






from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           AutoTokenizer,
           TFAutoModelForCausalLM,
           TFAutoModelForSeqaSeqLM,
           TFAutoModelForSpeechSeqaSeq,
           TFAutoModelForVisionaSeq,
           TFBartForConditionalGeneration,
           TFLogitsProcessorList,
           TFMinLengthLogitsProcessor,
           tf_top_k_top_p_filtering,
       )

if is_tensorflow_text_available():
       import tensorflow_text as text





@require_tf
class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  tf.convert_to_tensor(
               [
                   [
                       8.2_2_2_0_9_9_1,  # 3rd highest value; idx. 0
                       -0.5_6_2_0_0_4_4,
                       5.2_3_2_2_9_7_5_2,
                       4.0_3_8_6_3_9_3,
                       -6.8_7_9_8_3_7_8,
                       -0.5_4_7_8_5_8_0_2,
                       -3.2_0_1_2_1_5_3,
                       2.9_2_7_7_7_1_7_6,
                       1.8_8_1_7_1_9_5_3,
                       7.3_5_3_4_1_2_7_6,  # 5th highest value; idx. 9
                       8.4_3_2_0_7_8_3_3,  # 2nd highest value; idx. 10
                       -9.8_5_7_1_1_8_3_6,
                       -5.9_6_2_0_9_2_3_6,
                       -1.1_3_0_3_9_1_6_1,
                       -7.1_1_1_5_2_9_4,
                       -0.8_3_6_9_6_3_3,
                       -5.3_1_8_6_4_0_8,
                       7.0_6_4_2_7_4_0_7,
                       0.8_1_3_6_9_3_4_4,
                       -0.8_2_0_2_3_8_1_7,
                       -5.9_1_7_9_7_9_6,
                       0.5_8_8_1_3_4_4_3,
                       -6.9_9_7_7_8_4_3_8,
                       4.7_1_5_5_1_1_8_9,
                       -0.1_8_7_7_1_6_3_7,
                       7.4_4_0_2_0_7_5_9,  # 4th highest value; idx. 25
                       9.3_8_4_5_0_9_8_7,  # 1st highest value; idx. 26
                       2.1_2_6_6_2_9_4_1,
                       -9.3_2_5_6_2_0_3_8,
                       2.3_5_6_5_2_5_2_2,
                   ],  # cummulative prob of 5 highest values <= 0.6
                   [
                       0.5_8_4_2_5_5_1_8,
                       4.5_3_1_3_9_2_3_8,
                       -5.5_7_5_1_0_4_6_4,
                       -6.2_8_0_3_0_6_9_9,
                       -7.1_9_5_2_9_5_0_3,
                       -4.0_2_1_2_2_5_5_1,
                       1.3_9_3_3_7_0_3_7,
                       -6.0_6_7_0_7_0_5_7,
                       1.5_9_4_8_0_5_1_7,
                       -9.6_4_3_1_1_9,
                       0.0_3_9_0_7_7_9_9,
                       0.6_7_2_3_1_7_6_2,
                       -8.8_8_2_0_6_7_2_6,
                       6.2_7_1_1_5_9_2_2,  # 4th highest value; idx. 13
                       2.2_8_5_2_0_7_2_3,
                       4.8_2_7_6_7_5_0_6,
                       4.3_0_4_2_1_3_6_8,
                       8.8_2_7_5_3_1_3,  # 2nd highest value; idx. 17
                       5.4_4_0_2_9_9_5_8,  # 5th highest value; idx. 18
                       -4.4_7_3_5_7_9_4,
                       7.3_8_5_7_9_5_3_6,  # 3rd highest value; idx. 20
                       -2.9_1_0_5_1_6_6_3,
                       2.6_1_9_4_6_0_7_7,
                       -2.5_6_7_4_7_6_2,
                       -9.4_8_9_5_9_3_0_2,
                       -4.0_2_9_2_2_6_4_5,
                       -1.3_5_4_1_6_9_1_8,
                       9.6_7_7_0_2_3_2_3,  # 1st highest value; idx. 27
                       -5.8_9_4_7_8_5_5_3,
                       1.8_5_3_7_0_4_6_7,
                   ],  # cummulative prob of 5 highest values <= 0.6
               ]   ,						dtype=tf.floataa   ,						)

           UpperCamelCase					       =  tf.convert_to_tensor(
               [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]   ,						dtype=tf.intaa   ,						)  # expected non filtered idx as noted above

           UpperCamelCase					       =  tf.convert_to_tensor(
               [8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3]   ,						dtype=tf.floataa   ,						)  # expected non filtered values as noted above

           UpperCamelCase					       =  tf_top_k_top_p_filtering(_SCREAMING_SNAKE_CASE   ,						top_k=10   ,						top_p=0.6   ,						min_tokens_to_keep=4 )

           UpperCamelCase					       =  output[output != -float('inf' )]
           UpperCamelCase					       =  tf.cast(
               tf.where(tf.not_equal(_SCREAMING_SNAKE_CASE   ,						tf.constant(-float('inf' )   ,						dtype=tf.floataa ) ) )   ,						dtype=tf.intaa   ,						)

           tf.debugging.assert_near(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						rtol=1E-1_2 )
           tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )





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

     '''simple docstring'''






     if is_tf_available():
          snake_case__					=					{
              """AutoModelForCausalLM""": TFAutoModelForCausalLM,
              """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
              """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
              """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
              """LogitsProcessorList""": TFLogitsProcessorList,
              """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
              """create_tensor_fn""": tf.convert_to_tensor,
              """floats_tensor""": floats_tensor,
              """return_tensors""": """tf""",
          }







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
           UpperCamelCase					       =  2
           UpperCamelCase					       =  2





           class 					A__     (     tf.Module ):

                '''simple docstring'''






                def __init__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Tuple ):






                      """simple docstring"""




                      super(_SCREAMING_SNAKE_CASE   ,						self ).__init__()
                      UpperCamelCase					       =  model







                @tf.function(
                    input_signature=(
                        tf.TensorSpec((None, input_length)   ,						tf.intaa   ,						name='input_ids' ),
                        tf.TensorSpec((None, input_length)   ,						tf.intaa   ,						name='attention_mask' ),
                    )   ,						jit_compile=_SCREAMING_SNAKE_CASE   ,						)
                def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






                      """simple docstring"""




                      UpperCamelCase					       =  self.model.generate(
                          input_ids=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						max_new_tokens=_SCREAMING_SNAKE_CASE   ,						return_dict_in_generate=_SCREAMING_SNAKE_CASE   ,						)
                      return {"sequences": outputs["sequences"]}

           UpperCamelCase					       =  [[2, 0], [102, 103]]
           UpperCamelCase					       =  [[1, 0], [1, 1]]
           UpperCamelCase					       =  DummyModel(model=_SCREAMING_SNAKE_CASE )
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tf.saved_model.save(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						signatures={'serving_default': dummy_model.serving} )
                 UpperCamelCase					       =  tf.saved_model.load(_SCREAMING_SNAKE_CASE ).signatures['serving_default']
                 for batch_size in range(1   ,						len(_SCREAMING_SNAKE_CASE ) + 1 ):
                       UpperCamelCase					       =  {
                           'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
                           'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
                       }
                       UpperCamelCase					       =  serving_func(**_SCREAMING_SNAKE_CASE )['sequences']
                       UpperCamelCase					       =  test_model.generate(**_SCREAMING_SNAKE_CASE   ,						max_new_tokens=_SCREAMING_SNAKE_CASE )
                       tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     @slow
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
           UpperCamelCase					       =  1
           UpperCamelCase					       =  2





           class 					A__     (     tf.Module ):

                '''simple docstring'''






                def __init__( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    Any ):






                      """simple docstring"""




                      super(_SCREAMING_SNAKE_CASE   ,						self ).__init__()
                      UpperCamelCase					       =  model







                @tf.function(
                    input_signature=(
                        tf.TensorSpec((batch_size, None)   ,						tf.intaa   ,						name='input_ids' ),
                        tf.TensorSpec((batch_size, None)   ,						tf.intaa   ,						name='attention_mask' ),
                    )   ,						jit_compile=_SCREAMING_SNAKE_CASE   ,						)
                def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






                      """simple docstring"""




                      UpperCamelCase					       =  self.model.generate(
                          input_ids=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						max_new_tokens=_SCREAMING_SNAKE_CASE   ,						return_dict_in_generate=_SCREAMING_SNAKE_CASE   ,						)
                      return {"sequences": outputs["sequences"]}

           UpperCamelCase					       =  [[2], [102, 103]]
           UpperCamelCase					       =  [[1], [1, 1]]
           UpperCamelCase					       =  DummyModel(model=_SCREAMING_SNAKE_CASE )
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tf.saved_model.save(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						signatures={'serving_default': dummy_model.serving} )
                 UpperCamelCase					       =  tf.saved_model.load(_SCREAMING_SNAKE_CASE ).signatures['serving_default']
                 for input_row in range(len(_SCREAMING_SNAKE_CASE ) ):
                       UpperCamelCase					       =  {
                           'input_ids': tf.constant([dummy_input_ids[input_row]] ),
                           'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
                       }
                       UpperCamelCase					       =  serving_func(**_SCREAMING_SNAKE_CASE )['sequences']
                       UpperCamelCase					       =  test_model.generate(**_SCREAMING_SNAKE_CASE   ,						max_new_tokens=_SCREAMING_SNAKE_CASE )
                       tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )







     @slow
     @require_tensorflow_text
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           with tempfile.TemporaryDirectory() as tmp_dir:
                 # file needed to load the TF tokenizer
                 hf_hub_download(repo_id='google/flan-t5-small'   ,						filename='spiece.model'   ,						local_dir=_SCREAMING_SNAKE_CASE )





                 class 					A__     (     tf.keras.layers.Layer ):

                      '''simple docstring'''






                      def __init__( self      :    Union[str, Any] ):






                            """simple docstring"""




                            super().__init__()
                            UpperCamelCase					       =  text.SentencepieceTokenizer(
                                model=tf.io.gfile.GFile(os.path.join(_SCREAMING_SNAKE_CASE   ,						'spiece.model' )   ,						'rb' ).read() )
                            UpperCamelCase					       =  TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )







                      def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						*_SCREAMING_SNAKE_CASE      :    Any   ,						**_SCREAMING_SNAKE_CASE      :    Tuple ):






                            """simple docstring"""




                            UpperCamelCase					       =  self.tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
                            UpperCamelCase       ,  UpperCamelCase					       =  text.pad_model_inputs(
                                _SCREAMING_SNAKE_CASE   ,						max_seq_length=64   ,						pad_value=self.model.config.pad_token_id )
                            UpperCamelCase					       =  self.model.generate(input_ids=_SCREAMING_SNAKE_CASE   ,						attention_mask=_SCREAMING_SNAKE_CASE )
                            return self.tokenizer.detokenize(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  CompleteSentenceTransformer()
                 UpperCamelCase					       =  tf.keras.layers.Input(shape=(1,)   ,						dtype=tf.string   ,						name='inputs' )
                 UpperCamelCase					       =  complete_model(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tf.keras.Model(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                 keras_model.save(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  {
               'do_sample': True,
               'num_beams': 1,
               'top_p': 0.7,
               'top_k': 10,
               'temperature': 0.7,
           }
           UpperCamelCase					       =  14

           UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
           UpperCamelCase					       =  'Hello, my dog is cute and'
           UpperCamelCase					       =  tokenizer(_SCREAMING_SNAKE_CASE   ,						return_tensors='tf' )
           UpperCamelCase					       =  TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )

           UpperCamelCase					       =  638
           # forces the generation to happen on CPU, to avoid GPU-related quirks
           with tf.device(':/CPU:0' ):
                 tf.random.set_seed(0 )
                 UpperCamelCase					       =  model.generate(**_SCREAMING_SNAKE_CASE   ,						eos_token_id=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
           self.assertTrue(expectation == len(generated_tokens[0] ) )

           UpperCamelCase					       =  [638, 198]
           with tf.device(':/CPU:0' ):
                 tf.random.set_seed(0 )
                 UpperCamelCase					       =  model.generate(**_SCREAMING_SNAKE_CASE   ,						eos_token_id=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
           self.assertTrue(expectation == len(generated_tokens[0] ) )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
           UpperCamelCase					       =  'Hugging Face is a technology company based in New York and Paris.'
           UpperCamelCase					       =  bart_tokenizer(_SCREAMING_SNAKE_CASE   ,						return_tensors='tf' ).input_ids
           UpperCamelCase					       =  TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
           UpperCamelCase					       =  bart_model.generate(_SCREAMING_SNAKE_CASE ).numpy()





           class 					A__     (     __snake_case ):

                '''simple docstring'''






                def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






                      """simple docstring"""




                      return super().call(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
           UpperCamelCase					       =  bart_model.generate(_SCREAMING_SNAKE_CASE   ,						foo='bar' ).numpy()
           self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ) )





           class 					A__     (     bart_model.model.encoder.__class__ ):

                '''simple docstring'''






                def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						**_SCREAMING_SNAKE_CASE      :    Dict ):






                      """simple docstring"""




                      return super().call(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  FakeEncoder(bart_model.config   ,						bart_model.model.shared )
           UpperCamelCase					       =  fake_encoder

           # Normal generation still works (the output will be different because the encoder weights are different)
           UpperCamelCase					       =  bart_model.generate(_SCREAMING_SNAKE_CASE ).numpy()
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
                 # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
                 bart_model.generate(_SCREAMING_SNAKE_CASE   ,						foo='bar' )











__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



