



from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				str					)->int:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.config_class(**self.inputs_dict					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"hidden_sizes"					)					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"neck_hidden_sizes"					)					)
      self.parent.assertTrue(hasattr(UpperCamelCase  ,	"num_attention_heads"					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Optional[Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str]=1_3  ,	UpperCamelCase			:				Tuple=3_2  ,	UpperCamelCase			:				Any=2  ,	UpperCamelCase			:				Any=3  ,	UpperCamelCase			:				Optional[Any]=6_4_0  ,	UpperCamelCase			:				Tuple=4  ,	UpperCamelCase			:				Optional[int]="silu"  ,	UpperCamelCase			:				int=3  ,	UpperCamelCase			:				Optional[Any]=3_2  ,	UpperCamelCase			:				List[Any]=0.1  ,	UpperCamelCase			:				int=0.1  ,	UpperCamelCase			:				Tuple=0.1  ,	UpperCamelCase			:				Dict=0.0_2  ,	UpperCamelCase			:				Any=True  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Any=1_0  ,	UpperCamelCase			:				str=None  ,	)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_size
      __SCREAMING_SNAKE_CASE							:     int							=						patch_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						last_hidden_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     str							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     Dict							=						conv_kernel_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						output_stride
      __SCREAMING_SNAKE_CASE							:     Dict							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Dict							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						classifier_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Any							=						use_labels
      __SCREAMING_SNAKE_CASE							:     Tuple							=						is_training
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						num_labels
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scope





     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

      __SCREAMING_SNAKE_CASE							:     str							=						None
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						None
      if self.use_labels:
       __SCREAMING_SNAKE_CASE							:     Any							=						ids_tensor([self.batch_size]  ,	self.num_labels					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,	self.num_labels					)

      __SCREAMING_SNAKE_CASE							:     str							=						self.get_config()

      return config, pixel_values, labels, pixel_labels





     def 						__snake_case     (							self			:				Any					)->Any:
      return MobileViTConfig(
          image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	num_attention_heads=self.num_attention_heads  ,	hidden_act=self.hidden_act  ,	conv_kernel_size=self.conv_kernel_size  ,	output_stride=self.output_stride  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	classifier_dropout_prob=self.classifier_dropout_prob  ,	initializer_range=self.initializer_range  ,	)





     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Tuple					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						TFMobileViTModel(config=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						model(UpperCamelCase  ,	training=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,	(self.batch_size, self.last_hidden_size, expected_height, expected_width)					)





     def 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Any					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     str							=						TFMobileViTForImageClassification(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(UpperCamelCase  ,	labels=UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)					)





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				Union[str, Any]					)->Any:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     List[str]							=						TFMobileViTForSemanticSegmentation(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_size // self.output_stride

      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(
          result.logits.shape  ,	(self.batch_size, self.num_labels, expected_height, expected_width)					)

      __SCREAMING_SNAKE_CASE							:     int							=						model(UpperCamelCase  ,	labels=UpperCamelCase  ,	training=UpperCamelCase					)
      self.parent.assertEqual(
          result.logits.shape  ,	(self.batch_size, self.num_labels, expected_height, expected_width)					)






     def 						__snake_case     (							self			:				Dict					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						config_and_inputs
      __SCREAMING_SNAKE_CASE							:     int							=						{"pixel_values": pixel_values}
      return config, inputs_dict






@require_tf
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      (
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     lowerCAmelCase       =      (
         {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False
     lowerCAmelCase       =      False





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						TFMobileViTModelTester(self					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						TFMobileViTConfigTester(self  ,	config_class=UpperCamelCase  ,	has_text_modality=UpperCamelCase					)





     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      self.config_tester.run_common_tests()





     @unittest.skip(reason="MobileViT does not use inputs_embeds"					)
     def 						__snake_case     (							self			:				Any					)->Dict:
      pass





     @unittest.skip(reason="MobileViT does not support input and output embeddings"					)
     def 						__snake_case     (							self			:				Dict					)->str:
      pass





     @unittest.skip(reason="MobileViT does not output attentions"					)
     def 						__snake_case     (							self			:				Dict					)->str:
      pass





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						model_class(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     str							=						inspect.signature(model.call					)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __SCREAMING_SNAKE_CASE							:     int							=						[*signature.parameters.keys()]

       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						["pixel_values"]
       self.assertListEqual(arg_names[:1]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				Dict					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Union[str, Any]:
      def check_hidden_states_output(UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				str					):
       __SCREAMING_SNAKE_CASE							:     int							=						model_class(UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     int							=						model(**self._prepare_for_class(UpperCamelCase  ,	UpperCamelCase					)					)

       __SCREAMING_SNAKE_CASE							:     Tuple							=						outputs.hidden_states

       __SCREAMING_SNAKE_CASE							:     List[Any]							=						5
       self.assertEqual(len(UpperCamelCase					)  ,	UpperCamelCase					)

       # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
       # with the width and height being successively divided by 2.
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						2
       for i in range(len(UpperCamelCase					)					):
        self.assertListEqual(
            list(hidden_states[i].shape[-2:]					)  ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,	)
        divisor *= 2

       self.assertEqual(self.model_tester.output_stride  ,	divisor // 2					)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE							:     int							=						True
       check_hidden_states_output(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase					)

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

       check_hidden_states_output(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				Any					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[int]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase					)





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices("GPU"					)					) == 0  ,	reason="TF does not support backprop for grouped convolutions on CPU."  ,	)
     def 						__snake_case     (							self			:				str					)->List[str]:
      super().test_dataset_conversion()





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				int=2E-1  ,	UpperCamelCase			:				Optional[Any]=2E-1					)->List[Any]:
      self.assertTrue(np.allclose(UpperCamelCase  ,	UpperCamelCase  ,	atol=UpperCamelCase  ,	rtol=UpperCamelCase					)					)





     @unittest.skipIf(
         not is_tf_available() or len(tf.config.list_physical_devices("GPU"					)					) == 0  ,	reason="TF does not support backprop for grouped convolutions on CPU."  ,	)
     @slow
     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
       if model_class.__name__ != "TFMobileViTModel":
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						model_class(UpperCamelCase					)
        if getattr(UpperCamelCase  ,	"hf_compute_loss"  ,	UpperCamelCase					):
         super().test_keras_fit()





     def 						__snake_case     (							self			:				str					)->int:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       # set an ignore index to correctly test the masked loss used in
       # `TFMobileViTForSemanticSegmentation`.
       if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
        __SCREAMING_SNAKE_CASE							:     List[str]							=						5

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model_class(UpperCamelCase					)
       if getattr(UpperCamelCase  ,	"hf_compute_loss"  ,	UpperCamelCase					):
        # The number of elements in the loss should be the same as the number of elements in the label
        __SCREAMING_SNAKE_CASE							:     Tuple							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()  ,	reverse=UpperCamelCase					)[0]
        ]
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        __SCREAMING_SNAKE_CASE							:     Dict							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{"input_ids", "pixel_values", "input_features"}
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						possible_input_names.intersection(set(UpperCamelCase					)					).pop()
        __SCREAMING_SNAKE_CASE							:     List[str]							=						prepared_for_class.pop(UpperCamelCase					)

        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						model(UpperCamelCase  ,	**UpperCamelCase					)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

        # Test that model correctly compute the loss when we mask some positions
        __SCREAMING_SNAKE_CASE							:     Tuple							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Any							=						{"input_ids", "pixel_values", "input_features"}
        __SCREAMING_SNAKE_CASE							:     Dict							=						possible_input_names.intersection(set(UpperCamelCase					)					).pop()
        __SCREAMING_SNAKE_CASE							:     Dict							=						prepared_for_class.pop(UpperCamelCase					)
        if "labels" in prepared_for_class:
         __SCREAMING_SNAKE_CASE							:     List[str]							=						prepared_for_class["labels"].numpy()
         if len(labels.shape					) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          __SCREAMING_SNAKE_CASE							:     Tuple							=						tf.convert_to_tensor(UpperCamelCase					)
          __SCREAMING_SNAKE_CASE							:     Tuple							=						model(UpperCamelCase  ,	**UpperCamelCase					)[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
          self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)
        __SCREAMING_SNAKE_CASE							:     Tuple							=						model(UpperCamelCase					)[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

        # Test that model correctly compute the loss with a tuple
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self._prepare_for_class(inputs_dict.copy()  ,	UpperCamelCase  ,	return_labels=UpperCamelCase					)

        # Get keys that were added with the _prepare_for_class function
        __SCREAMING_SNAKE_CASE							:     int							=						prepared_for_class.keys() - inputs_dict.keys()
        __SCREAMING_SNAKE_CASE							:     int							=						inspect.signature(model.call					).parameters
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						list(signature.keys()					)

        # Create a dictionary holding the location of the tensors in the tuple
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						{0: input_name}
        for label_key in label_keys:
         __SCREAMING_SNAKE_CASE							:     List[Any]							=						signature_names.index(UpperCamelCase					)
         __SCREAMING_SNAKE_CASE							:     str							=						label_key
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sorted(tuple_index_mapping.items()					)
        # Initialize a list with their default values, update the values and convert to a tuple
        __SCREAMING_SNAKE_CASE							:     Any							=						[]

        for name in signature_names:
         if name != "kwargs":
          list_input.append(signature[name].default					)

        for index, value in sorted_tuple_index_mapping:
         __SCREAMING_SNAKE_CASE							:     Any							=						prepared_for_class[value]

        __SCREAMING_SNAKE_CASE							:     List[Any]							=						tuple(UpperCamelCase					)

        # Send to model
        __SCREAMING_SNAKE_CASE							:     List[str]							=						model(tuple_input[:-1]					)[0]

        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)






     @slow
     def 						__snake_case     (							self			:				str					)->List[Any]:
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						TFMobileViTModel.from_pretrained(UpperCamelCase					)
       self.assertIsNotNone(UpperCamelCase					)






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
 return image






@require_tf
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     @slow
     def 						__snake_case     (							self			:				Union[str, Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						prepare_img()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_processor(images=UpperCamelCase  ,	return_tensors="tf"					)

      # forward pass
      __SCREAMING_SNAKE_CASE							:     str							=						model(**UpperCamelCase  ,	training=UpperCamelCase					)

      # verify the logits
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tf.TensorShape((1, 1_0_0_0)					)
      self.assertEqual(outputs.logits.shape  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     int							=						tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]					)

      tf.debugging.assert_near(outputs.logits[0, :3]  ,	UpperCamelCase  ,	atol=1E-4  ,	rtol=1E-04					)






     @slow
     def 						__snake_case     (							self			:				Dict					)->Optional[Any]:
      # `from_pt` will be removed
      __SCREAMING_SNAKE_CASE							:     Dict							=						TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_img()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(images=UpperCamelCase  ,	return_tensors="tf"					)

      # forward pass
      __SCREAMING_SNAKE_CASE							:     List[str]							=						model(inputs.pixel_values  ,	training=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

      # verify the logits
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						tf.TensorShape((1, 2_1, 3_2, 3_2)					)
      self.assertEqual(logits.shape  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						tf.constant(
          [
              [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
              [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
              [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
          ]					)

      tf.debugging.assert_near(logits[0, :3, :3, :3]  ,	UpperCamelCase  ,	rtol=1E-4  ,	atol=1E-4					)













import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase										=					1_6
_UpperCAmelCase										=					3_2




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_ = 16	)  ->		Dict:
       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("bert-base-cased"	)
       UpperCamelCase_ 							=	load_dataset("glue"       ,		"mrpc"	)

       def tokenize_function(UpperCamelCase_	):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_ 							=	tokenizer(examples["sentence1"]       ,		examples["sentence2"]       ,		truncation=UpperCamelCase_       ,		max_length=UpperCamelCase_	)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCamelCase_ 							=	datasets.map(
                  UpperCamelCase_       ,		batched=UpperCamelCase_       ,		remove_columns=["idx", "sentence1", "sentence2"]       ,		)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCamelCase_ 							=	tokenized_datasets.rename_column("label"       ,		"labels"	)

       def collate_fn(UpperCamelCase_	):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCamelCase_ 							=	128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase_ 							=	16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_ 							=	8
              else:
                     UpperCamelCase_ 							=	None

              return tokenizer.pad(
                  UpperCamelCase_       ,		padding="longest"       ,		max_length=UpperCamelCase_       ,		pad_to_multiple_of=UpperCamelCase_       ,		return_tensors="pt"       ,		)

       # Instantiate dataloaders.
       UpperCamelCase_ 							=	DataLoader(
           tokenized_datasets["train"]       ,		shuffle=UpperCamelCase_       ,		collate_fn=UpperCamelCase_       ,		batch_size=UpperCamelCase_	)
       UpperCamelCase_ 							=	DataLoader(
           tokenized_datasets["validation"]       ,		shuffle=UpperCamelCase_       ,		collate_fn=UpperCamelCase_       ,		batch_size=UpperCamelCase_	)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     _UpperCAmelCase										=					mocked_dataloaders  # noqa: F811




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,		UpperCamelCase_	) == "1":
              UpperCamelCase_ 							=	2
       # Initialize accelerator
       UpperCamelCase_ 							=	Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision	)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase_ 							=	config["lr"]
       UpperCamelCase_ 							=	int(config["num_epochs"]	)
       UpperCamelCase_ 							=	int(config["seed"]	)
       UpperCamelCase_ 							=	int(config["batch_size"]	)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps       ,		"isdigit"	):
              if args.checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCamelCase_ 							=	int(args.checkpointing_steps	)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
       else:
              UpperCamelCase_ 							=	None

       set_seed(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	get_dataloaders(UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	evaluate.load("glue"       ,		"mrpc"	)

       # If the batch size is too big we use gradient accumulation
       UpperCamelCase_ 							=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCamelCase_ 							=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCamelCase_ 							=	MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase_ 							=	AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,		return_dict=UpperCamelCase_	)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCamelCase_ 							=	model.to(accelerator.device	)

       # Instantiate optimizer
       UpperCamelCase_ 							=	AdamW(params=model.parameters()       ,		lr=UpperCamelCase_	)

       # Instantiate scheduler
       UpperCamelCase_ 							=	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_       ,		num_warmup_steps=100       ,		num_training_steps=(len(UpperCamelCase_	) * num_epochs) // gradient_accumulation_steps       ,		)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.prepare(
           UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCamelCase_ 							=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCamelCase_ 							=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
                     accelerator.load_state(args.resume_from_checkpoint	)
                     UpperCamelCase_ 							=	os.path.basename(args.resume_from_checkpoint	)
              else:
                     # Get the most recent checkpoint
                     UpperCamelCase_ 							=	[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
                     dirs.sort(key=os.path.getctime	)
                     UpperCamelCase_ 							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCamelCase_ 							=	os.path.splitext(UpperCamelCase_	)[0]

              if "epoch" in training_difference:
                     UpperCamelCase_ 							=	int(training_difference.replace("epoch_"       ,		""	)	) + 1
                     UpperCamelCase_ 							=	None
              else:
                     UpperCamelCase_ 							=	int(training_difference.replace("step_"       ,		""	)	)
                     UpperCamelCase_ 							=	resume_step // len(UpperCamelCase_	)
                     resume_step -= starting_epoch * len(UpperCamelCase_	)

    # Now we train the model
       for epoch in range(UpperCamelCase_       ,		UpperCamelCase_	):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCamelCase_ 							=	accelerator.skip_first_batches(UpperCamelCase_       ,		UpperCamelCase_	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_ 							=	train_dataloader
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.loss
                     UpperCamelCase_ 							=	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(UpperCamelCase_       ,		UpperCamelCase_	):
                            UpperCamelCase_ 							=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                                   accelerator.save_state(UpperCamelCase_	)

              model.eval()
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device	)
                     with torch.no_grad():
                            UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.logits.argmax(dim=-1	)
                     UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.gather_for_metrics((predictions, batch["labels"])	)
                     metric.add_batch(
                         predictions=UpperCamelCase_       ,		references=UpperCamelCase_       ,		)

              UpperCamelCase_ 							=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''       ,		UpperCamelCase_	)

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                     accelerator.save_state(UpperCamelCase_	)




def 			lowerCAmelCase_			(				)  ->		Dict:
       UpperCamelCase_ 							=	argparse.ArgumentParser(description="Simple example of training script."	)
       parser.add_argument(
           "--mixed_precision"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		choices=["no", "fp16", "bf16", "fp8"]       ,		help="Whether to use mixed precision. Choose"
           "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
           "and an Nvidia Ampere GPU."       ,		)
       parser.add_argument("--cpu"       ,		action="store_true"       ,		help="If passed, will train on the CPU."	)
       parser.add_argument(
           "--checkpointing_steps"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,		)
       parser.add_argument(
           "--output_dir"       ,		type=UpperCamelCase_       ,		default="."       ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,		)
       parser.add_argument(
           "--resume_from_checkpoint"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="If the training should continue from a checkpoint folder."       ,		)
       UpperCamelCase_ 							=	parser.parse_args()
       UpperCamelCase_ 							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase_       ,		UpperCamelCase_	)


if __name__ == "__main__":
     main()

