

"""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  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   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     ,					)

						SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor(
						    [[0, 0], [0, 9], [0, 1_0], [0, 2_5], [0, 2_6], [1, 1_3], [1, 1_7], [1, 1_8], [1, 2_0], [1, 2_7]]     ,					dtype=tf.intaa     ,					)  # expected non filtered idx as noted above

						SCREAMING_SNAKE_CASE__										=   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

						SCREAMING_SNAKE_CASE__										=   tf_top_k_top_p_filtering(__UpperCAmelCase     ,					top_k=1_0     ,					top_p=0.6     ,					min_tokens_to_keep=4							)

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

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





@require_tf
class  lowerCamelCase    (unittest.TestCase      ,A__      ):
			# setting framework_dependent_parameters needs to be gated, just like its contents' imports
			if is_tf_available():
						lowerCamelCase__ :		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        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						# TF-only test: tf.saved_model export
						SCREAMING_SNAKE_CASE__										=   TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
						SCREAMING_SNAKE_CASE__										=   2
						SCREAMING_SNAKE_CASE__										=   2





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    int     ,					__UpperCAmelCase					:    Any							) ->     Tuple:
												super(__UpperCAmelCase     ,					self							).__init__()
												SCREAMING_SNAKE_CASE__										=   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=__UpperCAmelCase     ,					)
									def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Dict							) ->     Dict:
												SCREAMING_SNAKE_CASE__										=   self.model.generate(
												    input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					max_new_tokens=__UpperCAmelCase     ,					return_dict_in_generate=__UpperCAmelCase     ,					)
												return {"sequences": outputs["sequences"]}
						SCREAMING_SNAKE_CASE__										=   [[2, 0], [1_0_2, 1_0_3]]
						SCREAMING_SNAKE_CASE__										=   [[1, 0], [1, 1]]
						SCREAMING_SNAKE_CASE__										=   DummyModel(model=__UpperCAmelCase							)
						with tempfile.TemporaryDirectory() as tmp_dir:
									tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": dummy_model.serving}							)
									SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							).signatures["""serving_default"""]
									for batch_size in range(1     ,					len(__UpperCAmelCase							) + 1							):
												SCREAMING_SNAKE_CASE__										=   {
												    """input_ids""": tf.constant(dummy_input_ids[:batch_size]							),
												    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]							),
												}
												SCREAMING_SNAKE_CASE__										=   serving_func(**__UpperCAmelCase							)["""sequences"""]
												SCREAMING_SNAKE_CASE__										=   test_model.generate(**__UpperCAmelCase     ,					max_new_tokens=__UpperCAmelCase							)
												tf.debugging.assert_equal(__UpperCAmelCase     ,					__UpperCAmelCase							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     str:
						# TF-only test: tf.saved_model export
						SCREAMING_SNAKE_CASE__										=   TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   2





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    str     ,					__UpperCAmelCase					:    int							) ->     Optional[int]:
												super(__UpperCAmelCase     ,					self							).__init__()
												SCREAMING_SNAKE_CASE__										=   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=__UpperCAmelCase     ,					)
									def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Any							) ->     Tuple:
												SCREAMING_SNAKE_CASE__										=   self.model.generate(
												    input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					max_new_tokens=__UpperCAmelCase     ,					return_dict_in_generate=__UpperCAmelCase     ,					)
												return {"sequences": outputs["sequences"]}
						SCREAMING_SNAKE_CASE__										=   [[2], [1_0_2, 1_0_3]]
						SCREAMING_SNAKE_CASE__										=   [[1], [1, 1]]
						SCREAMING_SNAKE_CASE__										=   DummyModel(model=__UpperCAmelCase							)
						with tempfile.TemporaryDirectory() as tmp_dir:
									tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": dummy_model.serving}							)
									SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							).signatures["""serving_default"""]
									for input_row in range(len(__UpperCAmelCase							)							):
												SCREAMING_SNAKE_CASE__										=   {
												    """input_ids""": tf.constant([dummy_input_ids[input_row]]							),
												    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]							),
												}
												SCREAMING_SNAKE_CASE__										=   serving_func(**__UpperCAmelCase							)["""sequences"""]
												SCREAMING_SNAKE_CASE__										=   test_model.generate(**__UpperCAmelCase     ,					max_new_tokens=__UpperCAmelCase							)
												tf.debugging.assert_equal(__UpperCAmelCase     ,					__UpperCAmelCase							)

			@slow
			@require_tensorflow_text
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Dict:
						# 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=__UpperCAmelCase							)





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

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





												def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    Dict     ,					*__UpperCAmelCase					:    str     ,					**__UpperCAmelCase					:    Tuple							) ->     int:
															SCREAMING_SNAKE_CASE__										=   self.tokenizer.tokenize(__UpperCAmelCase							)
															SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   text.pad_model_inputs(
															    __UpperCAmelCase     ,					max_seq_length=6_4     ,					pad_value=self.model.config.pad_token_id							)
															SCREAMING_SNAKE_CASE__										=   self.model.generate(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)
															return self.tokenizer.detokenize(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   CompleteSentenceTransformer()
									SCREAMING_SNAKE_CASE__										=   tf.keras.layers.Input(shape=(1,)     ,					dtype=tf.string     ,					name="""inputs"""							)
									SCREAMING_SNAKE_CASE__										=   complete_model(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.keras.Model(__UpperCAmelCase     ,					__UpperCAmelCase							)
									keras_model.save(__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[Any]:
						# Has PT equivalent: this test relies on random sampling
						SCREAMING_SNAKE_CASE__										=   {
						    """do_sample""": True,
						    """num_beams""": 1,
						    """top_p""": 0.7,
						    """top_k""": 1_0,
						    """temperature""": 0.7,
						}
						SCREAMING_SNAKE_CASE__										=   1_4

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

						SCREAMING_SNAKE_CASE__										=   6_3_8
						# forces the generation to happen on CPU, to avoid GPU-related quirks
						with tf.device(""":/CPU:0"""							):
									tf.random.set_seed(0							)
									SCREAMING_SNAKE_CASE__										=   model.generate(**__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						self.assertTrue(expectation == len(generated_tokens[0]							)							)

						SCREAMING_SNAKE_CASE__										=   [6_3_8, 1_9_8]
						with tf.device(""":/CPU:0"""							):
									tf.random.set_seed(0							)
									SCREAMING_SNAKE_CASE__										=   model.generate(**__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						self.assertTrue(expectation == len(generated_tokens[0]							)							)





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





						class  lowerCamelCase    (A__      ):

									def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Any=None     ,					**__UpperCAmelCase					:    Optional[int]							) ->     Any:
												return super().call(__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""							)
						SCREAMING_SNAKE_CASE__										=   bart_model.generate(__UpperCAmelCase     ,					foo="""bar"""							).numpy()
						self.assertTrue(np.array_equal(__UpperCAmelCase     ,					__UpperCAmelCase							)							)





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

									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Optional[Any]     ,					**__UpperCAmelCase					:    List[Any]							) ->     Optional[int]:
												return super().call(__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   FakeEncoder(bart_model.config     ,					bart_model.model.shared							)
						SCREAMING_SNAKE_CASE__										=   fake_encoder

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





"""simple docstring"""




import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


