






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	):







				"""simple docstring"""




				def 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :Union[str, Any]						=      tf.convert_to_tensor(
					    [
					        [
					            8.222_0991,  # 3rd highest value; idx. 0
					            -0.562_0044,
					            5.2322_9752,
					            4.038_6393,
					            -6.879_8378,
					            -0.5478_5802,
					            -3.201_2153,
					            2.9277_7176,
					            1.8817_1953,
					            7.3534_1276,  # 5th highest value; idx. 9
					            8.4320_7833,  # 2nd highest value; idx. 10
					            -9.8571_1836,
					            -5.9620_9236,
					            -1.1303_9161,
					            -7.111_5294,
					            -0.836_9633,
					            -5.318_6408,
					            7.0642_7407,
					            0.8136_9344,
					            -0.8202_3817,
					            -5.917_9796,
					            0.5881_3443,
					            -6.9977_8438,
					            4.7155_1189,
					            -0.1877_1637,
					            7.4402_0759,  # 4th highest value; idx. 25
					            9.3845_0987,  # 1st highest value; idx. 26
					            2.1266_2941,
					            -9.3256_2038,
					            2.3565_2522,
					        ],  # cummulative prob of 5 highest values <= 0.6
					        [
					            0.5842_5518,
					            4.5313_9238,
					            -5.5751_0464,
					            -6.2803_0699,
					            -7.1952_9503,
					            -4.0212_2551,
					            1.3933_7037,
					            -6.0670_7057,
					            1.5948_0517,
					            -9.64_3119,
					            0.0390_7799,
					            0.6723_1762,
					            -8.8820_6726,
					            6.2711_5922,  # 4th highest value; idx. 13
					            2.2852_0723,
					            4.8276_7506,
					            4.3042_1368,
					            8.827_5313,  # 2nd highest value; idx. 17
					            5.4402_9958,  # 5th highest value; idx. 18
					            -4.473_5794,
					            7.3857_9536,  # 3rd highest value; idx. 20
					            -2.9105_1663,
					            2.6194_6077,
					            -2.567_4762,
					            -9.4895_9302,
					            -4.0292_2645,
					            -1.3541_6918,
					            9.6770_2323,  # 1st highest value; idx. 27
					            -5.8947_8553,
					            1.8537_0467,
					        ],  # cummulative prob of 5 highest values <= 0.6
					    ]  ,     dtype=tf.floataa  ,     )

					UpperCamelCase__ :List[Any]						=      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

					UpperCamelCase__ :Tuple						=      tf.convert_to_tensor(
					    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023]  ,     dtype=tf.floataa  ,     )  # expected non filtered values as noted above

					UpperCamelCase__ :Dict						=      tf_top_k_top_p_filtering(lowerCamelCase__  ,     top_k=10  ,     top_p=0.6  ,     min_tokens_to_keep=4     )

					UpperCamelCase__ :Dict						=      output[output != -float("""inf"""     )]
					UpperCamelCase__ :Optional[int]						=      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        lowerCAmelCase_				(      unittest.TestCase						,      lowercase	):







				"""simple docstring"""




				if is_tf_available():
								_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 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :Any						=      TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					UpperCamelCase__ :Dict						=      2
					UpperCamelCase__ :List[str]						=      2

					class        lowerCAmelCase_				(      tf.Module	):







									"""simple docstring"""




									def __init__( self     :Any  ,     lowerCamelCase__     :Optional[Any]     ):
										super(lowerCamelCase__  ,     self     ).__init__()
										UpperCamelCase__ :Optional[int]						=      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 				__a   ( self     :int  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :List[str]     ):
										UpperCamelCase__ :Dict						=      self.model.generate(
										    input_ids=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     max_new_tokens=lowerCamelCase__  ,     return_dict_in_generate=lowerCamelCase__  ,     )
										return {"sequences": outputs["sequences"]}

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







				@slow
				def 				__a   ( self     :str     ):
					UpperCamelCase__ :Union[str, Any]						=      TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					UpperCamelCase__ :int						=      1
					UpperCamelCase__ :Any						=      2

					class        lowerCAmelCase_				(      tf.Module	):







									"""simple docstring"""




									def __init__( self     :int  ,     lowerCamelCase__     :str     ):
										super(lowerCamelCase__  ,     self     ).__init__()
										UpperCamelCase__ :str						=      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 				__a   ( self     :List[str]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Any     ):
										UpperCamelCase__ :Optional[Any]						=      self.model.generate(
										    input_ids=lowerCamelCase__  ,     attention_mask=lowerCamelCase__  ,     max_new_tokens=lowerCamelCase__  ,     return_dict_in_generate=lowerCamelCase__  ,     )
										return {"sequences": outputs["sequences"]}

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







				@slow
				@require_tensorflow_text
				def 				__a   ( self     :Tuple     ):






					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        lowerCAmelCase_				(      tf.keras.layers.Layer	):







										"""simple docstring"""




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







										def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     *lowerCamelCase__     :List[Any]  ,     **lowerCamelCase__     :str     ):
											UpperCamelCase__ :List[Any]						=      self.tokenizer.tokenize(lowerCamelCase__     )
											UpperCamelCase__ :int						=      text.pad_model_inputs(
											    lowerCamelCase__  ,     max_seq_length=64  ,     pad_value=self.model.config.pad_token_id     )
											UpperCamelCase__ :Tuple						=      self.model.generate(input_ids=lowerCamelCase__  ,     attention_mask=lowerCamelCase__     )
											return self.tokenizer.detokenize(lowerCamelCase__     )

						UpperCamelCase__ :Optional[Any]						=      CompleteSentenceTransformer()
						UpperCamelCase__ :Optional[Any]						=      tf.keras.layers.Input(shape=(1,)  ,     dtype=tf.string  ,     name="""inputs"""     )
						UpperCamelCase__ :str						=      complete_model(lowerCamelCase__     )
						UpperCamelCase__ :Any						=      tf.keras.Model(lowerCamelCase__  ,     lowerCamelCase__     )
						keras_model.save(lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :Tuple						=      {
					    "do_sample": True,
					    "num_beams": 1,
					    "top_p": 0.7,
					    "top_k": 10,
					    "temperature": 0.7,
					}
					UpperCamelCase__ :List[str]						=      14

					UpperCamelCase__ :Optional[Any]						=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )
					UpperCamelCase__ :List[str]						=      "Hello, my dog is cute and"
					UpperCamelCase__ :List[str]						=      tokenizer(lowerCamelCase__  ,     return_tensors="""tf"""     )
					UpperCamelCase__ :Any						=      TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""     )

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

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







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :str						=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart"""     )
					UpperCamelCase__ :Optional[Any]						=      "Hugging Face is a technology company based in New York and Paris."
					UpperCamelCase__ :List[Any]						=      bart_tokenizer(lowerCamelCase__  ,     return_tensors="""tf"""     ).input_ids
					UpperCamelCase__ :Any						=      TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""     )
					UpperCamelCase__ :Optional[int]						=      bart_model.generate(lowerCamelCase__     ).numpy()

					class        lowerCAmelCase_				(      lowercase	):







									"""simple docstring"""




									def 				__a   ( self     :Dict  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :str=None  ,     **lowerCamelCase__     :List[str]     ):
										return super().call(lowerCamelCase__  ,     **lowerCamelCase__     )

					UpperCamelCase__ :Any						=      FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart"""     )
					UpperCamelCase__ :Tuple						=      bart_model.generate(lowerCamelCase__  ,     foo="""bar"""     ).numpy()
					self.assertTrue(np.array_equal(lowerCamelCase__  ,     lowerCamelCase__     )     )

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







									"""simple docstring"""




									def 				__a   ( self     :List[str]  ,     lowerCamelCase__     :Optional[int]  ,     **lowerCamelCase__     :Tuple     ):
										return super().call(lowerCamelCase__  ,     **lowerCamelCase__     )

					UpperCamelCase__ :Dict						=      FakeEncoder(bart_model.config  ,     bart_model.model.shared     )
					UpperCamelCase__ :Optional[Any]						=      fake_encoder

					# Normal generation still works (the output will be different because the encoder weights are different)
					UpperCamelCase__ :Any						=      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"""     )







'''simple docstring'''
import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__			=      False
try:
 UpperCamelCase__			=      _is_package_available('google.colab')
except ModuleNotFoundError:
 pass


@input.register
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							str = None	,  a	:							list = []      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  0
       lowercase_						:     Dict							     =  choices
       lowercase_						:     List[str]							     =  prompt
       if sys.platform == "win32":
          lowercase_						:     List[Any]							     =  "*"
       else:
          lowercase_						:     Union[str, Any]							     =  "➔ "




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							str = ""      ):






       '''simple docstring'''






       if sys.platform != "win32":
          writeColor(self.choices[index]	,  3_2	,  a      )
       else:
          forceWrite(self.choices[index]	,  a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							int      ):






       '''simple docstring'''






       if index == self.position:
          forceWrite(f""" {self.arrow_char} """      )
          self.write_choice(a      )
       else:
          forceWrite(f"""    {self.choices[index]}"""      )
       reset_cursor()




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Direction	,  a	:							int = 1      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.position
       if direction == Direction.DOWN:
          if self.position + 1 >= len(self.choices      ):
             return
          self.position += num_spaces
       else:
          if self.position - 1 < 0:
             return
          self.position -= num_spaces
       clear_line()
       self.print_choice(a      )
       move_cursor(a	,  direction.name      )
       self.print_choice(self.position      )




    @input.mark(KEYMAP["up"]      )
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.move_direction(Direction.UP      )




    @input.mark(KEYMAP["down"]      )
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self.move_direction(Direction.DOWN      )




    @input.mark(KEYMAP["newline"]      )
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       return self.position




    @input.mark(KEYMAP["interrupt"]      )
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       raise KeyboardInterrupt




    @input.mark_multiple(*[KEYMAP[str(a      )] for number in range(1_0      )]      )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     str							     =  int(chr(self.current_selection      )      )
       lowercase_						:     Tuple							     =  index - self.position
       if index == self.position:
          return
       if index < len(self.choices      ):
          if self.position > index:
             self.move_direction(Direction.UP	,  -movement      )
          elif self.position < index:
             self.move_direction(Direction.DOWN	,  a      )
          else:
             return
       else:
          return





    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int = 0      ):






       '''simple docstring'''






       if self.prompt:
          linebreak()
          forceWrite(self.prompt	,  "\n"      )
          if in_colab:
             forceWrite("Please input a choice index (starting from 0), and press enter"	,  "\n"      )
          else:
             forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,  "\n"      )
       lowercase_						:     Dict							     =  default_choice
       for i in range(len(self.choices      )      ):
          self.print_choice(a      )
          forceWrite("\n"      )
       move_cursor(len(self.choices      ) - self.position	,  "UP"      )
       with cursor.hide():
          while True:
             if in_colab:
                try:
                   lowercase_						:     List[str]							     =  int(builtins.input()      )
                except ValueError:
                   lowercase_						:     List[Any]							     =  default_choice
             else:
                lowercase_						:     Dict							     =  self.handle_input()
             if choice is not None:
                reset_cursor()
                for _ in range(len(self.choices      ) + 1      ):
                   move_cursor(1	,  "UP"      )
                   clear_line()
                self.write_choice(a	,  "\n"      )
                return choice




