




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    _lowerCamelCase							=      False

if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        PixaStructImageProcessor,
        PixaStructProcessor,
        PreTrainedTokenizerFast,
        TaTokenizer,
    )


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."      ,		)
@require_vision
@require_torch
class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =tempfile.mkdtemp()

            a			   =PixaStructImageProcessor()
            a			   =TaTokenizer.from_pretrained("""t5-small"""	)

            a			   =PixaStructProcessor(_lowerCAmelCase ,    _lowerCAmelCase	)

            processor.save_pretrained(self.tmpdirname	)
     def        lowerCAmelCase__  (				self ,    **_lowerCAmelCase	):
            return AutoProcessor.from_pretrained(self.tmpdirname ,    **_lowerCAmelCase	).tokenizer
     def        lowerCAmelCase__  (				self ,    **_lowerCAmelCase	):
            return AutoProcessor.from_pretrained(self.tmpdirname ,    **_lowerCAmelCase	).image_processor
     def        lowerCAmelCase__  (				self	):
            shutil.rmtree(self.tmpdirname	)
     def        lowerCAmelCase__  (				self	):
            a			   =[np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta	)]

            a			   =[Image.fromarray(np.moveaxis(_lowerCAmelCase ,    0 ,    -1	)	) for x in image_inputs]

            return image_inputs
     def        lowerCAmelCase__  (				self	):
            a			   =PixaStructProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()	)
            processor.save_pretrained(self.tmpdirname	)

            a			   =self.get_tokenizer(bos_token="""(BOS)""" ,    eos_token="""(EOS)"""	)
            a			   =self.get_image_processor(do_normalize=_lowerCAmelCase ,    padding_value=1.0	)

            a			   =PixaStructProcessor.from_pretrained(
                self.tmpdirname ,    bos_token="""(BOS)""" ,    eos_token="""(EOS)""" ,    do_normalize=_lowerCAmelCase ,    padding_value=1.0	)

            self.assertEqual(processor.tokenizer.get_vocab() ,    tokenizer_add_kwargs.get_vocab()	)
            self.assertIsInstance(processor.tokenizer ,    _lowerCAmelCase	)

            self.assertEqual(processor.image_processor.to_json_string() ,    image_processor_add_kwargs.to_json_string()	)
            self.assertIsInstance(processor.image_processor ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   =self.prepare_image_inputs()

            a			   =image_processor(_lowerCAmelCase ,    return_tensors="""np"""	)
            a			   =processor(images=_lowerCAmelCase ,    return_tensors="""np"""	)

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum() ,    input_processor[key].sum() ,    delta=1E-2	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   ="""lower newer"""

            a			   =processor(text=_lowerCAmelCase	)

            a			   =tokenizer(_lowerCAmelCase ,    return_token_type_ids=_lowerCAmelCase ,    add_special_tokens=_lowerCAmelCase	)

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key] ,    encoded_processor[key]	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   ="""lower newer"""
            a			   =self.prepare_image_inputs()

            a			   =processor(text=_lowerCAmelCase ,    images=_lowerCAmelCase	)

            self.assertListEqual(
                list(inputs.keys()	) ,    ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]	)

            # test if it raises when no input is passed
            with pytest.raises(_lowerCAmelCase	):
                   processor()
     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   ="""lower newer"""
            a			   =self.prepare_image_inputs()

            a			   =processor(text=_lowerCAmelCase ,    images=_lowerCAmelCase	)

            a			   =[512, 1_024, 2_048, 4_096]
            a			   =[770, 770, 770, 770]
            # with text
            for i, max_patch in enumerate(_lowerCAmelCase	):
                   a			   =processor(text=_lowerCAmelCase ,    images=_lowerCAmelCase ,    max_patches=_lowerCAmelCase	)
                   self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,    _lowerCAmelCase	)
                   self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,    expected_hidden_size[i]	)

            # without text input
            for i, max_patch in enumerate(_lowerCAmelCase	):
                   a			   =processor(images=_lowerCAmelCase ,    max_patches=_lowerCAmelCase	)
                   self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,    _lowerCAmelCase	)
                   self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,    expected_hidden_size[i]	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            a			   =processor.batch_decode(_lowerCAmelCase	)
            a			   =tokenizer.batch_decode(_lowerCAmelCase	)

            self.assertListEqual(_lowerCAmelCase ,    _lowerCAmelCase	)






     def        lowerCAmelCase__  (				self	):
            a			   =self.get_image_processor()
            a			   =self.get_tokenizer()

            a			   =PixaStructProcessor(tokenizer=_lowerCAmelCase ,    image_processor=_lowerCAmelCase	)

            a			   ="""lower newer"""
            a			   =self.prepare_image_inputs()

            a			   =processor(text=_lowerCAmelCase ,    images=_lowerCAmelCase	)

            # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
            self.assertListEqual(
                list(inputs.keys()	) ,    ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]	)

            a			   =processor(text=_lowerCAmelCase	)

            # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
            self.assertListEqual(list(inputs.keys()	) ,    ["""input_ids""", """attention_mask"""]	)






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 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]=None   ,       UpperCAmelCase_	:      Union[str, Any]=None      )->			Tuple:

       """simple docstring"""




       return field(default_factory=lambda: default   ,       metadata=UpperCAmelCase_      )


@dataclass
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					str     					=    field(
         metadata={"help": "The csv file to plot."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Disable logarithmic scale when plotting"}      ,		)
     _SCREAMING_SNAKE_CASE      :					bool     					=    field(
         default=UpperCAmelCase__      ,		metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         }      ,		)
     _SCREAMING_SNAKE_CASE      :					Optional[str]     					=    field(
         default=UpperCAmelCase__      ,		metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,		)
     _SCREAMING_SNAKE_CASE      :					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 							lowerCamelCase    (							UpperCAmelCase_	:      Dict      )->			int:

       """simple docstring"""




       try:
              int(UpperCAmelCase_      )
              return True
       except ValueError:
              return False





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]      )->			str:

       """simple docstring"""




       try:
              float(UpperCAmelCase_      )
              return True
       except ValueError:
              return False


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase	):
            a			   =args
            a			   =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

            with open(self.args.csv_file ,    newline=""""""	) as csv_file:
                   a			   =csv.DictReader(_lowerCAmelCase	)
                   for row in reader:
                          a			   =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			   =int(row["""result"""]	)
                          elif can_convert_to_float(row["""result"""]	):
                                 # value is not None
                                 a			   =float(row["""result"""]	)






     def        lowerCAmelCase__  (				self	):
            a   ,      a			   =plt.subplots()
            a			   ="""Time usage""" if self.args.is_time else """Memory usage"""
            a			   =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			   =sorted(set(self.result_dict[model_name]["""bsz"""]	)	)
                   a			   =sorted(set(self.result_dict[model_name]["""seq_len"""]	)	)
                   a			   =self.result_dict[model_name]["""result"""]

                   ((a)   ,      (a))			   =(
                       (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                   )

                   a			   =(
                       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			   =np.asarray(
                                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,    dtype=_lowerCAmelCase ,    )
                          else:
                                 a			   =np.asarray(
                                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,    dtype=np.floataa ,    )

                          ((a)   ,      (a))			   =(
                              ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                          )

                          a			   =np.asarray(_lowerCAmelCase ,    _lowerCAmelCase	)[: len(_lowerCAmelCase	)]
                          plt.scatter(
                              _lowerCAmelCase ,    _lowerCAmelCase ,    label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
                          plt.plot(_lowerCAmelCase ,    _lowerCAmelCase ,    """--"""	)

                   title_str += F''' {label_model_name} vs.'''

            a			   =title_str[:-4]
            a			   ="""Time in s""" if self.args.is_time else """Memory in MB"""

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

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





def 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




       a			   =HfArgumentParser(UpperCAmelCase_      )
       a			   =parser.parse_args_into_dataclasses()[0]
       a			   =Plot(args=UpperCAmelCase_      )
       plot.plot()


if __name__ == "__main__":
    main()
