




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def 				SCREAMING_SNAKE_CASE(      __lowercase=None		,							__lowercase=None  ) ->   Optional[Any]:
     return field(default_factory=lambda: default		,							metadata=__lowercase  )






@dataclass
class 	lowerCAmelCase_  :







  '''simple docstring'''

  UpperCamelCase_ :			str        =							field(
      metadata={"""help""": """The csv file to plot."""}					,				)
  UpperCamelCase_ :			bool        =							field(
      default=UpperCAmelCase_					,				metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}					,				)
  UpperCamelCase_ :			bool        =							field(
      default=UpperCAmelCase_					,				metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}					,				)
  UpperCamelCase_ :			bool        =							field(
      default=UpperCAmelCase_					,				metadata={"""help""": """Disable logarithmic scale when plotting"""}					,				)
  UpperCamelCase_ :			bool        =							field(
      default=UpperCAmelCase_					,				metadata={
          """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
      }					,				)
  UpperCamelCase_ :			Optional[str]        =							field(
      default=UpperCAmelCase_					,				metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}					,				)
  UpperCamelCase_ :			Optional[List[str]]        =							list_field(
      default=UpperCAmelCase_					,				metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}       )






def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Dict:
     try:
          int(__lowercase  )
          return True
     except ValueError:
          return False
def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   Optional[Any]:
     try:
          float(__lowercase  )
          return True
     except ValueError:
          return False






class 	lowerCAmelCase_  :







  '''simple docstring'''

  def __init__(   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[str]  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		Optional[Any]        =		args
       A:		int        =		defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

       with open(self.args.csv_file				,     newline=''''''  ) as csv_file:
            A:		List[str]        =		csv.DictReader(SCREAMING_SNAKE_CASE_  )
            for row in reader:
                 A:		Any        =		row['''model''']
                 self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']  )  )
                 self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']  )  )
                 if can_convert_to_int(row['''result''']  ):
                      # value is not None
                      A:		Optional[Any]        =		int(row['''result''']  )
                 elif can_convert_to_float(row['''result''']  ):
                      # value is not None
                      A:		List[str]        =		float(row['''result''']  )




  def 				_snake_case    (   self							:       Any  ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		plt.subplots()
       A:		Optional[int]        =		'''Time usage''' if self.args.is_time else '''Memory usage'''
       A:		Union[str, Any]        =		title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

       if not self.args.no_log_scale:
            # set logarithm scales
            ax.set_xscale('''log'''  )
            ax.set_yscale('''log'''  )

       for axis in [ax.xaxis, ax.yaxis]:
            axis.set_major_formatter(ScalarFormatter()  )

       for model_name_idx, model_name in enumerate(self.result_dict.keys()  ):
            A:		List[Any]        =		sorted(set(self.result_dict[model_name]['''bsz''']  )  )
            A:		List[Any]        =		sorted(set(self.result_dict[model_name]['''seq_len''']  )  )
            A:		int        =		self.result_dict[model_name]['''result''']

            (A):		int        =		(
                (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
            )

            A:		Tuple        =		(
                model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
            )

            for inner_loop_value in inner_loop_array:
                 if self.args.plot_along_batch:
                      A:		Any        =		np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,     dtype=SCREAMING_SNAKE_CASE_				,     )
                 else:
                      A:		Optional[int]        =		np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,     dtype=np.floataa				,     )

                 (A):		Optional[Any]        =		(
                     ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                 )

                 A:		List[Any]        =		np.asarray(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )[: len(SCREAMING_SNAKE_CASE_  )]
                 plt.scatter(
                     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""  )
                 plt.plot(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     '''--'''  )

            title_str += f""" {label_model_name} vs."""

       A:		Dict        =		title_str[:-4]
       A:		Union[str, Any]        =		'''Time in s''' if self.args.is_time else '''Memory in MB'''

       # plot
       plt.title(SCREAMING_SNAKE_CASE_  )
       plt.xlabel(SCREAMING_SNAKE_CASE_  )
       plt.ylabel(SCREAMING_SNAKE_CASE_  )
       plt.legend()

       if self.args.figure_png_file is not None:
            plt.savefig(self.args.figure_png_file  )
       else:
            plt.show()






def 				SCREAMING_SNAKE_CASE(      ) ->   List[Any]:
     A:		Optional[Any]        =		HfArgumentParser(__lowercase  )
     A:		Optional[int]        =		parser.parse_args_into_dataclasses()[0]
     A:		Union[str, Any]        =		Plot(args=__lowercase  )
     plot.plot()


if __name__ == "__main__":
     main()












'''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


UpperCamelCase     =				logging.get_logger(__name__)






class 	lowerCAmelCase_  (					nn.Module       ):







  '''simple docstring'''

  UpperCamelCase_ :			HybridCLIPConfig
  UpperCamelCase_ :			jnp.dtype        =							jnp.floataa



  def 				_snake_case    (   self							:       List[str]  ) ->		int:







       '''simple docstring'''



       A:		List[Any]        =		self.config.text_config
       A:		Any        =		self.config.vision_config

       A:		Union[str, Any]        =		self.config.projection_dim
       A:		List[str]        =		text_config.hidden_size
       A:		List[str]        =		vision_config.hidden_size

       A:		List[Any]        =		FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
       A:		int        =		FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__				,     SCREAMING_SNAKE_CASE_  ).module_class

       A:		str        =		text_module(SCREAMING_SNAKE_CASE_				,     dtype=self.dtype  )
       A:		Union[str, Any]        =		vision_module(SCREAMING_SNAKE_CASE_				,     dtype=self.dtype  )

       A:		Any        =		nn.Dense(
           self.projection_dim				,     dtype=self.dtype				,     kernel_init=jax.nn.initializers.normal(0.02  )				,     use_bias=SCREAMING_SNAKE_CASE_				,     )
       A:		Tuple        =		nn.Dense(
           self.projection_dim				,     dtype=self.dtype				,     kernel_init=jax.nn.initializers.normal(0.02  )				,     use_bias=SCREAMING_SNAKE_CASE_				,     )
       A:		Dict        =		self.param('''logit_scale'''				,     jax.nn.initializers.ones				,     []  )




  def __call__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Dict=None				,     SCREAMING_SNAKE_CASE_							:       List[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Dict=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       bool = True				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		return_dict if return_dict is not None else self.config.return_dict

       A:		List[str]        =		self.vision_model(
           pixel_values=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_				,     output_attentions=SCREAMING_SNAKE_CASE_				,     output_hidden_states=SCREAMING_SNAKE_CASE_				,     return_dict=SCREAMING_SNAKE_CASE_				,     )

       A:		Tuple        =		self.text_model(
           input_ids=SCREAMING_SNAKE_CASE_				,     attention_mask=SCREAMING_SNAKE_CASE_				,     token_type_ids=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_				,     output_attentions=SCREAMING_SNAKE_CASE_				,     output_hidden_states=SCREAMING_SNAKE_CASE_				,     return_dict=SCREAMING_SNAKE_CASE_				,     )

       A:		str        =		vision_outputs[1]
       A:		Any        =		self.visual_projection(SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		text_outputs[1]
       A:		str        =		self.text_projection(SCREAMING_SNAKE_CASE_  )

       # normalized features
       A:		Tuple        =		image_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE_				,     axis=-1				,     keepdims=SCREAMING_SNAKE_CASE_  )
       A:		Tuple        =		text_embeds / jnp.linalg.norm(SCREAMING_SNAKE_CASE_				,     axis=-1				,     keepdims=SCREAMING_SNAKE_CASE_  )

       # cosine similarity as logits
       A:		Optional[int]        =		jnp.exp(self.logit_scale  )
       A:		Union[str, Any]        =		jnp.matmul(SCREAMING_SNAKE_CASE_				,     image_embeds.T  ) * logit_scale
       A:		Tuple        =		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=SCREAMING_SNAKE_CASE_				,     logits_per_text=SCREAMING_SNAKE_CASE_				,     text_embeds=SCREAMING_SNAKE_CASE_				,     image_embeds=SCREAMING_SNAKE_CASE_				,     text_model_output=SCREAMING_SNAKE_CASE_				,     vision_model_output=SCREAMING_SNAKE_CASE_				,     )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  UpperCamelCase_ :			Union[str, Any]        =							HybridCLIPConfig
  UpperCamelCase_ :			List[str]        =							FlaxHybridCLIPModule



  def __init__(   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       HybridCLIPConfig				,     SCREAMING_SNAKE_CASE_							:       Optional[Tuple] = None				,     SCREAMING_SNAKE_CASE_							:       int = 0				,     SCREAMING_SNAKE_CASE_							:       jnp.dtype = jnp.floataa				,     **SCREAMING_SNAKE_CASE_							:       str				,     ) ->		int:







       '''simple docstring'''



       if input_shape is None:
            A:		List[Any]        =		((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

       A:		int        =		self.module_class(config=SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       super().__init__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     input_shape=SCREAMING_SNAKE_CASE_				,     seed=SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       FrozenDict = None  ) ->		FrozenDict:







       '''simple docstring'''



       A:		List[Any]        =		jnp.zeros(input_shape[0]				,     dtype='''i4'''  )
       A:		List[str]        =		jnp.broadcast_to(jnp.arange(jnp.atleast_ad(SCREAMING_SNAKE_CASE_  ).shape[-1]  )				,     input_shape[0]  )
       A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )
       A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       A:		Any        =		jax.random.normal(SCREAMING_SNAKE_CASE_				,     input_shape[1]  )

       A    ,						A:		int        =		jax.random.split(SCREAMING_SNAKE_CASE_  )
       A:		Dict        =		{'''params''': params_rng, '''dropout''': dropout_rng}

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



  def __call__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       int=None				,     SCREAMING_SNAKE_CASE_							:       Any=None				,     SCREAMING_SNAKE_CASE_							:       List[str]=None				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       bool = False				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     SCREAMING_SNAKE_CASE_							:       Optional[bool] = None				,     ) ->		str:







       '''simple docstring'''



       A:		List[str]        =		output_attentions if output_attentions is not None else self.config.output_attentions
       A:		int        =		(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       A:		Dict        =		return_dict if return_dict is not None else self.config.return_dict

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

       if token_type_ids is None:
            A:		Tuple        =		jnp.zeros_like(SCREAMING_SNAKE_CASE_  )

       if attention_mask is None:
            A:		Any        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       # Handle any PRNG if needed
       A:		Union[str, Any]        =		{}
       if dropout_rng is not None:
            A:		Any        =		dropout_rng

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



  def 				_snake_case    (   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       str=None				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=None				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=False				,     ) ->		Optional[Any]:







       '''simple docstring'''



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

       if token_type_ids is None:
            A:		List[str]        =		jnp.zeros_like(SCREAMING_SNAKE_CASE_  )

       if attention_mask is None:
            A:		Union[str, Any]        =		jnp.ones_like(SCREAMING_SNAKE_CASE_  )

       # Handle any PRNG if needed
       A:		Any        =		{}
       if dropout_rng is not None:
            A:		Tuple        =		dropout_rng

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

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



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       dict = None				,     SCREAMING_SNAKE_CASE_							:       jax.random.PRNGKey = None				,     SCREAMING_SNAKE_CASE_							:       List[str]=False  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		Union[str, Any]        =		{}
       if dropout_rng is not None:
            A:		Any        =		dropout_rng

       def _get_features(SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Dict				,     SCREAMING_SNAKE_CASE_							:       Any  ):
            A:		List[str]        =		module.vision_model(pixel_values=SCREAMING_SNAKE_CASE_				,     deterministic=SCREAMING_SNAKE_CASE_  )
            A:		Union[str, Any]        =		vision_outputs[1]  # pooled_output
            A:		Any        =		module.visual_projection(SCREAMING_SNAKE_CASE_  )
            return image_features

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




  @classmethod
  def 				_snake_case    (   cls							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       str = None				,     SCREAMING_SNAKE_CASE_							:       str = None				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       List[str]				,     ) ->		FlaxPreTrainedModel:







       '''simple docstring'''



       A:		List[Any]        =		{
           argument[len('''text_'''  ) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''  )
       }

       A:		str        =		{
           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
       A:		Union[str, Any]        =		kwargs_text.pop('''model'''				,     SCREAMING_SNAKE_CASE_  )
       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

                 A:		List[str]        =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
                 A:		Optional[int]        =		text_config

            A:		Optional[int]        =		FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		kwargs_vision.pop('''model'''				,     SCREAMING_SNAKE_CASE_  )
       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

                 A:		Any        =		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
                 A:		Dict        =		vision_config

            A:		Union[str, Any]        =		FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       # instantiate config with corresponding kwargs
       A:		Dict        =		kwargs.pop('''dtype'''				,     jnp.floataa  )
       A:		Any        =		HybridCLIPConfig.from_text_vision_configs(text_model.config				,     vision_model.config				,     **SCREAMING_SNAKE_CASE_  )

       # init model
       A:		List[str]        =		cls(SCREAMING_SNAKE_CASE_				,     *SCREAMING_SNAKE_CASE_				,     dtype=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       if vision_config.model_type == "clip":
            A:		List[Any]        =		vision_model.params['''vision_model''']
            A:		int        =		vision_model.params['''visual_projection''']['''kernel''']
       else:
            A:		Tuple        =		vision_model.params

       A:		Tuple        =		text_model.params

       return model






