"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase__			=    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}


@is_pipeline_test
class 							a					(		unittest.TestCase ):
    _snake_case  :	str  				=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    _snake_case  :	Tuple  				=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        _snake_case  :	Optional[int]  				=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        _snake_case  :	Optional[int]  				=				{
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any]       ):
      _UpperCAmelCase    			=   ZeroShotClassificationPipeline(
          model=__SCREAMING_SNAKE_CASE ,					tokenizer=__SCREAMING_SNAKE_CASE ,					candidate_labels=["""polics""", """health"""]       )
      return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels="""politics"""       )
      self.assertEqual(__SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       )]}       )

      # No kwarg
      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					["""politics"""]       )
      self.assertEqual(__SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       )]}       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels=["""politics"""]       )
      self.assertEqual(__SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       )]}       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels="""politics, public health"""       )
      self.assertEqual(
          __SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )]}       )
      self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       ) ,					1.0       )

      _UpperCAmelCase    			=   classifier("""Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health"""]       )
      self.assertEqual(
          __SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )]}       )
      self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       ) ,					1.0       )

      _UpperCAmelCase    			=   classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template="""This text is about {}"""       )
      self.assertEqual(__SCREAMING_SNAKE_CASE ,					{"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       )]}       )

      # https://github.com/huggingface/transformers/issues/13846
      _UpperCAmelCase    			=   classifier(["""I am happy"""] ,					["""positive""", """negative"""]       )
      self.assertEqual(
          __SCREAMING_SNAKE_CASE ,					[
              {"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )]}
              for i in range(1       )
          ] ,					)
      _UpperCAmelCase    			=   classifier(["""I am happy""", """I am sad"""] ,					["""positive""", """negative"""]       )
      self.assertEqual(
          __SCREAMING_SNAKE_CASE ,					[
              {"""sequence""": ANY(__SCREAMING_SNAKE_CASE       ), """labels""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )], """scores""": [ANY(__SCREAMING_SNAKE_CASE       ), ANY(__SCREAMING_SNAKE_CASE       )]}
              for i in range(2       )
          ] ,					)

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier("""""" ,					candidate_labels="""politics"""       )

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier(__SCREAMING_SNAKE_CASE ,					candidate_labels="""politics"""       )

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier("""Who are you voting for in 2020?""" ,					candidate_labels=""""""       )

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier("""Who are you voting for in 2020?""" ,					candidate_labels=__SCREAMING_SNAKE_CASE       )

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier(
            """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template="""Not formatting template""" ,					)

      with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
        classifier(
            """Who are you voting for in 2020?""" ,					candidate_labels="""politics""" ,					hypothesis_template=__SCREAMING_SNAKE_CASE ,					)

      self.run_entailment_id(__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Pipeline       ):
      _UpperCAmelCase    			=   zero_shot_classifier.model.config
      _UpperCAmelCase    			=   config.labelaid
      _UpperCAmelCase    			=   zero_shot_classifier.entailment_id

      _UpperCAmelCase    			=   {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
      self.assertEqual(zero_shot_classifier.entailment_id ,					-1       )

      _UpperCAmelCase    			=   {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
      self.assertEqual(zero_shot_classifier.entailment_id ,					0       )

      _UpperCAmelCase    			=   {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
      self.assertEqual(zero_shot_classifier.entailment_id ,					0       )

      _UpperCAmelCase    			=   {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
      self.assertEqual(zero_shot_classifier.entailment_id ,					2       )

      _UpperCAmelCase    			=   original_labelaid
      self.assertEqual(__SCREAMING_SNAKE_CASE ,					zero_shot_classifier.entailment_id       )


    @require_torch
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""pt""" ,					)
      # There was a regression in 4.10 for this
      # Adding a test so we don't make the mistake again.
      # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
      zero_shot_classifier(
          """Who are you voting for in 2020?""" * 100 ,					candidate_labels=["""politics""", """public health""", """science"""]       )


    @require_torch
    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""pt""" ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""science""", """public health""", """politics"""],
              """scores""": [0.333, 0.333, 0.333],
          } ,					)


    @require_tf
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   pipeline(
          """zero-shot-classification""" ,					model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,					framework="""tf""" ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""science""", """public health""", """politics"""],
              """scores""": [0.333, 0.333, 0.333],
          } ,					)


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   pipeline("""zero-shot-classification""" ,					model="""roberta-large-mnli""" ,					framework="""pt"""       )
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""politics""", """public health""", """science"""],
              """scores""": [0.976, 0.015, 0.009],
          } ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
          """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
          """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
          """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
          """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
          """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
          """ English-to-German translation task, improving over the existing best results, including ensembles by"""
          """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
          """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
          """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
          """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
          """ large and limited training data.""" ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,					multi_label=__SCREAMING_SNAKE_CASE ,					)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": (
                  """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                  """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                  """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                  """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                  """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                  """ superior in quality while being more parallelizable and requiring significantly less time to"""
                  """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                  """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                  """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                  """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                  """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                  """ other tasks by applying it successfully to English constituency parsing both with large and"""
                  """ limited training data."""
              ),
              """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
              """scores""": [0.817, 0.713, 0.018, 0.018],
          } ,					)


    @slow
    @require_tf
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   pipeline("""zero-shot-classification""" ,					model="""roberta-large-mnli""" ,					framework="""tf"""       )
      _UpperCAmelCase    			=   zero_shot_classifier(
          """Who are you voting for in 2020?""" ,					candidate_labels=["""politics""", """public health""", """science"""]       )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": """Who are you voting for in 2020?""",
              """labels""": ["""politics""", """public health""", """science"""],
              """scores""": [0.976, 0.015, 0.009],
          } ,					)
      _UpperCAmelCase    			=   zero_shot_classifier(
          """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
          """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
          """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
          """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
          """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
          """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
          """ English-to-German translation task, improving over the existing best results, including ensembles by"""
          """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
          """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
          """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
          """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
          """ large and limited training data.""" ,					candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,					multi_label=__SCREAMING_SNAKE_CASE ,					)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE       ) ,					{
              """sequence""": (
                  """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                  """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                  """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                  """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                  """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                  """ superior in quality while being more parallelizable and requiring significantly less time to"""
                  """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                  """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                  """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                  """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                  """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                  """ other tasks by applying it successfully to English constituency parsing both with large and"""
                  """ limited training data."""
              ),
              """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
              """scores""": [0.817, 0.713, 0.018, 0.018],
          } ,					)








'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




