




'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )
@require_tf
class   __A :



   """simple docstring"""







   A_												=				XGLMConfig
   A_												=				{}
   A_												=				'''gelu'''

   def __init__(      self				, _lowerCamelCase				, _lowerCamelCase=1_4				, _lowerCamelCase=7				, _lowerCamelCase=True				, _lowerCamelCase=True				, _lowerCamelCase=True				, _lowerCamelCase=9_9				, _lowerCamelCase=3_2				, _lowerCamelCase=2				, _lowerCamelCase=4				, _lowerCamelCase=3_7				, _lowerCamelCase="gelu"				, _lowerCamelCase=0.1				, _lowerCamelCase=0.1				, _lowerCamelCase=5_1_2				, _lowerCamelCase=0.0_2				, )->       Optional[int]:
      lowercase__       							=							parent
      lowercase__       							=							batch_size
      lowercase__       							=							seq_length
      lowercase__       							=							is_training
      lowercase__       							=							use_input_mask
      lowercase__       							=							use_labels
      lowercase__       							=							vocab_size
      lowercase__       							=							d_model
      lowercase__       							=							num_hidden_layers
      lowercase__       							=							num_attention_heads
      lowercase__       							=							ffn_dim
      lowercase__       							=							activation_function
      lowercase__       							=							activation_dropout
      lowercase__       							=							attention_dropout
      lowercase__       							=							max_position_embeddings
      lowercase__       							=							initializer_range
      lowercase__       							=							None
      lowercase__       							=							0
      lowercase__       							=							2
      lowercase__       							=							1

   def snake_case_(      self						)->       Optional[int]:
      return XGLMConfig.from_pretrained('''facebook/xglm-564M'''						)

   def snake_case_(      self						)->       Tuple:
      lowercase__       							=							tf.clip_by_value(
          ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size						)				, clip_value_min=0				, clip_value_max=3						)

      lowercase__       							=							None
      if self.use_input_mask:
         lowercase__       							=							random_attention_mask([self.batch_size, self.seq_length]						)

      lowercase__       							=							self.get_config()

      lowercase__       							=							floats_tensor([self.num_hidden_layers, self.num_attention_heads]				, 2						)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )

   def snake_case_(      self						)->       Any:
      return XGLMConfig(
          vocab_size=self.vocab_size				, d_model=self.hidden_size				, num_layers=self.num_hidden_layers				, attention_heads=self.num_attention_heads				, ffn_dim=self.ffn_dim				, activation_function=self.activation_function				, activation_dropout=self.activation_dropout				, attention_dropout=self.attention_dropout				, max_position_embeddings=self.max_position_embeddings				, initializer_range=self.initializer_range				, use_cache=__lowercase				, bos_token_id=self.bos_token_id				, eos_token_id=self.eos_token_id				, pad_token_id=self.pad_token_id				, return_dict=__lowercase				, )

   def snake_case_(      self						)->       Optional[int]:
      lowercase__       							=							self.prepare_config_and_inputs()

      (
          lowercase__
      )       							=							config_and_inputs

      lowercase__       							=							{
          'input_ids': input_ids,
          'head_mask': head_mask,
      }

      return config, inputs_dict



@require_tf
class   __A (							snake_case_     ,   snake_case_     ,   unittest.TestCase  ):



   """simple docstring"""







   A_												=				(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   A_												=				(TFXGLMForCausalLM,) if is_tf_available() else ()
   A_												=				(
       {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   A_												=				False
   A_												=				False
   A_												=				False

   def snake_case_(      self						)->       Optional[int]:
      lowercase__       							=							TFXGLMModelTester(self						)
      lowercase__       							=							ConfigTester(self				, config_class=__lowercase				, n_embd=3_7						)

   def snake_case_(      self						)->       Optional[Any]:
      self.config_tester.run_common_tests()

   @slow
   def snake_case_(      self						)->       Tuple:







      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__       							=							TFXGLMModel.from_pretrained(__lowercase						)
         self.assertIsNotNone(__lowercase						)

   @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''						)
   def snake_case_(      self						)->       Dict:
      super().test_resize_token_embeddings()



@require_tf
class   __A (							unittest.TestCase  ):



   """simple docstring"""







   @slow
   def snake_case_(      self				, _lowerCamelCase=True						)->       Any:
      lowercase__       							=							TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
      lowercase__       							=							tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]				, dtype=tf.intaa						)  # The dog
      # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
      # fmt: off
      lowercase__       							=							[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
      # fmt: on
      lowercase__       							=							model.generate(__lowercase				, do_sample=__lowercase				, num_beams=1						)
      if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()				, __lowercase						)

   @slow
   def snake_case_(      self						)->       Optional[Any]:
      lowercase__       							=							XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)
      lowercase__       							=							TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)

      tf.random.set_seed(0						)
      lowercase__       							=							tokenizer('''Today is a nice day and'''				, return_tensors='''tf'''						)
      lowercase__       							=							tokenized.input_ids
      # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
      with tf.device(''':/CPU:0'''						):
         lowercase__       							=							model.generate(__lowercase				, do_sample=__lowercase				, seed=[7, 0]						)
      lowercase__       							=							tokenizer.decode(output_ids[0]				, skip_special_tokens=__lowercase						)

      lowercase__       							=							(
          'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
      )
      self.assertEqual(__lowercase				, __lowercase						)

   @slow
   def snake_case_(      self						)->       int:
      lowercase__       							=							TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
      lowercase__       							=							XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)

      lowercase__       							=							'left'

      # use different length sentences to test batching
      lowercase__       							=							[
          'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
          'left-padding, such as in batched generation. The output for the sequence below should be the same '
          'regardless of whether left padding is applied or not. When',
          'Hello, my dog is a little',
      ]

      lowercase__       							=							tokenizer(__lowercase				, return_tensors='''tf'''				, padding=__lowercase						)
      lowercase__       							=							inputs['input_ids']

      lowercase__       							=							model.generate(input_ids=__lowercase				, attention_mask=inputs['''attention_mask''']				, max_new_tokens=1_2						)

      lowercase__       							=							tokenizer(sentences[0]				, return_tensors='''tf'''						).input_ids
      lowercase__       							=							model.generate(input_ids=__lowercase				, max_new_tokens=1_2						)

      lowercase__       							=							tokenizer(sentences[1]				, return_tensors='''tf'''						).input_ids
      lowercase__       							=							model.generate(input_ids=__lowercase				, max_new_tokens=1_2						)

      lowercase__       							=							tokenizer.batch_decode(__lowercase				, skip_special_tokens=__lowercase						)
      lowercase__       							=							tokenizer.decode(output_non_padded[0]				, skip_special_tokens=__lowercase						)
      lowercase__       							=							tokenizer.decode(output_padded[0]				, skip_special_tokens=__lowercase						)

      lowercase__       							=							[
          'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
          'left-padding, such as in batched generation. The output for the sequence below should be the same '
          'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
          'a single',
          'Hello, my dog is a little bit of a shy one, but he is very friendly',
      ]
      self.assertListEqual(__lowercase				, __lowercase						)
      self.assertListEqual(__lowercase				, [non_padded_sentence, padded_sentence]						)








'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






