_rng

        def _get_features(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       module.text_model(
                   input_ids=__UpperCamelCase     ,	attention_mask=__UpperCamelCase     ,	position_ids=__UpperCamelCase     ,	token_type_ids=__UpperCamelCase     ,	deterministic=__UpperCamelCase     ,	)
               UpperCamelCase_							       =       text_outputs[1]
               UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase=False     ):




        """simple docstring"""


        UpperCamelCase_							       =       {}
        if dropout_rng is not None:
               UpperCamelCase_							       =       dropout_rng

        def _get_features(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       module.vision_model(pixel_values=__UpperCamelCase     ,	deterministic=__UpperCamelCase     )
               UpperCamelCase_							       =       vision_outputs[1]  # pooled_output
               UpperCamelCase_							       =       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 lowerCamelCase_				(      cls     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	*__UpperCamelCase     ,	**__UpperCamelCase     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       {
            argument[len("""text_"""     ) :]: value for argument, value in kwargs.items() if argument.startswith("""text_"""     )
        }

        UpperCamelCase_							       =       {
            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
        UpperCamelCase_							       =       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

                      UpperCamelCase_							       =       AutoConfig.from_pretrained(__UpperCamelCase     )
                      UpperCamelCase_							       =       text_config

               UpperCamelCase_							       =       FlaxAutoModel.from_pretrained(__UpperCamelCase     ,	*__UpperCamelCase     ,	**__UpperCamelCase     )

        UpperCamelCase_							       =       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

                      UpperCamelCase_							       =       AutoConfig.from_pretrained(__UpperCamelCase     )
                      UpperCamelCase_							       =       vision_config

               UpperCamelCase_							       =       FlaxAutoModel.from_pretrained(__UpperCamelCase     ,	*__UpperCamelCase     ,	**__UpperCamelCase     )

        # instantiate config with corresponding kwargs
        UpperCamelCase_							       =       kwargs.pop("""dtype"""     ,	jnp.floataa     )
        UpperCamelCase_							       =       HybridCLIPConfig.from_text_vision_configs(text_model.config     ,	vision_model.config     ,	**__UpperCamelCase     )

        # init model
        UpperCamelCase_							       =       cls(__UpperCamelCase     ,	*__UpperCamelCase     ,	dtype=__UpperCamelCase     ,	**__UpperCamelCase     )

        if vision_config.model_type == "clip":
               UpperCamelCase_							       =       vision_model.params["""vision_model"""]
               UpperCamelCase_							       =       vision_model.params["""visual_projection"""]["""kernel"""]
        else:
               UpperCamelCase_							       =       vision_model.params

        UpperCamelCase_							       =       text_model.params

        return model





import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

_A	  =					pathlib.Path.cwd().joinpath('''project_euler''')
_A	  =					pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
      _A	  =					json.load(file_handle)




def 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	ModuleType:
       UpperCamelCase_							       =       importlib.util.spec_from_file_location(file_path.name	,      str(a__     )     )
       UpperCamelCase_							       =       importlib.util.module_from_spec(a__     )  # type: ignore
       spec.loader.exec_module(a__     )  # type: ignore
       return module




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
              if problem_dir_path.is_file() or problem_dir_path.name.startswith("""_"""     ):
                     continue
              for file_path in problem_dir_path.iterdir():
                     if file_path.suffix != ".py" or file_path.name.startswith(("""_""", """test""")     ):
                            continue
                     solution_file_paths.append(a__     )
       return solution_file_paths




def 				lowerCamelCase__				(						)		->	str:
       with open(os.environ["""GITHUB_EVENT_PATH"""]     ) as file:
              UpperCamelCase_							       =       json.load(a__     )
       return event["pull_request"]["url"] + "/files"




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       UpperCamelCase_							       =       {
           """Accept""": """application/vnd.github.v3+json""",
           """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
       }
       UpperCamelCase_							       =       requests.get(get_files_url()	,      headers=a__     ).json()
       for file in files:
              UpperCamelCase_							       =       pathlib.Path.cwd().joinpath(file["""filename"""]     )
              if (
                  filepath.suffix != ".py"
                  or filepath.name.startswith(("""_""", """test""")     )
                  or not filepath.name.startswith("""sol"""     )
              ):
                     continue
              solution_file_paths.append(a__     )
       return solution_file_paths




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       if os.environ.get("""CI"""     ) and os.environ.get("""GITHUB_EVENT_NAME"""     ) == "pull_request":
              # Return only if there are any, otherwise default to all solutions
              if filepaths := added_solution_file_path():
                     return filepaths
       return all_solution_file_paths()







@pytest.mark.parametrize(
    """solution_path"""	,      collect_solution_file_paths()	,      ids=lambda a__     : f'''{path.parent.name}/{path.name}'''	,      )
def 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	None:
       UpperCamelCase_							       =       solution_path.parent.name[8:].zfill(3     )
       UpperCamelCase_							       =       PROBLEM_ANSWERS[problem_number]
       UpperCamelCase_							       =       convert_path_to_module(a__     )
       UpperCamelCase_							       =       str(solution_module.solution()     )  # type: ignore
       UpperCamelCase_							       =       hashlib.shaaaa(answer.encode()     ).hexdigest()
       assert (
           answer == expected
       ), f'''Expected solution to {problem_number} to have hash {expected}, got {answer}'''



