

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 							snake_case__(	self					:							Optional[Any]				) ->Any:
									snake_case_  =      tf.convert_to_tensor(
									    [
									        [
									            8.2220991,  # 3rd highest value; idx. 0
									            -0.5620044,
									            5.23229752,
									            4.0386393,
									            -6.8798378,
									            -0.54785802,
									            -3.2012153,
									            2.92777176,
									            1.88171953,
									            7.35341276,  # 5th highest value; idx. 9
									            8.43207833,  # 2nd highest value; idx. 10
									            -9.85711836,
									            -5.96209236,
									            -1.13039161,
									            -7.1115294,
									            -0.8369633,
									            -5.3186408,
									            7.06427407,
									            0.81369344,
									            -0.82023817,
									            -5.9179796,
									            0.58813443,
									            -6.99778438,
									            4.71551189,
									            -0.18771637,
									            7.44020759,  # 4th highest value; idx. 25
									            9.38450987,  # 1st highest value; idx. 26
									            2.12662941,
									            -9.32562038,
									            2.35652522,
									        ],  # cummulative prob of 5 highest values <= 0.6
									        [
									            0.58425518,
									            4.53139238,
									            -5.57510464,
									            -6.28030699,
									            -7.19529503,
									            -4.02122551,
									            1.39337037,
									            -6.06707057,
									            1.59480517,
									            -9.643119,
									            0.03907799,
									            0.67231762,
									            -8.88206726,
									            6.27115922,  # 4th highest value; idx. 13
									            2.28520723,
									            4.82767506,
									            4.30421368,
									            8.8275313,  # 2nd highest value; idx. 17
									            5.44029958,  # 5th highest value; idx. 18
									            -4.4735794,
									            7.38579536,  # 3rd highest value; idx. 20
									            -2.91051663,
									            2.61946077,
									            -2.5674762,
									            -9.48959302,
									            -4.02922645,
									            -1.35416918,
									            9.67702323,  # 1st highest value; idx. 27
									            -5.89478553,
									            1.85370467,
									        ],  # cummulative prob of 5 highest values <= 0.6
									    ]				,  dtype=tf.floataa				,  )

									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

									snake_case_  =      tf.convert_to_tensor(
									    [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023]				,  dtype=tf.floataa				,  )  # expected non filtered values as noted above

									snake_case_  =      tf_top_k_top_p_filtering(_UpperCamelCase				,  top_k=1_0				,  top_p=0.6				,  min_tokens_to_keep=4				)

									snake_case_  =      output[output != -float('''inf'''				)]
									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       snake_case_  (						unittest.TestCase      ,			__A   ):







			'''simple docstring'''

			if is_tf_available():
						SCREAMING_SNAKE_CASE	:  Optional[int]		     =							{
						    "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 							snake_case__(	self					:							List[Any]				) ->Optional[int]:
									# TF-only test: tf.saved_model export
									snake_case_  =      TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
									snake_case_  =      2
									snake_case_  =      2





									class       snake_case_  (						tf.Module   ):







												'''simple docstring'''

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






									snake_case_  =      [[2, 0], [1_0_2, 1_0_3]]
									snake_case_  =      [[1, 0], [1, 1]]
									snake_case_  =      DummyModel(model=_UpperCamelCase				)
									with tempfile.TemporaryDirectory() as tmp_dir:
															tf.saved_model.save(_UpperCamelCase				,  _UpperCamelCase				,  signatures={'''serving_default''': dummy_model.serving}				)
															snake_case_  =      tf.saved_model.load(_UpperCamelCase				).signatures['''serving_default''']
															for batch_size in range(1				,  len(_UpperCamelCase				) + 1				):
																					snake_case_  =      {
																					    '''input_ids''': tf.constant(dummy_input_ids[:batch_size]				),
																					    '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]				),
																					}
																					snake_case_  =      serving_func(**_UpperCamelCase				)['''sequences''']
																					snake_case_  =      test_model.generate(**_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				)
																					tf.debugging.assert_equal(_UpperCamelCase				,  _UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							List[str]				) ->int:
									# TF-only test: tf.saved_model export
									snake_case_  =      TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
									snake_case_  =      1
									snake_case_  =      2





									class       snake_case_  (						tf.Module   ):







												'''simple docstring'''

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






									snake_case_  =      [[2], [1_0_2, 1_0_3]]
									snake_case_  =      [[1], [1, 1]]
									snake_case_  =      DummyModel(model=_UpperCamelCase				)
									with tempfile.TemporaryDirectory() as tmp_dir:
															tf.saved_model.save(_UpperCamelCase				,  _UpperCamelCase				,  signatures={'''serving_default''': dummy_model.serving}				)
															snake_case_  =      tf.saved_model.load(_UpperCamelCase				).signatures['''serving_default''']
															for input_row in range(len(_UpperCamelCase				)				):
																					snake_case_  =      {
																					    '''input_ids''': tf.constant([dummy_input_ids[input_row]]				),
																					    '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]				),
																					}
																					snake_case_  =      serving_func(**_UpperCamelCase				)['''sequences''']
																					snake_case_  =      test_model.generate(**_UpperCamelCase				,  max_new_tokens=_UpperCamelCase				)
																					tf.debugging.assert_equal(_UpperCamelCase				,  _UpperCamelCase				)







			@slow
			@require_tensorflow_text
			def 							snake_case__(	self					:							Optional[Any]				) ->List[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=_UpperCamelCase				)





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







																		'''simple docstring'''

																		def __init__(	self					:							Tuple				) ->List[Any]:
																								super().__init__()
																								snake_case_  =      text.SentencepieceTokenizer(
																								    model=tf.io.gfile.GFile(os.path.join(_UpperCamelCase				,  '''spiece.model'''				)				,  '''rb'''				).read()				)
																								snake_case_  =      TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)







																		def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[Any]				,  *_UpperCamelCase					:							Optional[int]				,  **_UpperCamelCase					:							str				) ->List[Any]:
																								snake_case_  =      self.tokenizer.tokenize(_UpperCamelCase				)
																								snake_case_,       snake_case_  =      text.pad_model_inputs(
																								    _UpperCamelCase				,  max_seq_length=6_4				,  pad_value=self.model.config.pad_token_id				)
																								snake_case_  =      self.model.generate(input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)
																								return self.tokenizer.detokenize(_UpperCamelCase				)






															snake_case_  =      CompleteSentenceTransformer()
															snake_case_  =      tf.keras.layers.Input(shape=(1,)				,  dtype=tf.string				,  name='''inputs'''				)
															snake_case_  =      complete_model(_UpperCamelCase				)
															snake_case_  =      tf.keras.Model(_UpperCamelCase				,  _UpperCamelCase				)
															keras_model.save(_UpperCamelCase				)







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

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

									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				)
															snake_case_  =      model.generate(**_UpperCamelCase				,  eos_token_id=_UpperCamelCase				,  **_UpperCamelCase				)
									self.assertTrue(expectation == len(generated_tokens[0]				)				)

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







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





									class       snake_case_  (						__A   ):







												'''simple docstring'''

												def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Tuple=None				,  **_UpperCamelCase					:							Optional[int]				) ->List[str]:
																		return super().call(_UpperCamelCase				,  **_UpperCamelCase				)






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





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







												'''simple docstring'''

												def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  **_UpperCamelCase					:							Tuple				) ->Optional[Any]:
																		return super().call(_UpperCamelCase				,  **_UpperCamelCase				)






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

									# Normal generation still works (the output will be different because the encoder weights are different)
									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"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()

