


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
           TimesformerForVideoClassification,
           TimesformerModel,
       )
       from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor


class   a__	:

       def __init__(   self      :     Optional[int]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     str=1_3      ,							lowerCamelCase_      :     Optional[int]=1_0      ,							lowerCamelCase_      :     Union[str, Any]=3      ,							lowerCamelCase_      :     List[Any]=2      ,							lowerCamelCase_      :     int=2      ,							lowerCamelCase_      :     str=True      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     List[str]=3_2      ,							lowerCamelCase_      :     Any=5      ,							lowerCamelCase_      :     Optional[Any]=4      ,							lowerCamelCase_      :     str=3_7      ,							lowerCamelCase_      :     Any="gelu"      ,							lowerCamelCase_      :     List[Any]=0.1      ,							lowerCamelCase_      :     Dict=0.1      ,							lowerCamelCase_      :     int=1_0      ,							lowerCamelCase_      :     str=0.0_2      ,							lowerCamelCase_      :     Dict="divided_space_time"      ,							lowerCamelCase_      :     str=None      ,							):
            a_    :     Union[str, Any]										=    parent
            a_    :     Any										=    batch_size
            a_    :     Union[str, Any]										=    image_size
            a_    :     List[Any]										=    num_channels
            a_    :     List[Any]										=    patch_size
            a_    :     Optional[Any]										=    num_frames
            a_    :     List[Any]										=    is_training
            a_    :     int										=    use_labels
            a_    :     Optional[Any]										=    hidden_size
            a_    :     Optional[int]										=    num_hidden_layers
            a_    :     List[Any]										=    num_attention_heads
            a_    :     List[str]										=    intermediate_size
            a_    :     Any										=    hidden_act
            a_    :     Union[str, Any]										=    hidden_dropout_prob
            a_    :     Optional[Any]										=    attention_probs_dropout_prob
            a_    :     int										=    attention_type
            a_    :     List[str]										=    initializer_range
            a_    :     List[Any]										=    scope
            a_    :     str										=    num_labels

            # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
            a_    :     Union[str, Any]										=    (image_size // patch_size) ** 2
            a_    :     List[str]										=    (num_frames) * self.num_patches_per_frame + 1

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Any										=    floats_tensor(
                [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

            a_    :     List[Any]										=    None
            if self.use_labels:
                 a_    :     List[Any]										=    ids_tensor([self.batch_size]      ,							self.num_labels    )

            a_    :     Union[str, Any]										=    self.get_config()

            return config, pixel_values, labels

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    TimesformerConfig(
                image_size=self.image_size      ,							patch_size=self.patch_size      ,							num_channels=self.num_channels      ,							num_frames=self.num_frames      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							initializer_range=self.initializer_range      ,							attention_type=self.attention_type      ,							)
            a_    :     List[Any]										=    self.num_labels
            return config

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[str]    ):
            a_    :     List[str]										=    TimesformerModel(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     List[Any]										=    model(lowerCamelCase_    )
            self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    TimesformerForVideoClassification(lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            a_    :     Optional[int]										=    model(lowerCamelCase_    )

            # verify the logits shape
            a_    :     Dict										=    torch.Size((self.batch_size, self.num_labels)    )
            self.parent.assertEqual(result.logits.shape      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     int										=    self.prepare_config_and_inputs()
            a_    :     Tuple										=    config_and_inputs
            a_    :     List[str]										=    {"""pixel_values""": pixel_values}
            return config, inputs_dict


@require_torch
class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Union[str, Any]							    =     (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
       lowerCamelCase__:     Dict							    =     (
           {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
           if is_torch_available()
           else {}
       )

       lowerCamelCase__:     Any							    =     False
       lowerCamelCase__:     Tuple							    =     False
       lowerCamelCase__:     Optional[Any]							    =     False
       lowerCamelCase__:     Any							    =     False

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Tuple										=    TimesformerModelTester(self    )
            a_    :     Any										=    ConfigTester(
                self      ,							config_class=lowerCamelCase_      ,							has_text_modality=lowerCamelCase_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Any]=False    ):
            a_    :     List[str]										=    copy.deepcopy(lowerCamelCase_    )

            if return_labels:
                 if model_class in get_values(lowerCamelCase_    ):
                      a_    :     Any										=    torch.zeros(
                          self.model_tester.batch_size      ,							dtype=torch.long      ,							device=lowerCamelCase_    )

            return inputs_dict

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            self.config_tester.run_common_tests()

       @unittest.skip(reason="""TimeSformer does not use inputs_embeds"""    )
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            pass

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     List[str]										=    model_class(lowerCamelCase_    )
                 self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)    )
                 a_    :     Any										=    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCamelCase_      ,							nn.Linear    )    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     List[Any]										=    model_class(lowerCamelCase_    )
                 a_    :     int										=    inspect.signature(model.forward    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a_    :     Optional[Any]										=    [*signature.parameters.keys()]

                 a_    :     Union[str, Any]										=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Optional[int]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Union[str, Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_video_classification(*lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     Any    ):
            for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_    :     str										=    TimesformerModel.from_pretrained(lowerCamelCase_    )
                 self.assertIsNotNone(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            if not self.has_attentions:
                 pass

            else:
                 a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs_for_common()
                 a_    :     Optional[int]										=    True

                 for model_class in self.all_model_classes:
                      a_    :     Any										=    self.model_tester.seq_length
                      a_    :     Optional[int]										=    self.model_tester.num_frames

                      a_    :     str										=    True
                      a_    :     str										=    False
                      a_    :     Optional[int]										=    True
                      a_    :     Optional[int]										=    model_class(lowerCamelCase_    )
                      model.to(lowerCamelCase_    )
                      model.eval()
                      with torch.no_grad():
                           a_    :     List[str]										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )
                      a_    :     Optional[Any]										=    outputs.attentions
                      self.assertEqual(len(lowerCamelCase_    )      ,							self.model_tester.num_hidden_layers    )

                      # check that output_attentions also work using config
                      del inputs_dict["output_attentions"]
                      a_    :     List[Any]										=    True
                      a_    :     Optional[int]										=    model_class(lowerCamelCase_    )
                      model.to(lowerCamelCase_    )
                      model.eval()
                      with torch.no_grad():
                           a_    :     List[Any]										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )
                      a_    :     Union[str, Any]										=    outputs.attentions
                      self.assertEqual(len(lowerCamelCase_    )      ,							self.model_tester.num_hidden_layers    )

                      # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                      self.assertListEqual(
                          list(attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]      ,							)
                      a_    :     List[Any]										=    len(lowerCamelCase_    )

                      # Check attention is always last and order is fine
                      a_    :     str										=    True
                      a_    :     List[str]										=    True
                      a_    :     List[Any]										=    model_class(lowerCamelCase_    )
                      model.to(lowerCamelCase_    )
                      model.eval()
                      with torch.no_grad():
                           a_    :     Any										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )

                      self.assertEqual(out_len + 1      ,							len(lowerCamelCase_    )    )

                      a_    :     Any										=    outputs.attentions

                      self.assertEqual(len(lowerCamelCase_    )      ,							self.model_tester.num_hidden_layers    )

                      # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                      self.assertListEqual(
                          list(self_attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]      ,							)

       def 						UpperCAmelCase(   self      :     Dict    ):
            def check_hidden_states_output(lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Union[str, Any]    ):
                 a_    :     Union[str, Any]										=    model_class(lowerCamelCase_    )
                 model.to(lowerCamelCase_    )
                 model.eval()

                 with torch.no_grad():
                      a_    :     List[str]										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )

                 a_    :     List[str]										=    outputs.hidden_states
                 a_    :     Union[str, Any]										=    self.model_tester.num_hidden_layers + 1
                 self.assertEqual(len(lowerCamelCase_    )      ,							lowerCamelCase_    )

                 a_    :     List[str]										=    self.model_tester.seq_length

                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]    )      ,							[seq_length, self.model_tester.hidden_size]      ,							)

            a_    :     Dict										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     Any										=    True
                 check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a_    :     Tuple										=    True

                 check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )



def  _a			(						):
     a_    :     Tuple										=    hf_hub_download(
         repo_id="""hf-internal-testing/spaghetti-video"""  ,  filename="""eating_spaghetti.npy"""  ,  repo_type="""dataset""" )
     a_    :     int										=    np.load(__UpperCamelCase )
     return list(__UpperCamelCase )


@require_torch
@require_vision
class   a__	(						unittest.TestCase    ):

       @cached_property
       def 						UpperCAmelCase(   self      :     int    ):
            # logits were tested with a different mean and std, so we use the same here
            return (
                VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,							image_std=[0.5, 0.5, 0.5]    )
                if is_vision_available()
                else None
            )

       @slow
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Any										=    TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400"""    ).to(
                lowerCamelCase_    )

            a_    :     Any										=    self.default_image_processor
            a_    :     List[str]										=    prepare_video()
            a_    :     Any										=    image_processor(video[:8]      ,							return_tensors="""pt"""    ).to(lowerCamelCase_    )

            # forward pass
            with torch.no_grad():
                 a_    :     Dict										=    model(**lowerCamelCase_    )

            # verify the logits
            a_    :     List[str]										=    torch.Size((1, 4_0_0)    )
            self.assertEqual(outputs.logits.shape      ,							lowerCamelCase_    )

            a_    :     Optional[Any]										=    torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]    ).to(lowerCamelCase_    )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							lowerCamelCase_      ,							atol=1E-4    )    )







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  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


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



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    args
            a_    :     Optional[int]										=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

            with open(self.args.csv_file      ,							newline=""""""    ) as csv_file:
                 a_    :     List[Any]										=    csv.DictReader(lowerCamelCase_    )
                 for row in reader:
                      a_    :     List[str]										=    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_    :     Dict										=    float(row["""result"""]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_			,			a_    :     Tuple										=    plt.subplots()
            a_    :     List[Any]										=    """Time usage""" if self.args.is_time else """Memory usage"""
            a_    :     Optional[int]										=    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_    :     Union[str, Any]										=    sorted(set(self.result_dict[model_name]["""seq_len"""]    )    )
                 a_    :     List[str]										=    self.result_dict[model_name]["""result"""]

                 ((a_)			,			(a_))    :     List[Any]										=    (
                     (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                 )

                 a_    :     str										=    (
                     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_    :     Optional[int]										=    np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=lowerCamelCase_      ,							)
                      else:
                           a_    :     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_)			,			(a_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

                      a_    :     List[Any]										=    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_    :     int										=    title_str[:-4]
            a_    :     Tuple										=    """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  _a			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()



