[tests] remove TF tests (uses of require_tf) (#38944)
* remove uses of require_tf * remove redundant import guards * this class has no tests * nits * del tf rng comment
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@@ -53,7 +53,6 @@ from transformers.testing_utils import (
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get_tests_dir,
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require_jinja,
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require_read_token,
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require_tf,
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require_tokenizers,
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require_torch,
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run_test_in_subprocess,
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@@ -3106,40 +3105,6 @@ class TokenizerTesterMixin:
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# model(**encoded_sequence_fast)
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# model(**batch_encoded_sequence_fast)
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@require_tf
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@slow
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def test_tf_encode_plus_sent_to_model(self):
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from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING
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MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
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self.skipTest(f"{tokenizer.__class__.__name__} is not in the MODEL_TOKENIZER_MAPPING")
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config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
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config = config_class()
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if config.is_encoder_decoder or config.pad_token_id is None:
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self.skipTest(reason="Model is not an encoder-decoder model or has no set pad token id")
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model = model_class(config)
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# Make sure the model contains at least the full vocabulary size in its embedding matrix
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self.assertGreaterEqual(model.config.vocab_size, len(tokenizer))
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# Build sequence
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first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
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sequence = " ".join(first_ten_tokens)
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encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="tf")
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batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")
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# This should not fail
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model(encoded_sequence)
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model(batch_encoded_sequence)
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# TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
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@require_torch
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@slow
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