


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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				tf.convert_to_tensor(
					    [
					        [
					            8.2_22_09_91,  # 3rd highest value; idx. 0
					            -0.5_62_00_44,
					            5.23_22_97_52,
					            4.0_38_63_93,
					            -6.8_79_83_78,
					            -0.54_78_58_02,
					            -3.2_01_21_53,
					            2.92_77_71_76,
					            1.88_17_19_53,
					            7.35_34_12_76,  # 5th highest value; idx. 9
					            8.43_20_78_33,  # 2nd highest value; idx. 10
					            -9.85_71_18_36,
					            -5.96_20_92_36,
					            -1.13_03_91_61,
					            -7.1_11_52_94,
					            -0.8_36_96_33,
					            -5.3_18_64_08,
					            7.06_42_74_07,
					            0.81_36_93_44,
					            -0.82_02_38_17,
					            -5.9_17_97_96,
					            0.58_81_34_43,
					            -6.99_77_84_38,
					            4.71_55_11_89,
					            -0.18_77_16_37,
					            7.44_02_07_59,  # 4th highest value; idx. 25
					            9.38_45_09_87,  # 1st highest value; idx. 26
					            2.12_66_29_41,
					            -9.32_56_20_38,
					            2.35_65_25_22,
					        ],  # cummulative prob of 5 highest values <= 0.6
					        [
					            0.58_42_55_18,
					            4.53_13_92_38,
					            -5.57_51_04_64,
					            -6.28_03_06_99,
					            -7.19_52_95_03,
					            -4.02_12_25_51,
					            1.39_33_70_37,
					            -6.06_70_70_57,
					            1.59_48_05_17,
					            -9.64_31_19,
					            0.03_90_77_99,
					            0.67_23_17_62,
					            -8.88_20_67_26,
					            6.27_11_59_22,  # 4th highest value; idx. 13
					            2.28_52_07_23,
					            4.82_76_75_06,
					            4.30_42_13_68,
					            8.8_27_53_13,  # 2nd highest value; idx. 17
					            5.44_02_99_58,  # 5th highest value; idx. 18
					            -4.4_73_57_94,
					            7.38_57_95_36,  # 3rd highest value; idx. 20
					            -2.91_05_16_63,
					            2.61_94_60_77,
					            -2.5_67_47_62,
					            -9.48_95_93_02,
					            -4.02_92_26_45,
					            -1.35_41_69_18,
					            9.67_70_23_23,  # 1st highest value; idx. 27
					            -5.89_47_85_53,
					            1.85_37_04_67,
					        ],  # 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(
					    [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23]				,   dtype=tf.floataa				,   )  # expected non filtered values as noted above

					A								=				tf_top_k_top_p_filtering(_snake_case				,   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(_snake_case				,   tf.constant(-float("""inf"""   )				,   dtype=tf.floataa   )   )   )				,   dtype=tf.intaa				,   )

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






@require_tf
class       _UpperCamelCase    (					unittest.TestCase    ,		UpperCAmelCase_				):

				"""simple docstring"""







				if is_tf_available():
								lowerCAmelCase 		= {
								    "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   )	->				Optional[Any]:
					A								=				TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
					A								=				2
					A								=				2






					class       _UpperCamelCase    (					tf.Module				):

									"""simple docstring"""







									def __init__(						self				,   a__   )	->				Union[str, Any]:
										super(_snake_case				,   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=_snake_case				,   )
									def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				int:
										A								=				self.model.generate(
										    input_ids=_snake_case				,   attention_mask=_snake_case				,   max_new_tokens=_snake_case				,   return_dict_in_generate=_snake_case				,   )
										return {"sequences": outputs["sequences"]}
					A								=				[[2, 0], [102, 103]]
					A								=				[[1, 0], [1, 1]]
					A								=				DummyModel(model=_snake_case   )
					with tempfile.TemporaryDirectory() as tmp_dir:
						tf.saved_model.save(_snake_case				,   _snake_case				,   signatures={"""serving_default""": dummy_model.serving}   )
						A								=				tf.saved_model.load(_snake_case   ).signatures["""serving_default"""]
						for batch_size in range(1				,   len(_snake_case   ) + 1   ):
							A								=				{
							    """input_ids""": tf.constant(dummy_input_ids[:batch_size]   ),
							    """attention_mask""": tf.constant(dummy_attention_masks[:batch_size]   ),
							}
							A								=				serving_func(**_snake_case   )["""sequences"""]
							A								=				test_model.generate(**_snake_case				,   max_new_tokens=_snake_case   )
							tf.debugging.assert_equal(_snake_case				,   _snake_case   )

				@slow
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
					A								=				1
					A								=				2






					class       _UpperCamelCase    (					tf.Module				):

									"""simple docstring"""







									def __init__(						self				,   a__   )	->				Optional[int]:
										super(_snake_case				,   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=_snake_case				,   )
									def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Dict:
										A								=				self.model.generate(
										    input_ids=_snake_case				,   attention_mask=_snake_case				,   max_new_tokens=_snake_case				,   return_dict_in_generate=_snake_case				,   )
										return {"sequences": outputs["sequences"]}
					A								=				[[2], [102, 103]]
					A								=				[[1], [1, 1]]
					A								=				DummyModel(model=_snake_case   )
					with tempfile.TemporaryDirectory() as tmp_dir:
						tf.saved_model.save(_snake_case				,   _snake_case				,   signatures={"""serving_default""": dummy_model.serving}   )
						A								=				tf.saved_model.load(_snake_case   ).signatures["""serving_default"""]
						for input_row in range(len(_snake_case   )   ):
							A								=				{
							    """input_ids""": tf.constant([dummy_input_ids[input_row]]   ),
							    """attention_mask""": tf.constant([dummy_attention_masks[input_row]]   ),
							}
							A								=				serving_func(**_snake_case   )["""sequences"""]
							A								=				test_model.generate(**_snake_case				,   max_new_tokens=_snake_case   )
							tf.debugging.assert_equal(_snake_case				,   _snake_case   )

				@slow
				@require_tensorflow_text
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:


					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=_snake_case   )






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

										"""simple docstring"""







										def __init__(						self   )	->				int:
											super().__init__()
											A								=				text.SentencepieceTokenizer(
											    model=tf.io.gfile.GFile(os.path.join(_snake_case				,   """spiece.model"""   )				,   """rb"""   ).read()   )
											A								=				TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5"""   )

										def      _UpperCAmelCase					(						self				,   a__				,   *a__				,   **a__   )	->				Any:
											A								=				self.tokenizer.tokenize(_snake_case   )
											A					,			A								=				text.pad_model_inputs(
											    _snake_case				,   max_seq_length=64				,   pad_value=self.model.config.pad_token_id   )
											A								=				self.model.generate(input_ids=_snake_case				,   attention_mask=_snake_case   )
											return self.tokenizer.detokenize(_snake_case   )
						A								=				CompleteSentenceTransformer()
						A								=				tf.keras.layers.Input(shape=(1,)				,   dtype=tf.string				,   name="""inputs"""   )
						A								=				complete_model(_snake_case   )
						A								=				tf.keras.Model(_snake_case				,   _snake_case   )
						keras_model.save(_snake_case   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					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(_snake_case				,   return_tensors="""tf"""   )
					A								=				TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )

					A								=				638
					# 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(**_snake_case				,   eos_token_id=_snake_case				,   **_snake_case   )
					self.assertTrue(expectation == len(generated_tokens[0]   )   )

					A								=				[638, 198]
					with tf.device(""":/CPU:0"""   ):
						tf.random.set_seed(0   )
						A								=				model.generate(**_snake_case				,   eos_token_id=_snake_case				,   **_snake_case   )
					self.assertTrue(expectation == len(generated_tokens[0]   )   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					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(_snake_case				,   return_tensors="""tf"""   ).input_ids
					A								=				TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart"""   )
					A								=				bart_model.generate(_snake_case   ).numpy()






					class       _UpperCamelCase    (					UpperCAmelCase_				):

									"""simple docstring"""







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






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

									"""simple docstring"""







									def      _UpperCAmelCase					(						self				,   a__				,   **a__   )	->				str:
										return super().call(_snake_case				,   **_snake_case   )
					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(_snake_case   ).numpy()
					with self.assertRaises(_snake_case   ):
						# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
						bart_model.generate(_snake_case				,   foo="""bar"""   )







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


if __name__ == "__main__":
				print(f'''{solution() = }''')



