






'''simple docstring'''

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       ):

  def 						A__		(   self   )						->   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-12   )
        tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )






@require_tf
class        a_		(    unittest.TestCase			, lowerCamelCase       ):
  # setting framework_dependent_parameters needs to be gated, just like its contents' imports
  if is_tf_available():
    lowercase  	=   {
        """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 						A__		(   self   )						->   int:



        """simple docstring"""






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






        class        a_		(    tf.Module       ):

          def __init__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



                """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_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 						A__		(   self   )						->   Tuple:



        """simple docstring"""






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






        class        a_		(    tf.Module       ):

          def __init__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



                """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[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 						A__		(   self   )						->   int:



        """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       ):

                def __init__(   self   )						->   Optional[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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



                      """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 						A__		(   self   )						->   Any:



        """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 						A__		(   self   )						->   List[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_		(    lowerCamelCase       ):

          def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE   )						->   List[str]:



                """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__       ):

          def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



                """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"""   )








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
