


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				A   :					List[Any]	=      "platform"

				import jax
				import jax.numpy as jnp

				from transformers import BlenderbotTokenizer
				from transformers.models.blenderbot.modeling_flax_blenderbot import (
				    FlaxBlenderbotForConditionalGeneration,
				    FlaxBlenderbotModel,
				    shift_tokens_right,
				)

def    lowercase_	(	_A    :					List[str]					,      _A    :					Any					,      _A    :					Union[str, Any]=None					,      _A    :					Tuple=None					,      _A    :					Union[str, Any]=None					,      _A    :					List[str]=None					,      _A    :					str=None					,      _A    :					Dict=None					,      ):

						"""simple docstring"""




						if attention_mask is None:
												lowerCamelCase__	:       Optional[Any]					=						np.where(input_ids != config.pad_token_id					,      1					,      0		)
						if decoder_attention_mask is None:
												lowerCamelCase__	:       Union[str, Any]					=						np.where(decoder_input_ids != config.pad_token_id					,      1					,      0		)
						if head_mask is None:
												lowerCamelCase__	:       Dict					=						np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												lowerCamelCase__	:       str					=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												lowerCamelCase__	:       List[str]					=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}







class     _lowercase				:


							"""simple docstring"""




							def __init__(     self							:    List[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any]=13      ,   __lowerCamelCase							:    Optional[int]=7      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    List[Any]=False      ,   __lowerCamelCase							:    Dict=99      ,   __lowerCamelCase							:    List[Any]=16      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Union[str, Any]=4      ,   __lowerCamelCase							:    str=4      ,   __lowerCamelCase							:    int="gelu"      ,   __lowerCamelCase							:    List[Any]=0.1      ,   __lowerCamelCase							:    Optional[Any]=0.1      ,   __lowerCamelCase							:    Union[str, Any]=32      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    Any=1      ,   __lowerCamelCase							:    List[Any]=0      ,   __lowerCamelCase							:    Any=0.0_2      ,   ):


													'''simple docstring'''



													lowerCamelCase__	:       List[str]					=						parent
													lowerCamelCase__	:       int					=						batch_size
													lowerCamelCase__	:       Tuple					=						seq_length
													lowerCamelCase__	:       Tuple					=						is_training
													lowerCamelCase__	:       Dict					=						use_labels
													lowerCamelCase__	:       Dict					=						vocab_size
													lowerCamelCase__	:       Any					=						hidden_size
													lowerCamelCase__	:       Tuple					=						num_hidden_layers
													lowerCamelCase__	:       Tuple					=						num_attention_heads
													lowerCamelCase__	:       Optional[Any]					=						intermediate_size
													lowerCamelCase__	:       str					=						hidden_act
													lowerCamelCase__	:       Any					=						hidden_dropout_prob
													lowerCamelCase__	:       Dict					=						attention_probs_dropout_prob
													lowerCamelCase__	:       Optional[int]					=						max_position_embeddings
													lowerCamelCase__	:       int					=						eos_token_id
													lowerCamelCase__	:       Tuple					=						pad_token_id
													lowerCamelCase__	:       Union[str, Any]					=						bos_token_id
													lowerCamelCase__	:       List[str]					=						initializer_range





							def 		lowerCAmelCase  (     self							:    List[str] ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size )      ,   3      ,   self.vocab_size )
													lowerCamelCase__	:       Tuple					=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,   dtype=np.intaa ))      ,   -1 )

													lowerCamelCase__	:       int					=						shift_tokens_right(__lowerCamelCase      ,   1      ,   2 )

													lowerCamelCase__	:       Tuple					=						BlenderbotConfig(
													    vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   encoder_layers=self.num_hidden_layers      ,   decoder_layers=self.num_hidden_layers      ,   encoder_attention_heads=self.num_attention_heads      ,   decoder_attention_heads=self.num_attention_heads      ,   encoder_ffn_dim=self.intermediate_size      ,   decoder_ffn_dim=self.intermediate_size      ,   dropout=self.hidden_dropout_prob      ,   attention_dropout=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   eos_token_id=self.eos_token_id      ,   bos_token_id=self.bos_token_id      ,   pad_token_id=self.pad_token_id      ,   initializer_range=self.initializer_range      ,   use_cache=__lowerCamelCase      ,   )
													lowerCamelCase__	:       Tuple					=						prepare_blenderbot_inputs_dict(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
													return config, inputs_dict





							def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						self.prepare_config_and_inputs()
													return config, inputs_dict





							def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple ):


													'''simple docstring'''



													lowerCamelCase__	:       Any					=						20
													lowerCamelCase__	:       Any					=						model_class_name(__lowerCamelCase )

													lowerCamelCase__	:       Tuple					=						model.encode(inputs_dict["input_ids"] )

													lowerCamelCase__	:       int					=						(
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													lowerCamelCase__	:       str					=						model.init_cache(decoder_input_ids.shape[0]      ,   __lowerCamelCase      ,   __lowerCamelCase )
													lowerCamelCase__	:       List[Any]					=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,   dtype="i4" )

													lowerCamelCase__	:       Dict					=						jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]      ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,   )
													lowerCamelCase__	:       Dict					=						model.decode(
													    decoder_input_ids[:, :-1]      ,   __lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase      ,   past_key_values=__lowerCamelCase      ,   decoder_position_ids=__lowerCamelCase      ,   )

													lowerCamelCase__	:       str					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,   dtype="i4" )
													lowerCamelCase__	:       Union[str, Any]					=						model.decode(
													    decoder_input_ids[:, -1:]      ,   __lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase      ,   past_key_values=outputs_cache.past_key_values      ,   decoder_position_ids=__lowerCamelCase      ,   )

													lowerCamelCase__	:       Dict					=						model.decode(__lowerCamelCase      ,   __lowerCamelCase )

													lowerCamelCase__	:       str					=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
													self.parent.assertTrue(diff < 1E-3      ,   msg=f"Max diff is {diff}" )





							def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       List[Any]					=						20
													lowerCamelCase__	:       Optional[int]					=						model_class_name(__lowerCamelCase )

													lowerCamelCase__	:       List[str]					=						model.encode(inputs_dict["input_ids"] )

													lowerCamelCase__	:       int					=						(
													    inputs_dict["decoder_input_ids"],
													    inputs_dict["decoder_attention_mask"],
													)

													lowerCamelCase__	:       Optional[Any]					=						jnp.concatenate(
													    [
													        decoder_attention_mask,
													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
													    ]      ,   axis=-1      ,   )

													lowerCamelCase__	:       Tuple					=						model.init_cache(decoder_input_ids.shape[0]      ,   __lowerCamelCase      ,   __lowerCamelCase )
													lowerCamelCase__	:       Dict					=						jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]      ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,   )

													lowerCamelCase__	:       Optional[int]					=						model.decode(
													    decoder_input_ids[:, :-1]      ,   __lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase      ,   past_key_values=__lowerCamelCase      ,   decoder_position_ids=__lowerCamelCase      ,   )
													lowerCamelCase__	:       Dict					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,   dtype="i4" )
													lowerCamelCase__	:       Dict					=						model.decode(
													    decoder_input_ids[:, -1:]      ,   __lowerCamelCase      ,   past_key_values=outputs_cache.past_key_values      ,   decoder_attention_mask=__lowerCamelCase      ,   decoder_position_ids=__lowerCamelCase      ,   )

													lowerCamelCase__	:       Dict					=						model.decode(__lowerCamelCase      ,   __lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase )

													lowerCamelCase__	:       Tuple					=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
													self.parent.assertTrue(diff < 1E-3      ,   msg=f"Max diff is {diff}" )







@require_flax
class     _lowercase				(							unittest.TestCase):


							"""simple docstring"""




							A__      		=    99





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       Optional[Any]					=						np.array(
													    [
													        [71, 82, 18, 33, 46, 91, 2],
													        [68, 34, 26, 58, 30, 82, 2],
													        [5, 97, 17, 39, 94, 40, 2],
													        [76, 83, 94, 25, 70, 78, 2],
													        [87, 59, 41, 35, 48, 66, 2],
													        [55, 13, 16, 58, 5, 2, 1],  # note padding
													        [64, 27, 31, 51, 12, 75, 2],
													        [52, 64, 86, 17, 83, 39, 2],
													        [48, 61, 9, 24, 71, 82, 2],
													        [26, 1, 60, 48, 22, 13, 2],
													        [21, 5, 62, 28, 14, 76, 2],
													        [45, 98, 37, 86, 59, 48, 2],
													        [70, 70, 50, 9, 28, 0, 2],
													    ]      ,   dtype=np.intaa      ,   )

													lowerCamelCase__	:       Optional[Any]					=						input_ids.shape[0]
													lowerCamelCase__	:       str					=						BlenderbotConfig(
													    vocab_size=self.vocab_size      ,   d_model=24      ,   encoder_layers=2      ,   decoder_layers=2      ,   encoder_attention_heads=2      ,   decoder_attention_heads=2      ,   encoder_ffn_dim=32      ,   decoder_ffn_dim=32      ,   max_position_embeddings=48      ,   eos_token_id=2      ,   pad_token_id=1      ,   bos_token_id=0      ,   )
													return config, input_ids, batch_size





							def 		lowerCAmelCase  (     self							:    Tuple ):


													'''simple docstring'''



													lowerCamelCase__	:       List[str]					=						self._get_config_and_data()
													lowerCamelCase__	:       Optional[Any]					=						FlaxBlenderbotForConditionalGeneration(__lowerCamelCase )
													lowerCamelCase__	:       Any					=						lm_model(input_ids=__lowerCamelCase )
													lowerCamelCase__	:       str					=						(batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs["logits"].shape      ,   __lowerCamelCase )





							def 		lowerCAmelCase  (     self							:    Tuple ):


													'''simple docstring'''



													lowerCamelCase__	:       str					=						BlenderbotConfig(
													    vocab_size=self.vocab_size      ,   d_model=14      ,   encoder_layers=2      ,   decoder_layers=2      ,   encoder_attention_heads=2      ,   decoder_attention_heads=2      ,   encoder_ffn_dim=8      ,   decoder_ffn_dim=8      ,   max_position_embeddings=48      ,   )
													lowerCamelCase__	:       Optional[int]					=						FlaxBlenderbotForConditionalGeneration(__lowerCamelCase )
													lowerCamelCase__	:       str					=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,   dtype=np.intaa )
													lowerCamelCase__	:       List[str]					=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,   dtype=np.intaa )
													lowerCamelCase__	:       int					=						lm_model(input_ids=__lowerCamelCase      ,   decoder_input_ids=__lowerCamelCase )
													lowerCamelCase__	:       List[Any]					=						(*summary.shape, config.vocab_size)
													self.assertEqual(outputs["logits"].shape      ,   __lowerCamelCase )





							def 		lowerCAmelCase  (     self							:    Optional[int] ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,   dtype=np.intaa )
													lowerCamelCase__	:       int					=						shift_tokens_right(__lowerCamelCase      ,   1      ,   2 )
													lowerCamelCase__	:       List[Any]					=						np.equal(__lowerCamelCase      ,   1 ).astype(np.floataa ).sum()
													lowerCamelCase__	:       Dict					=						np.equal(__lowerCamelCase      ,   1 ).astype(np.floataa ).sum()
													self.assertEqual(shifted.shape      ,   input_ids.shape )
													self.assertEqual(__lowerCamelCase      ,   n_pad_before - 1 )
													self.assertTrue(np.equal(shifted[:, 0]      ,   2 ).all() )







@require_flax
class     _lowercase				(							lowercase__							,						unittest.TestCase							,						lowercase__):


							"""simple docstring"""




							A__      		=    True
							A__      		=    (
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							A__      		=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





							def 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						FlaxBlenderbotModelTester(self )





							def 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



													lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )





							def 		lowerCAmelCase  (     self							:    List[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       int					=						self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )





							def 		lowerCAmelCase  (     self							:    Optional[int] ):


													'''simple docstring'''



													lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																									lowerCamelCase__	:       Any					=						self._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase )
																									lowerCamelCase__	:       Union[str, Any]					=						model_class(__lowerCamelCase )

																									@jax.jit
																									def encode_jitted(__lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Tuple=None      ,   **__lowerCamelCase							:    int ):
																															return model.encode(input_ids=__lowerCamelCase      ,   attention_mask=__lowerCamelCase )

																									with self.subTest("JIT Enabled" ):
																															lowerCamelCase__	:       Optional[Any]					=						encode_jitted(**__lowerCamelCase ).to_tuple()

																									with self.subTest("JIT Disabled" ):
																															with jax.disable_jit():
																																					lowerCamelCase__	:       List[Any]					=						encode_jitted(**__lowerCamelCase ).to_tuple()

																									self.assertEqual(len(__lowerCamelCase )      ,   len(__lowerCamelCase ) )
																									for jitted_output, output in zip(__lowerCamelCase      ,   __lowerCamelCase ):
																															self.assertEqual(jitted_output.shape      ,   output.shape )





							def 		lowerCAmelCase  (     self							:    Optional[int] ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																									lowerCamelCase__	:       Union[str, Any]					=						model_class(__lowerCamelCase )
																									lowerCamelCase__	:       str					=						model.encode(inputs_dict["input_ids"]      ,   inputs_dict["attention_mask"] )

																									lowerCamelCase__	:       List[Any]					=						{
																									    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																									    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																									    "encoder_outputs": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(__lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    str ):
																															return model.decode(
																															    decoder_input_ids=__lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase      ,   encoder_outputs=__lowerCamelCase      ,   )

																									with self.subTest("JIT Enabled" ):
																															lowerCamelCase__	:       Optional[int]					=						decode_jitted(**__lowerCamelCase ).to_tuple()

																									with self.subTest("JIT Disabled" ):
																															with jax.disable_jit():
																																					lowerCamelCase__	:       Any					=						decode_jitted(**__lowerCamelCase ).to_tuple()

																									self.assertEqual(len(__lowerCamelCase )      ,   len(__lowerCamelCase ) )
																									for jitted_output, output in zip(__lowerCamelCase      ,   __lowerCamelCase ):
																															self.assertEqual(jitted_output.shape      ,   output.shape )





							@slow
							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													for model_class_name in self.all_model_classes:
																			lowerCamelCase__	:       str					=						model_class_name.from_pretrained("facebook/blenderbot-400M-distill" )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			lowerCamelCase__	:       int					=						np.ones((1, 1) ) * model.config.eos_token_id
																			lowerCamelCase__	:       Optional[int]					=						model(__lowerCamelCase )
																			self.assertIsNotNone(__lowerCamelCase )





							@unittest.skipUnless(jax_device != "cpu"      ,   "3B test too slow on CPU." )
							@slow
							def 		lowerCAmelCase  (     self							:    List[Any] ):


													'''simple docstring'''



													lowerCamelCase__	:       List[Any]					=						{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
													lowerCamelCase__	:       Any					=						{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

													lowerCamelCase__	:       Dict					=						FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"      ,   from_pt=__lowerCamelCase )
													lowerCamelCase__	:       Dict					=						BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B" )

													lowerCamelCase__	:       Dict					=						["Sam"]
													lowerCamelCase__	:       List[Any]					=						tokenizer(__lowerCamelCase      ,   return_tensors="jax" )

													lowerCamelCase__	:       Any					=						model.generate(**__lowerCamelCase      ,   **__lowerCamelCase )
													lowerCamelCase__	:       List[Any]					=						"Sam is a great name. It means \"sun\" in Gaelic."

													lowerCamelCase__	:       int					=						tokenizer.batch_decode(__lowerCamelCase      ,   **__lowerCamelCase )
													assert generated_txt[0].strip() == tgt_text






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


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


