
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 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tf.convert_to_tensor(
              [
                  [
                      8.2_2_2_0_9_9_1,  # 3rd highest value; idx. 0
                      -0.5_6_2_0_0_4_4,
                      5.2_3_2_2_9_7_5_2,
                      4.0_3_8_6_3_9_3,
                      -6.8_7_9_8_3_7_8,
                      -0.5_4_7_8_5_8_0_2,
                      -3.2_0_1_2_1_5_3,
                      2.9_2_7_7_7_1_7_6,
                      1.8_8_1_7_1_9_5_3,
                      7.3_5_3_4_1_2_7_6,  # 5th highest value; idx. 9
                      8.4_3_2_0_7_8_3_3,  # 2nd highest value; idx. 10
                      -9.8_5_7_1_1_8_3_6,
                      -5.9_6_2_0_9_2_3_6,
                      -1.1_3_0_3_9_1_6_1,
                      -7.1_1_1_5_2_9_4,
                      -0.8_3_6_9_6_3_3,
                      -5.3_1_8_6_4_0_8,
                      7.0_6_4_2_7_4_0_7,
                      0.8_1_3_6_9_3_4_4,
                      -0.8_2_0_2_3_8_1_7,
                      -5.9_1_7_9_7_9_6,
                      0.5_8_8_1_3_4_4_3,
                      -6.9_9_7_7_8_4_3_8,
                      4.7_1_5_5_1_1_8_9,
                      -0.1_8_7_7_1_6_3_7,
                      7.4_4_0_2_0_7_5_9,  # 4th highest value; idx. 25
                      9.3_8_4_5_0_9_8_7,  # 1st highest value; idx. 26
                      2.1_2_6_6_2_9_4_1,
                      -9.3_2_5_6_2_0_3_8,
                      2.3_5_6_5_2_5_2_2,
                  ],  # cummulative prob of 5 highest values <= 0.6
                  [
                      0.5_8_4_2_5_5_1_8,
                      4.5_3_1_3_9_2_3_8,
                      -5.5_7_5_1_0_4_6_4,
                      -6.2_8_0_3_0_6_9_9,
                      -7.1_9_5_2_9_5_0_3,
                      -4.0_2_1_2_2_5_5_1,
                      1.3_9_3_3_7_0_3_7,
                      -6.0_6_7_0_7_0_5_7,
                      1.5_9_4_8_0_5_1_7,
                      -9.6_4_3_1_1_9,
                      0.0_3_9_0_7_7_9_9,
                      0.6_7_2_3_1_7_6_2,
                      -8.8_8_2_0_6_7_2_6,
                      6.2_7_1_1_5_9_2_2,  # 4th highest value; idx. 13
                      2.2_8_5_2_0_7_2_3,
                      4.8_2_7_6_7_5_0_6,
                      4.3_0_4_2_1_3_6_8,
                      8.8_2_7_5_3_1_3,  # 2nd highest value; idx. 17
                      5.4_4_0_2_9_9_5_8,  # 5th highest value; idx. 18
                      -4.4_7_3_5_7_9_4,
                      7.3_8_5_7_9_5_3_6,  # 3rd highest value; idx. 20
                      -2.9_1_0_5_1_6_6_3,
                      2.6_1_9_4_6_0_7_7,
                      -2.5_6_7_4_7_6_2,
                      -9.4_8_9_5_9_3_0_2,
                      -4.0_2_9_2_2_6_4_5,
                      -1.3_5_4_1_6_9_1_8,
                      9.6_7_7_0_2_3_2_3,  # 1st highest value; idx. 27
                      -5.8_9_4_7_8_5_5_3,
                      1.8_5_3_7_0_4_6_7,
                  ],  # cummulative prob of 5 highest values <= 0.6
              ]		,	dtype=tf.floataa		,	)

          snake_case__						=				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

          snake_case__						=				tf.convert_to_tensor(
              [8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3]		,	dtype=tf.floataa		,	)  # expected non filtered values as noted above

          snake_case__						=				tf_top_k_top_p_filtering(lowerCamelCase		,	top_k=10		,	top_p=0.6		,	min_tokens_to_keep=4 )

          snake_case__						=				output[output != -float("inf" )]
          snake_case__						=				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 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase       ,					__UpperCamelCase ):
      # setting framework_dependent_parameters needs to be gated, just like its contents' imports
      if is_tf_available():
            _A						:		List[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 A_			(			self ):
          # 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 					_SCREAMING_SNAKE_CASE  (      tf.Module ):
                def __init__(			self		,	lowerCamelCase ):
                    super(lowerCamelCase		,	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=lowerCamelCase		,	)
                def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
                    snake_case__						=				self.model.generate(
                        input_ids=lowerCamelCase		,	attention_mask=lowerCamelCase		,	max_new_tokens=lowerCamelCase		,	return_dict_in_generate=lowerCamelCase		,	)
                    return {"sequences": outputs["sequences"]}





          snake_case__						=				[[2, 0], [1_02, 1_03]]
          snake_case__						=				[[1, 0], [1, 1]]
          snake_case__						=				DummyModel(model=lowerCamelCase )
          with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(lowerCamelCase		,	lowerCamelCase		,	signatures={"serving_default": dummy_model.serving} )
              snake_case__						=				tf.saved_model.load(lowerCamelCase ).signatures["serving_default"]
              for batch_size in range(1		,	len(lowerCamelCase ) + 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(**lowerCamelCase )["sequences"]
                  snake_case__						=				test_model.generate(**lowerCamelCase		,	max_new_tokens=lowerCamelCase )
                  tf.debugging.assert_equal(lowerCamelCase		,	lowerCamelCase )
      @slow
      def A_			(			self ):
          # 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 					_SCREAMING_SNAKE_CASE  (      tf.Module ):
                def __init__(			self		,	lowerCamelCase ):
                    super(lowerCamelCase		,	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=lowerCamelCase		,	)
                def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
                    snake_case__						=				self.model.generate(
                        input_ids=lowerCamelCase		,	attention_mask=lowerCamelCase		,	max_new_tokens=lowerCamelCase		,	return_dict_in_generate=lowerCamelCase		,	)
                    return {"sequences": outputs["sequences"]}





          snake_case__						=				[[2], [1_02, 1_03]]
          snake_case__						=				[[1], [1, 1]]
          snake_case__						=				DummyModel(model=lowerCamelCase )
          with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(lowerCamelCase		,	lowerCamelCase		,	signatures={"serving_default": dummy_model.serving} )
              snake_case__						=				tf.saved_model.load(lowerCamelCase ).signatures["serving_default"]
              for input_row in range(len(lowerCamelCase ) ):
                  snake_case__						=				{
                      "input_ids": tf.constant([dummy_input_ids[input_row]] ),
                      "attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
                  }
                  snake_case__						=				serving_func(**lowerCamelCase )["sequences"]
                  snake_case__						=				test_model.generate(**lowerCamelCase		,	max_new_tokens=lowerCamelCase )
                  tf.debugging.assert_equal(lowerCamelCase		,	lowerCamelCase )
      @slow
      @require_tensorflow_text
      def A_			(			self ):
          # 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 					_SCREAMING_SNAKE_CASE  (      tf.keras.layers.Layer ):
                    def __init__(			self ):
                        super().__init__()
                        snake_case__						=				text.SentencepieceTokenizer(
                            model=tf.io.gfile.GFile(os.path.join(lowerCamelCase		,	"spiece.model" )		,	"rb" ).read() )
                        snake_case__						=				TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5" )



                    def A_			(			self		,	lowerCamelCase		,	*lowerCamelCase		,	**lowerCamelCase ):
                        snake_case__						=				self.tokenizer.tokenize(lowerCamelCase )
                        snake_case__						,					snake_case__						=				text.pad_model_inputs(
                            lowerCamelCase		,	max_seq_length=64		,	pad_value=self.model.config.pad_token_id )
                        snake_case__						=				self.model.generate(input_ids=lowerCamelCase		,	attention_mask=lowerCamelCase )
                        return self.tokenizer.detokenize(lowerCamelCase )





              snake_case__						=				CompleteSentenceTransformer()
              snake_case__						=				tf.keras.layers.Input(shape=(1,)		,	dtype=tf.string		,	name="inputs" )
              snake_case__						=				complete_model(lowerCamelCase )
              snake_case__						=				tf.keras.Model(lowerCamelCase		,	lowerCamelCase )
              keras_model.save(lowerCamelCase )
      def A_			(			self ):
          # Has PT equivalent: this test relies on random sampling
          snake_case__						=				{
              "do_sample": True,
              "num_beams": 1,
              "top_p": 0.7,
              "top_k": 10,
              "temperature": 0.7,
          }
          snake_case__						=				14

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

          snake_case__						=				6_38
          # 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(**lowerCamelCase		,	eos_token_id=lowerCamelCase		,	**lowerCamelCase )
          self.assertTrue(expectation == len(generated_tokens[0] ) )

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



      def A_			(			self ):
          # 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(lowerCamelCase		,	return_tensors="tf" ).input_ids
          snake_case__						=				TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart" )
          snake_case__						=				bart_model.generate(lowerCamelCase ).numpy()




          class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
                def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
                    return super().call(lowerCamelCase		,	**lowerCamelCase )





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




          class 					_SCREAMING_SNAKE_CASE  (      bart_model.model.encoder.__class__ ):
                def A_			(			self		,	lowerCamelCase		,	**lowerCamelCase ):
                    return super().call(lowerCamelCase		,	**lowerCamelCase )





          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(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 __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						,					snake_case__						=				set(__lowerCAmelCase							), [start]

    while stack:
        snake_case__						=				stack.pop()
        explored.add(__lowerCAmelCase							)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]							):
            if adj not in explored:
                stack.append(__lowerCAmelCase							)
    return explored


__magic_name__            =  {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))



