





"""simple docstring"""


import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__      =      getLogger(__name__)


lowerCamelCase__      =      "cuda" if torch.cuda.is_available() else "cpu"




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_ = 8						,lowercase_ = DEFAULT_DEVICE						,lowercase_=False						,lowercase_="summarization"						,lowercase_=None						,**lowercase_						,)    ->						Dict:




      """simple docstring"""

      _UpperCamelCase					:		Tuple  =				Path(lowercase_	).open("w"						,encoding="utf-8"	)
      _UpperCamelCase					:		str  =				str(lowercase_	)
      _UpperCamelCase					:		str  =				AutoModelForSeqaSeqLM.from_pretrained(lowercase_	).to(lowercase_	)
      if fpaa:
            _UpperCamelCase					:		int  =				model.half()

      _UpperCamelCase					:		Dict  =				AutoTokenizer.from_pretrained(lowercase_	)
      logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

      _UpperCamelCase					:		str  =				time.time()
      # update config with task specific params
      use_task_specific_params(lowercase_						,lowercase_	)
      if prefix is None:
            _UpperCamelCase					:		int  =				prefix or getattr(model.config						,"prefix"						,""	) or ""
      for examples_chunk in tqdm(list(chunks(lowercase_						,lowercase_	)	)	):
            _UpperCamelCase					:		Dict  =				[prefix + text for text in examples_chunk]
            _UpperCamelCase					:		Optional[int]  =				tokenizer(lowercase_						,return_tensors="pt"						,truncation=lowercase_						,padding="longest"	).to(lowercase_	)
            _UpperCamelCase					:		Optional[Any]  =				model.generate(
                input_ids=batch.input_ids						,attention_mask=batch.attention_mask						,**lowercase_						,)
            _UpperCamelCase					:		Optional[Any]  =				tokenizer.batch_decode(lowercase_						,skip_special_tokens=lowercase_						,clean_up_tokenization_spaces=lowercase_	)
            for hypothesis in dec:
                  fout.write(hypothesis + "\n"	)
                  fout.flush()
      fout.close()
      _UpperCamelCase					:		Optional[int]  =				int(time.time() - start_time	)  # seconds
      _UpperCamelCase					:		List[Any]  =				len(lowercase_	)
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,4	)}




def     lowercase__			(    )    ->						Union[str, Any]:




      """simple docstring"""

      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"	)




def     lowercase__			(    lowercase_=True	)    ->						List[Any]:




      """simple docstring"""

      _UpperCamelCase					:		int  =				argparse.ArgumentParser()
      parser.add_argument("model_name"						,type=lowercase_						,help="like facebook/bart-large-cnn,t5-base, etc."	)
      parser.add_argument("input_path"						,type=lowercase_						,help="like cnn_dm/test.source"	)
      parser.add_argument("save_path"						,type=lowercase_						,help="where to save summaries"	)
      parser.add_argument("--reference_path"						,type=lowercase_						,required=lowercase_						,help="like cnn_dm/test.target"	)
      parser.add_argument("--score_path"						,type=lowercase_						,required=lowercase_						,default="metrics.json"						,help="where to save metrics"	)
      parser.add_argument("--device"						,type=lowercase_						,required=lowercase_						,default=lowercase_						,help="cuda, cuda:1, cpu etc."	)
      parser.add_argument(
          "--prefix"						,type=lowercase_						,required=lowercase_						,default=lowercase_						,help="will be added to the begininng of src examples"	)
      parser.add_argument("--task"						,type=lowercase_						,default="summarization"						,help="used for task_specific_params + metrics"	)
      parser.add_argument("--bs"						,type=lowercase_						,default=8						,required=lowercase_						,help="batch size"	)
      parser.add_argument(
          "--n_obs"						,type=lowercase_						,default=-1						,required=lowercase_						,help="How many observations. Defaults to all."	)
      parser.add_argument("--fp16"						,action="store_true"	)
      parser.add_argument("--dump-args"						,action="store_true"						,help="print the custom hparams with the results"	)
      parser.add_argument(
          "--info"						,nargs="?"						,type=lowercase_						,const=datetime_now()						,help=(
              "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
              " lang=en-ru. If no value is passed, the current datetime string will be used."
          )						,)
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				parser.parse_known_args()
      _UpperCamelCase					:		Tuple  =				parse_numeric_n_bool_cl_kwargs(lowercase_	)
      if parsed_args and verbose:
            print(F'''parsed the following generate kwargs: {parsed_args}'''	)
      with open(args.input_path	) as f:
            _UpperCamelCase					:		str  =				[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
            _UpperCamelCase					:		List[str]  =				examples[: args.n_obs]
      Path(args.save_path	).parent.mkdir(exist_ok=lowercase_	)
      if args.reference_path is None and Path(args.score_path	).exists():
            warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''	)
      _UpperCamelCase					:		List[Any]  =				generate_summaries_or_translations(
          lowercase_						,args.save_path						,args.model_name						,batch_size=args.bs						,device=args.device						,fpaa=args.fpaa						,task=args.task						,prefix=args.prefix						,**lowercase_						,)

      if args.reference_path is None:
            return {}

      # Compute scores
      _UpperCamelCase					:		Union[str, Any]  =				calculate_bleu if "translation" in args.task else calculate_rouge
      _UpperCamelCase					:		Optional[Any]  =				[x.rstrip() for x in open(args.save_path	).readlines()]
      _UpperCamelCase					:		Optional[Any]  =				[x.rstrip() for x in open(args.reference_path	).readlines()][: len(lowercase_	)]
      _UpperCamelCase					:		dict  =				score_fn(lowercase_						,lowercase_	)
      scores.update(lowercase_	)

      if args.dump_args:
            scores.update(lowercase_	)
      if args.info:
            _UpperCamelCase					:		int  =				args.info

      if verbose:
            print(lowercase_	)

      if args.score_path is not None:
            json.dump(lowercase_						,open(args.score_path						,"w"	)	)

      return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)







"""simple docstring"""


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


lowerCamelCase__      =      logging.get_logger(__name__)



class    __SCREAMING_SNAKE_CASE			(  nn.Module      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:HybridCLIPConfig
  SCREAMING_SNAKE_CASE__							:jnp.dtype		   =    jnp.floataa

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Tuple:
        _UpperCamelCase					:		List[str]  =				self.config.text_config
        _UpperCamelCase					:		Union[str, Any]  =				self.config.vision_config

        _UpperCamelCase					:		Optional[int]  =				self.config.projection_dim
        _UpperCamelCase					:		List[Any]  =				text_config.hidden_size
        _UpperCamelCase					:		Optional[Any]  =				vision_config.hidden_size

        _UpperCamelCase					:		str  =				FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
        _UpperCamelCase					:		int  =				FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,      __a		).module_class

        _UpperCamelCase					:		str  =				text_module(__a    ,      dtype=self.dtype		)
        _UpperCamelCase					:		List[Any]  =				vision_module(__a    ,      dtype=self.dtype		)

        _UpperCamelCase					:		List[str]  =				nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02		)    ,      use_bias=__a    ,      )
        _UpperCamelCase					:		int  =				nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02		)    ,      use_bias=__a    ,      )
        _UpperCamelCase					:		int  =				self.param("logit_scale"    ,      jax.nn.initializers.ones    ,      []		)


  def __call__(						self      :				Any    ,      __a      :				List[str]=None    ,      __a      :				Optional[int]=None    ,      __a      :				Tuple=None    ,      __a      :				Tuple=None    ,      __a      :				str=None    ,      __a      :				bool = True    ,      __a      :				Dict=None    ,      __a      :				Any=None    ,      __a      :				Any=None    ,      )						-> List[str]:
        _UpperCamelCase					:		List[str]  =				return_dict if return_dict is not None else self.config.return_dict

        _UpperCamelCase					:		Any  =				self.vision_model(
            pixel_values=__a    ,      deterministic=__a    ,      output_attentions=__a    ,      output_hidden_states=__a    ,      return_dict=__a    ,      )

        _UpperCamelCase					:		Any  =				self.text_model(
            input_ids=__a    ,      attention_mask=__a    ,      token_type_ids=__a    ,      position_ids=__a    ,      deterministic=__a    ,      output_attentions=__a    ,      output_hidden_states=__a    ,      return_dict=__a    ,      )

        _UpperCamelCase					:		str  =				vision_outputs[1]
        _UpperCamelCase					:		Dict  =				self.visual_projection(__a		)

        _UpperCamelCase					:		Dict  =				text_outputs[1]
        _UpperCamelCase					:		Dict  =				self.text_projection(__a		)

        # normalized features
        _UpperCamelCase					:		Optional[int]  =				image_embeds / jnp.linalg.norm(__a    ,      axis=-1    ,      keepdims=__a		)
        _UpperCamelCase					:		Tuple  =				text_embeds / jnp.linalg.norm(__a    ,      axis=-1    ,      keepdims=__a		)

        # cosine similarity as logits
        _UpperCamelCase					:		Dict  =				jnp.exp(self.logit_scale		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.matmul(__a    ,      image_embeds.T		) * logit_scale
        _UpperCamelCase					:		List[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=__a    ,      logits_per_text=__a    ,      text_embeds=__a    ,      image_embeds=__a    ,      text_model_output=__a    ,      vision_model_output=__a    ,      )





class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    HybridCLIPConfig
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    FlaxHybridCLIPModule

  def __init__(						self      :				Tuple    ,      __a      :				HybridCLIPConfig    ,      __a      :				Optional[Tuple] = None    ,      __a      :				int = 0    ,      __a      :				jnp.dtype = jnp.floataa    ,      **__a      :				Tuple    ,      )						-> List[str]:
        if input_shape is None:
              _UpperCamelCase					:		str  =				((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

        _UpperCamelCase					:		List[Any]  =				self.module_class(config=__a    ,      dtype=__a    ,      **__a		)
        super().__init__(__a    ,      __a    ,      input_shape=__a    ,      seed=__a    ,      dtype=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				jax.random.PRNGKey    ,      __a      :				Tuple    ,      __a      :				FrozenDict = None		)						-> FrozenDict:
        # init input tensor
        _UpperCamelCase					:		List[str]  =				jnp.zeros(input_shape[0]    ,      dtype="i4"		)
        _UpperCamelCase					:		List[Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_shape[0]		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.ones_like(__a		)
        _UpperCamelCase					:		str  =				jnp.ones_like(__a		)

        _UpperCamelCase					:		Optional[int]  =				jax.random.normal(__a    ,      input_shape[1]		)

        _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				jax.random.split(__a		)
        _UpperCamelCase					:		Tuple  =				{"params": params_rng, "dropout": dropout_rng}

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

  def __call__(						self      :				Dict    ,      __a      :				int    ,      __a      :				Any    ,      __a      :				Union[str, Any]=None    ,      __a      :				Optional[int]=None    ,      __a      :				Union[str, Any]=None    ,      __a      :				dict = None    ,      __a      :				jax.random.PRNGKey = None    ,      __a      :				bool = False    ,      __a      :				Optional[bool] = None    ,      __a      :				Optional[bool] = None    ,      __a      :				Optional[bool] = None    ,      )						-> Optional[Any]:
        _UpperCamelCase					:		List[Any]  =				output_attentions if output_attentions is not None else self.config.output_attentions
        _UpperCamelCase					:		Tuple  =				(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _UpperCamelCase					:		List[str]  =				return_dict if return_dict is not None else self.config.return_dict

        if position_ids is None:
              _UpperCamelCase					:		Union[str, Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_ids.shape		)

        if token_type_ids is None:
              _UpperCamelCase					:		str  =				jnp.zeros_like(__a		)

        if attention_mask is None:
              _UpperCamelCase					:		Optional[int]  =				jnp.ones_like(__a		)

        # Handle any PRNG if needed
        _UpperCamelCase					:		Optional[int]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		List[Any]  =				dropout_rng

        return self.module.apply(
            {"params": params or self.params}    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype=jnp.floataa		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      not train    ,      __a    ,      __a    ,      __a    ,      rngs=__a    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Any    ,      __a      :				List[str]=None    ,      __a      :				Union[str, Any]=None    ,      __a      :				Any=None    ,      __a      :				dict = None    ,      __a      :				jax.random.PRNGKey = None    ,      __a      :				List[str]=False    ,      )						-> str:
        if position_ids is None:
              _UpperCamelCase					:		Union[str, Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_ids.shape		)

        if token_type_ids is None:
              _UpperCamelCase					:		Tuple  =				jnp.zeros_like(__a		)

        if attention_mask is None:
              _UpperCamelCase					:		List[str]  =				jnp.ones_like(__a		)

        # Handle any PRNG if needed
        _UpperCamelCase					:		List[str]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		Dict  =				dropout_rng

        def _get_features(__a      :				Optional[int]    ,      __a      :				Optional[Any]    ,      __a      :				List[Any]    ,      __a      :				Optional[int]    ,      __a      :				Union[str, Any]    ,      __a      :				List[Any]		):
              _UpperCamelCase					:		List[Any]  =				module.text_model(
                  input_ids=__a    ,      attention_mask=__a    ,      position_ids=__a    ,      token_type_ids=__a    ,      deterministic=__a    ,      )
              _UpperCamelCase					:		Tuple  =				text_outputs[1]
              _UpperCamelCase					:		Tuple  =				module.text_projection(__a		)
              return text_features

        return self.module.apply(
            {"params": params or self.params}    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      jnp.array(__a    ,      dtype="i4"		)    ,      not train    ,      method=_get_features    ,      rngs=__a    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				int    ,      __a      :				dict = None    ,      __a      :				jax.random.PRNGKey = None    ,      __a      :				Optional[Any]=False		)						-> Tuple:
        _UpperCamelCase					:		List[str]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		Tuple  =				dropout_rng

        def _get_features(__a      :				int    ,      __a      :				List[str]    ,      __a      :				Optional[Any]		):
              _UpperCamelCase					:		Optional[int]  =				module.vision_model(pixel_values=__a    ,      deterministic=__a		)
              _UpperCamelCase					:		Optional[int]  =				vision_outputs[1]  # pooled_output
              _UpperCamelCase					:		str  =				module.visual_projection(__a		)
              return image_features

        return self.module.apply(
            {"params": params or self.params}    ,      jnp.array(__a    ,      dtype=jnp.floataa		)    ,      not train    ,      method=_get_features    ,      rngs=__a    ,      )


  @classmethod
  def       __SCREAMING_SNAKE_CASE (						cls      :				List[str]    ,      __a      :				str = None    ,      __a      :				str = None    ,      *__a      :				List[Any]    ,      **__a      :				Tuple    ,      )						-> FlaxPreTrainedModel:
        _UpperCamelCase					:		Union[str, Any]  =				{
            argument[len("text_"		) :]: value for argument, value in kwargs.items() if argument.startswith("text_"		)
        }

        _UpperCamelCase					:		Optional[Any]  =				{
            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					:		int  =				kwargs_text.pop("model"    ,      __a		)
        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					:		Any  =				AutoConfig.from_pretrained(__a		)
                    _UpperCamelCase					:		int  =				text_config

              _UpperCamelCase					:		Optional[Any]  =				FlaxAutoModel.from_pretrained(__a    ,      *__a    ,      **__a		)

        _UpperCamelCase					:		str  =				kwargs_vision.pop("model"    ,      __a		)
        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					:		Dict  =				AutoConfig.from_pretrained(__a		)
                    _UpperCamelCase					:		str  =				vision_config

              _UpperCamelCase					:		int  =				FlaxAutoModel.from_pretrained(__a    ,      *__a    ,      **__a		)

        # instantiate config with corresponding kwargs
        _UpperCamelCase					:		Optional[int]  =				kwargs.pop("dtype"    ,      jnp.floataa		)
        _UpperCamelCase					:		Optional[int]  =				HybridCLIPConfig.from_text_vision_configs(text_model.config    ,      vision_model.config    ,      **__a		)

        # init model
        _UpperCamelCase					:		List[str]  =				cls(__a    ,      *__a    ,      dtype=__a    ,      **__a		)

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

        _UpperCamelCase					:		str  =				text_model.params

        return model
