




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    =			tf.convert_to_tensor(
							    [
							        [
							            8.2_220_991,  # 3rd highest value; idx. 0
							            -0.5_620_044,
							            5.23_229_752,
							            4.0_386_393,
							            -6.8_798_378,
							            -0.54_785_802,
							            -3.2_012_153,
							            2.92_777_176,
							            1.88_171_953,
							            7.35_341_276,  # 5th highest value; idx. 9
							            8.43_207_833,  # 2nd highest value; idx. 10
							            -9.85_711_836,
							            -5.96_209_236,
							            -1.13_039_161,
							            -7.1_115_294,
							            -0.8_369_633,
							            -5.3_186_408,
							            7.06_427_407,
							            0.81_369_344,
							            -0.82_023_817,
							            -5.9_179_796,
							            0.58_813_443,
							            -6.99_778_438,
							            4.71_551_189,
							            -0.18_771_637,
							            7.44_020_759,  # 4th highest value; idx. 25
							            9.38_450_987,  # 1st highest value; idx. 26
							            2.12_662_941,
							            -9.32_562_038,
							            2.35_652_522,
							        ],  # cummulative prob of 5 highest values <= 0.6
							        [
							            0.58_425_518,
							            4.53_139_238,
							            -5.57_510_464,
							            -6.28_030_699,
							            -7.19_529_503,
							            -4.02_122_551,
							            1.39_337_037,
							            -6.06_707_057,
							            1.59_480_517,
							            -9.643_119,
							            0.03_907_799,
							            0.67_231_762,
							            -8.88_206_726,
							            6.27_115_922,  # 4th highest value; idx. 13
							            2.28_520_723,
							            4.82_767_506,
							            4.30_421_368,
							            8.8_275_313,  # 2nd highest value; idx. 17
							            5.44_029_958,  # 5th highest value; idx. 18
							            -4.4_735_794,
							            7.38_579_536,  # 3rd highest value; idx. 20
							            -2.91_051_663,
							            2.61_946_077,
							            -2.5_674_762,
							            -9.48_959_302,
							            -4.02_922_645,
							            -1.35_416_918,
							            9.67_702_323,  # 1st highest value; idx. 27
							            -5.89_478_553,
							            1.85_370_467,
							        ],  # cummulative prob of 5 highest values <= 0.6
							    ]					,      dtype=tf.floataa					,      )

							a    =			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

							a    =			tf.convert_to_tensor(
							    [8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023]					,      dtype=tf.floataa					,      )  # expected non filtered values as noted above

							a    =			tf_top_k_top_p_filtering(__lowerCamelCase					,      top_k=10					,      top_p=0.6					,      min_tokens_to_keep=4							)

							a    =			output[output != -float("inf"							)]
							a    =			tf.cast(
							    tf.where(tf.not_equal(__lowerCamelCase					,      tf.constant(-float("inf"							)					,      dtype=tf.floataa							)							)							)					,      dtype=tf.intaa					,      )

							tf.debugging.assert_near(__lowerCamelCase					,      __lowerCamelCase					,      rtol=1e-12							)
							tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)




@require_tf
class     snake_case__							(unittest.TestCase    ,       _UpperCamelCase   ):
				"""simple docstring"""



				if is_tf_available():
								SCREAMING_SNAKE_CASE_       :							Any							 =					{
								    """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 							__UpperCAmelCase							(      self				:     Any							)					->     List[str]:
							# TF-only test: tf.saved_model export
							a    =			TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
							a    =			2
							a    =			2




							class     snake_case__							(tf.Module   ):
											"""simple docstring"""



											def __init__(      self				:     str					,      __lowerCamelCase				:     int							)					->     List[str]:
														super(__lowerCamelCase					,      self							).__init__()
														a    =			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=__lowerCamelCase					,      )
											def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Any							)					->     Optional[Any]:
														a    =			self.model.generate(
														    input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      max_new_tokens=__lowerCamelCase					,      return_dict_in_generate=__lowerCamelCase					,      )
														return {"sequences": outputs["sequences"]}

							a    =			[[2, 0], [1_02, 1_03]]
							a    =			[[1, 0], [1, 1]]
							a    =			DummyModel(model=__lowerCamelCase							)
							with tempfile.TemporaryDirectory() as tmp_dir:
										tf.saved_model.save(__lowerCamelCase					,      __lowerCamelCase					,      signatures={"serving_default": dummy_model.serving}							)
										a    =			tf.saved_model.load(__lowerCamelCase							).signatures["serving_default"]
										for batch_size in range(1					,      len(__lowerCamelCase							) + 1							):
													a    =			{
													    "input_ids": tf.constant(dummy_input_ids[:batch_size]							),
													    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]							),
													}
													a    =			serving_func(**__lowerCamelCase							)["sequences"]
													a    =			test_model.generate(**__lowerCamelCase					,      max_new_tokens=__lowerCamelCase							)
													tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[Any]:
							# TF-only test: tf.saved_model export
							a    =			TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
							a    =			1
							a    =			2




							class     snake_case__							(tf.Module   ):
											"""simple docstring"""



											def __init__(      self				:     Union[str, Any]					,      __lowerCamelCase				:     int							)					->     Any:
														super(__lowerCamelCase					,      self							).__init__()
														a    =			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=__lowerCamelCase					,      )
											def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple							)					->     str:
														a    =			self.model.generate(
														    input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      max_new_tokens=__lowerCamelCase					,      return_dict_in_generate=__lowerCamelCase					,      )
														return {"sequences": outputs["sequences"]}

							a    =			[[2], [1_02, 1_03]]
							a    =			[[1], [1, 1]]
							a    =			DummyModel(model=__lowerCamelCase							)
							with tempfile.TemporaryDirectory() as tmp_dir:
										tf.saved_model.save(__lowerCamelCase					,      __lowerCamelCase					,      signatures={"serving_default": dummy_model.serving}							)
										a    =			tf.saved_model.load(__lowerCamelCase							).signatures["serving_default"]
										for input_row in range(len(__lowerCamelCase							)							):
													a    =			{
													    "input_ids": tf.constant([dummy_input_ids[input_row]]							),
													    "attention_mask": tf.constant([dummy_attention_masks[input_row]]							),
													}
													a    =			serving_func(**__lowerCamelCase							)["sequences"]
													a    =			test_model.generate(**__lowerCamelCase					,      max_new_tokens=__lowerCamelCase							)
													tf.debugging.assert_equal(__lowerCamelCase					,      __lowerCamelCase							)





				@slow
				@require_tensorflow_text
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Union[str, Any]:
							# TF-only test: tf.saved_model export
							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=__lowerCamelCase							)




										class     snake_case__							(tf.keras.layers.Layer   ):
														"""simple docstring"""



														def __init__(      self				:     Dict							)					->     List[str]:
																	super().__init__()
																	a    =			text.SentencepieceTokenizer(
																	    model=tf.io.gfile.GFile(os.path.join(__lowerCamelCase					,      "spiece.model"							)					,      "rb"							).read()							)
																	a    =			TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5"							)





														def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      *__lowerCamelCase				:     Optional[Any]					,      **__lowerCamelCase				:     Dict							)					->     Optional[int]:
																	a    =			self.tokenizer.tokenize(__lowerCamelCase							)
																	a    ,		a    =			text.pad_model_inputs(
																	    __lowerCamelCase					,      max_seq_length=64					,      pad_value=self.model.config.pad_token_id							)
																	a    =			self.model.generate(input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase							)
																	return self.tokenizer.detokenize(__lowerCamelCase							)

										a    =			CompleteSentenceTransformer()
										a    =			tf.keras.layers.Input(shape=(1,)					,      dtype=tf.string					,      name="inputs"							)
										a    =			complete_model(__lowerCamelCase							)
										a    =			tf.keras.Model(__lowerCamelCase					,      __lowerCamelCase							)
										keras_model.save(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							# Has PT equivalent: this test relies on random sampling
							a    =			{
							    "do_sample": True,
							    "num_beams": 1,
							    "top_p": 0.7,
							    "top_k": 10,
							    "temperature": 0.7,
							}
							a    =			14

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

							a    =			6_38
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"							):
										tf.random.set_seed(0							)
										a    =			model.generate(**__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)
							self.assertTrue(expectation == len(generated_tokens[0]							)							)

							a    =			[6_38, 1_98]
							with tf.device(":/CPU:0"							):
										tf.random.set_seed(0							)
										a    =			model.generate(**__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)
							self.assertTrue(expectation == len(generated_tokens[0]							)							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							# Has PT equivalent: ample use of framework-specific code
							a    =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart"							)
							a    =			"Hugging Face is a technology company based in New York and Paris."
							a    =			bart_tokenizer(__lowerCamelCase					,      return_tensors="tf"							).input_ids
							a    =			TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"							)
							a    =			bart_model.generate(__lowerCamelCase							).numpy()




							class     snake_case__							(_UpperCamelCase   ):
											"""simple docstring"""



											def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Dict=None					,      **__lowerCamelCase				:     Any							)					->     List[str]:
														return super().call(__lowerCamelCase					,      **__lowerCamelCase							)

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




							class     snake_case__							(bart_model.model.encoder.__class__   ):
											"""simple docstring"""



											def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     int					,      **__lowerCamelCase				:     List[str]							)					->     Any:
														return super().call(__lowerCamelCase					,      **__lowerCamelCase							)

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

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












from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






