

'''simple docstring'''




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     lowercase_		(						A__	):

  '''simple docstring'''





  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		self.config_class(**self.inputs_dict       )
    self.parent.assertTrue(hasattr(__snake_case				,	'hidden_sizes'       )       )
    self.parent.assertTrue(hasattr(__snake_case				,	'neck_hidden_sizes'       )       )
    self.parent.assertTrue(hasattr(__snake_case				,	'num_attention_heads'       )       )




class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=1_3				,	a_=3_2				,	a_=2				,	a_=3				,	a_=6_4_0				,	a_=4				,	a_="silu"				,	a_=3				,	a_=3_2				,	a_=0.1				,	a_=0.1				,	a_=0.1				,	a_=0.02				,	a_=True				,	a_=True				,	a_=1_0				,	a_=None				,	)							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		parent
    UpperCAmelCase  							=		batch_size
    UpperCAmelCase  							=		image_size
    UpperCAmelCase  							=		patch_size
    UpperCAmelCase  							=		num_channels
    UpperCAmelCase  							=		last_hidden_size
    UpperCAmelCase  							=		num_attention_heads
    UpperCAmelCase  							=		hidden_act
    UpperCAmelCase  							=		conv_kernel_size
    UpperCAmelCase  							=		output_stride
    UpperCAmelCase  							=		hidden_dropout_prob
    UpperCAmelCase  							=		attention_probs_dropout_prob
    UpperCAmelCase  							=		classifier_dropout_prob
    UpperCAmelCase  							=		use_labels
    UpperCAmelCase  							=		is_training
    UpperCAmelCase  							=		num_labels
    UpperCAmelCase  							=		initializer_range
    UpperCAmelCase  							=		scope
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    if self.use_labels:
      UpperCAmelCase  							=		ids_tensor([self.batch_size]				,	self.num_labels       )
      UpperCAmelCase  							=		ids_tensor([self.batch_size, self.image_size, self.image_size]				,	self.num_labels       )

    UpperCAmelCase  							=		self.get_config()

    return config, pixel_values, labels, pixel_labels
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    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				,	a_				,	a_				,	a_				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		TFMobileViTModel(config=__snake_case       )
    UpperCAmelCase  							=		model(__snake_case				,	training=__snake_case       )
    UpperCAmelCase  							=		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				,	a_				,	a_				,	a_				,	a_       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		self.num_labels
    UpperCAmelCase  							=		TFMobileViTForImageClassification(__snake_case       )
    UpperCAmelCase  							=		model(__snake_case				,	labels=__snake_case				,	training=__snake_case       )
    self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels)       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.num_labels
    UpperCAmelCase  							=		TFMobileViTForSemanticSegmentation(__snake_case       )
    UpperCAmelCase  							=		self.image_size // self.output_stride

    UpperCAmelCase  							=		model(__snake_case				,	training=__snake_case       )
    self.parent.assertEqual(
        result.logits.shape				,	(self.batch_size, self.num_labels, expected_height, expected_width)       )

    UpperCAmelCase  							=		model(__snake_case				,	labels=__snake_case				,	training=__snake_case       )
    self.parent.assertEqual(
        result.logits.shape				,	(self.batch_size, self.num_labels, expected_height, expected_width)       )





  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		self.prepare_config_and_inputs()
    UpperCAmelCase  							=		config_and_inputs
    UpperCAmelCase  							=		{'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_tf
class     lowercase_		(						A__	,    A__	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	List[Any]      					=				(
      (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
      if is_tf_available()
      else ()
  )
  __lowerCAmelCase   :	Any      					=				(
      {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase   :	List[str]      					=				False
  __lowerCAmelCase   :	Optional[Any]      					=				False
  __lowerCAmelCase   :	Dict      					=				False
  __lowerCAmelCase   :	List[Any]      					=				False
  __lowerCAmelCase   :	List[Any]      					=				False
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		TFMobileViTModelTester(self       )
    UpperCAmelCase  							=		TFMobileViTConfigTester(self				,	config_class=__snake_case				,	has_text_modality=__snake_case       )
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    self.config_tester.run_common_tests()
  @unittest.skip(reason='MobileViT does not use inputs_embeds'       )
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    pass
  @unittest.skip(reason='MobileViT does not support input and output embeddings'       )
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    pass
  @unittest.skip(reason='MobileViT does not output attentions'       )
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    pass
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      UpperCAmelCase  							=		model_class(__snake_case       )
      UpperCAmelCase  							=		inspect.signature(model.call       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      UpperCAmelCase  							=		[*signature.parameters.keys()]

      UpperCAmelCase  							=		['''pixel_values''']
      self.assertListEqual(arg_names[:1]				,	__snake_case       )
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__snake_case       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    def check_hidden_states_output(a_				,	a_				,	a_       ):
      UpperCAmelCase  							=		model_class(__snake_case       )

      UpperCAmelCase  							=		model(**self._prepare_for_class(__snake_case				,	__snake_case       )       )

      UpperCAmelCase  							=		outputs.hidden_states

      UpperCAmelCase  							=		5
      self.assertEqual(len(__snake_case       )				,	__snake_case       )

      # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
      # with the width and height being successively divided by 2.
      UpperCAmelCase  							=		2
      for i in range(len(__snake_case       )       ):
        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       )

    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      UpperCAmelCase  							=		True
      check_hidden_states_output(__snake_case				,	__snake_case				,	__snake_case       )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      UpperCAmelCase  							=		True

      check_hidden_states_output(__snake_case				,	__snake_case				,	__snake_case       )
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__snake_case       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case       )
  @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       )							->			Optional[int]:


    """simple docstring"""
    super().test_dataset_conversion()
  def   snake_case_     (       self				,	a_				,	a_				,	a_=2E-1				,	a_=2E-1       )							->			Any:


    """simple docstring"""
    self.assertTrue(np.allclose(__snake_case				,	__snake_case				,	atol=__snake_case				,	rtol=__snake_case       )       )
  @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       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		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":
        UpperCAmelCase  							=		model_class(__snake_case       )
        if getattr(__snake_case				,	'hf_compute_loss'				,	__snake_case       ):
          super().test_keras_fit()
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		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":
        UpperCAmelCase  							=		5

      UpperCAmelCase  							=		model_class(__snake_case       )
      if getattr(__snake_case				,	'hf_compute_loss'				,	__snake_case       ):
        # The number of elements in the loss should be the same as the number of elements in the label
        UpperCAmelCase  							=		self._prepare_for_class(inputs_dict.copy()				,	__snake_case				,	return_labels=__snake_case       )
        UpperCAmelCase  							=		prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()				,	reverse=__snake_case       )[0]
        ]
        UpperCAmelCase  							=		added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        UpperCAmelCase  							=		self._prepare_for_class(inputs_dict.copy()				,	__snake_case				,	return_labels=__snake_case       )
        UpperCAmelCase  							=		{'''input_ids''', '''pixel_values''', '''input_features'''}
        UpperCAmelCase  							=		possible_input_names.intersection(set(__snake_case       )       ).pop()
        UpperCAmelCase  							=		prepared_for_class.pop(__snake_case       )

        UpperCAmelCase  							=		model(__snake_case				,	**__snake_case       )[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
        UpperCAmelCase  							=		self._prepare_for_class(inputs_dict.copy()				,	__snake_case				,	return_labels=__snake_case       )
        UpperCAmelCase  							=		{'''input_ids''', '''pixel_values''', '''input_features'''}
        UpperCAmelCase  							=		possible_input_names.intersection(set(__snake_case       )       ).pop()
        UpperCAmelCase  							=		prepared_for_class.pop(__snake_case       )
        if "labels" in prepared_for_class:
          UpperCAmelCase  							=		prepared_for_class['''labels'''].numpy()
          if len(labels.shape       ) > 1 and labels.shape[1] != 1:
            # labels[0] = -100
            UpperCAmelCase  							=		tf.convert_to_tensor(__snake_case       )
            UpperCAmelCase  							=		model(__snake_case				,	**__snake_case       )[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
        UpperCAmelCase  							=		self._prepare_for_class(inputs_dict.copy()				,	__snake_case				,	return_labels=__snake_case       )
        UpperCAmelCase  							=		model(__snake_case       )[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
        UpperCAmelCase  							=		self._prepare_for_class(inputs_dict.copy()				,	__snake_case				,	return_labels=__snake_case       )

        # Get keys that were added with the _prepare_for_class function
        UpperCAmelCase  							=		prepared_for_class.keys() - inputs_dict.keys()
        UpperCAmelCase  							=		inspect.signature(model.call       ).parameters
        UpperCAmelCase  							=		list(signature.keys()       )

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

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

        for index, value in sorted_tuple_index_mapping:
          UpperCAmelCase  							=		prepared_for_class[value]

        UpperCAmelCase  							=		tuple(__snake_case       )

        # Send to model
        UpperCAmelCase  							=		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       )							->			List[str]:


    """simple docstring"""
    for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      UpperCAmelCase  							=		TFMobileViTModel.from_pretrained(__snake_case       )
      self.assertIsNotNone(__snake_case       )




def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image



@require_tf
class     lowercase_		(						unittest.TestCase	):

  '''simple docstring'''





  @slow
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'       )

    UpperCAmelCase  							=		MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'       )
    UpperCAmelCase  							=		prepare_img()
    UpperCAmelCase  							=		image_processor(images=__snake_case				,	return_tensors='tf'       )

    # forward pass
    UpperCAmelCase  							=		model(**__snake_case				,	training=__snake_case       )

    # verify the logits
    UpperCAmelCase  							=		tf.TensorShape((1, 1_0_0_0)       )
    self.assertEqual(outputs.logits.shape				,	__snake_case       )

    UpperCAmelCase  							=		tf.constant([-1.9364, -1.2327, -0.4653]       )

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





  @slow
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    # `from_pt` will be removed
    UpperCAmelCase  							=		TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'       )

    UpperCAmelCase  							=		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'       )

    UpperCAmelCase  							=		prepare_img()
    UpperCAmelCase  							=		image_processor(images=__snake_case				,	return_tensors='tf'       )

    # forward pass
    UpperCAmelCase  							=		model(inputs.pixel_values				,	training=__snake_case       )
    UpperCAmelCase  							=		outputs.logits

    # verify the logits
    UpperCAmelCase  							=		tf.TensorShape((1, 2_1, 3_2, 3_2)       )
    self.assertEqual(logits.shape				,	__snake_case       )

    UpperCAmelCase  							=		tf.constant(
        [
            [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
            [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
            [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
        ]       )

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





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


