
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   int							)	->     Optional[int]:
							if isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														lowerCamelCase_					=       np.full((len(UpperCAmelCase__							), sequence_length, 2)      ,					UpperCAmelCase__							)
							else:
														lowerCamelCase_					=       np.full((len(UpperCAmelCase__							), sequence_length)      ,					UpperCAmelCase__							)

							for i, tensor in enumerate(UpperCAmelCase__							):
														if padding_side == "right":
																					if isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
																												lowerCamelCase_					=       tensor[:sequence_length]
																					else:
																												lowerCamelCase_					=       tensor[:sequence_length]
														else:
																					if isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
																												lowerCamelCase_					=       tensor[:sequence_length]
																					else:
																												lowerCamelCase_					=       tensor[:sequence_length]

							return out_tensor.tolist()



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     int:
							lowerCamelCase_					=       ord(UpperCAmelCase__							)
							if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
														return True
							lowerCamelCase_					=       unicodedata.category(UpperCAmelCase__							)
							if cat.startswith("""P"""							):
														return True
							return False





@dataclass
class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     None
					SCREAMING_SNAKE_CASE           =     None
					SCREAMING_SNAKE_CASE           =     -1_0_0
					SCREAMING_SNAKE_CASE           =     "pt"

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Dict ):
												import torch

												lowerCamelCase_					=       """label""" if """label""" in features[0].keys() else """labels"""
												lowerCamelCase_					=       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
												lowerCamelCase_					=       self.tokenizer.pad(
												    __UpperCamelCase ,       padding=self.padding ,       max_length=self.max_length ,       pad_to_multiple_of=self.pad_to_multiple_of ,       return_tensors="""pt""" if labels is None else None ,       )

												if labels is None:
																			return batch

												lowerCamelCase_					=       torch.tensor(batch["""entity_ids"""] ).shape[1]
												lowerCamelCase_					=       self.tokenizer.padding_side
												if padding_side == "right":
																			lowerCamelCase_					=       [
																			    list(__UpperCamelCase ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) for label in labels
																			]
												else:
																			lowerCamelCase_					=       [
																			    [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase )) + list(__UpperCamelCase ) for label in labels
																			]

												lowerCamelCase_					=       [feature["""ner_tags"""] for feature in features]
												lowerCamelCase_					=       padding_tensor(__UpperCamelCase ,       -1 ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       [feature["""original_entity_spans"""] for feature in features]
												lowerCamelCase_					=       padding_tensor(__UpperCamelCase ,       (-1, -1) ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       {k: torch.tensor(__UpperCamelCase ,       dtype=torch.intaa ) for k, v in batch.items()}

												return batch







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
		lowercase   =	'''platform'''

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Any=None      ,					UpperCAmelCase__					:   Optional[Any]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Dict=None      ,					)	->     List[Any]:
							if attention_mask is None:
														lowerCamelCase_					=       np.where(input_ids != config.pad_token_id      ,					1      ,					0							)
							if decoder_attention_mask is None:
														lowerCamelCase_					=       np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0							)
							if head_mask is None:
														lowerCamelCase_					=       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCamelCase_					=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCamelCase_					=       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    __A:

					def __init__(						self       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  str=1_3 ,       __UpperCamelCase       :  List[str]=7 ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  List[Any]=False ,       __UpperCamelCase       :  Dict=9_9 ,       __UpperCamelCase       :  Tuple=1_6 ,       __UpperCamelCase       :  Any=2 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Optional[Any]="gelu" ,       __UpperCamelCase       :  List[str]=0.1 ,       __UpperCamelCase       :  str=0.1 ,       __UpperCamelCase       :  Union[str, Any]=3_2 ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  Tuple=1 ,       __UpperCamelCase       :  Optional[Any]=0 ,       __UpperCamelCase       :  str=0.02 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       eos_token_id
												lowerCamelCase_					=       pad_token_id
												lowerCamelCase_					=       bos_token_id
												lowerCamelCase_					=       initializer_range

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size ) ,       3 ,       self.vocab_size )
												lowerCamelCase_					=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,       dtype=np.intaa )) ,       -1 )

												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )

												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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=__UpperCamelCase ,       )
												lowerCamelCase_					=       prepare_blenderbot_inputs_dict(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return config, inputs_dict

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.prepare_config_and_inputs()
												return config, inputs_dict

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												lowerCamelCase_					=       model.init_cache(decoder_input_ids.shape[0] ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4""" )

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

												lowerCamelCase_					=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4""" )
												lowerCamelCase_					=       model.decode(
												    decoder_input_ids[:, -1:] ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       past_key_values=outputs_cache.past_key_values ,       decoder_position_ids=__UpperCamelCase ,       )

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												lowerCamelCase_					=       jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
												    ] ,       axis=-1 ,       )

												lowerCamelCase_					=       model.init_cache(decoder_input_ids.shape[0] ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )

												lowerCamelCase_					=       model.decode(
												    decoder_input_ids[:, :-1] ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       past_key_values=__UpperCamelCase ,       decoder_position_ids=__UpperCamelCase ,       )
												lowerCamelCase_					=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4""" )
												lowerCamelCase_					=       model.decode(
												    decoder_input_ids[:, -1:] ,       __UpperCamelCase ,       past_key_values=outputs_cache.past_key_values ,       decoder_attention_mask=__UpperCamelCase ,       decoder_position_ids=__UpperCamelCase ,       )

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase )

												lowerCamelCase_					=       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    __A(					unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     9_9

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       np.array(
												    [
												        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
												        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
												        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
												        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
												        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
												        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
												        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
												        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
												        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
												        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
												        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
												        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
												        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
												    ] ,       dtype=np.intaa ,       )

												lowerCamelCase_					=       input_ids.shape[0]
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=2_4 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=3_2 ,       decoder_ffn_dim=3_2 ,       max_position_embeddings=4_8 ,       eos_token_id=2 ,       pad_token_id=1 ,       bos_token_id=0 ,       )
												return config, input_ids, batch_size

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self._get_config_and_data()
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=1_4 ,       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=4_8 ,       )
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase ,       decoder_input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,       dtype=np.intaa )
												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape ,       input_ids.shape )
												self.assertEqual(__UpperCamelCase ,       n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0] ,       2 ).all() )





@require_flax
class    __A(					UpperCAmelCase  ,		unittest.TestCase  ,		UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       FlaxBlenderbotSmallModelTester(self )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       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(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase )
																										lowerCamelCase_					=       model_class(__UpperCamelCase )

																										@jax.jit
																										def encode_jitted(__UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict=None ,       **__UpperCamelCase       :  Tuple ):
																																	return model.encode(input_ids=__UpperCamelCase ,       attention_mask=__UpperCamelCase )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

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

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       model_class(__UpperCamelCase )
																										lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] ,       inputs_dict["""attention_mask"""] )

																										lowerCamelCase_					=       {
																										    """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(__UpperCamelCase       :  int ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       encoder_outputs=__UpperCamelCase ,       )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

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



					@slow
					def  lowercase__   (						self       :  str ):
												for model_class_name in self.all_model_classes:
																			lowerCamelCase_					=       model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			lowerCamelCase_					=       np.ones((1, 1) ) * model.config.eos_token_id
																			lowerCamelCase_					=       model(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )





