

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
	lowerCAmelCase__						 =			'''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__				(						SCREAMING_SNAKE_CASE_:				List[str]						,							SCREAMING_SNAKE_CASE_:				List[str]						,							SCREAMING_SNAKE_CASE_:				List[Any]=None						,							SCREAMING_SNAKE_CASE_:				Dict=None						,							SCREAMING_SNAKE_CASE_:				Optional[Any]=None						,							SCREAMING_SNAKE_CASE_:				Dict=None						,							SCREAMING_SNAKE_CASE_:				str=None						,							SCREAMING_SNAKE_CASE_:				Optional[int]=None						,							)      ->   Union[str, Any]:




				'''simple docstring'''





				if attention_mask is None:
								A__          =      np.where(input_ids != config.pad_token_id						,							1						,							0   )
				if decoder_attention_mask is None:
								A__          =      np.where(decoder_input_ids != config.pad_token_id						,							1						,							0   )
				if head_mask is None:
								A__          =      np.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								A__          =      np.ones((config.decoder_layers, config.decoder_attention_heads)   )
				if cross_attn_head_mask is None:
								A__          =      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__    :
			"""simple docstring"""







			def __init__(     self   ,   lowercase   ,   lowercase=13   ,   lowercase=7   ,   lowercase=True   ,   lowercase=False   ,   lowercase=99   ,   lowercase=16   ,   lowercase=2   ,   lowercase=4   ,   lowercase=4   ,   lowercase="gelu"   ,   lowercase=0.1   ,   lowercase=0.1   ,   lowercase=32   ,   lowercase=2   ,   lowercase=1   ,   lowercase=0   ,   lowercase=0.02   ,   )	->    Any:
							'''simple docstring'''






							A__          =      parent
							A__          =      batch_size
							A__          =      seq_length
							A__          =      is_training
							A__          =      use_labels
							A__          =      vocab_size
							A__          =      hidden_size
							A__          =      num_hidden_layers
							A__          =      num_attention_heads
							A__          =      intermediate_size
							A__          =      hidden_act
							A__          =      hidden_dropout_prob
							A__          =      attention_probs_dropout_prob
							A__          =      max_position_embeddings
							A__          =      eos_token_id
							A__          =      pad_token_id
							A__          =      bos_token_id
							A__          =      initializer_range


			def   UpperCamelCase		(     self							)	->    str:
							'''simple docstring'''






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

							A__          =      shift_tokens_right(lowercase   ,   1   ,   2							)

							A__          =      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=lowercase   ,   )
							A__          =      prepare_blenderbot_inputs_dict(lowercase   ,   lowercase   ,   lowercase							)
							return config, inputs_dict


			def   UpperCamelCase		(     self							)	->    Tuple:
							'''simple docstring'''






							A__     ,					A__          =      self.prepare_config_and_inputs()
							return config, inputs_dict


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Optional[Any]:
							'''simple docstring'''






							A__          =      20
							A__          =      model_class_name(lowercase							)

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

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

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

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

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

							A__          =      model.decode(lowercase   ,   lowercase							)

							A__          =      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   UpperCamelCase		(     self   ,   lowercase   ,   lowercase   ,   lowercase							)	->    Dict:
							'''simple docstring'''






							A__          =      20
							A__          =      model_class_name(lowercase							)

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

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

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

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

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

							A__          =      model.decode(lowercase   ,   lowercase   ,   decoder_attention_mask=lowercase							)

							A__          =      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      ):
			"""simple docstring"""







			__lowerCamelCase										=							99


			def   UpperCamelCase		(     self							)	->    Tuple:
							'''simple docstring'''






							A__          =      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   ,   )

							A__          =      input_ids.shape[0]
							A__          =      BlenderbotSmallConfig(
							    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   UpperCamelCase		(     self							)	->    List[str]:
							'''simple docstring'''






							A__     ,					A__     ,					A__          =      self._get_config_and_data()
							A__          =      FlaxBlenderbotSmallForConditionalGeneration(lowercase							)
							A__          =      lm_model(input_ids=lowercase							)
							A__          =      (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape   ,   lowercase							)


			def   UpperCamelCase		(     self							)	->    Any:
							'''simple docstring'''






							A__          =      BlenderbotSmallConfig(
							    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   ,   )
							A__          =      FlaxBlenderbotSmallForConditionalGeneration(lowercase							)
							A__          =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,   dtype=np.intaa							)
							A__          =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,   dtype=np.intaa							)
							A__          =      lm_model(input_ids=lowercase   ,   decoder_input_ids=lowercase							)
							A__          =      (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape   ,   lowercase							)


			def   UpperCamelCase		(     self							)	->    Tuple:
							'''simple docstring'''






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

@require_flax
class        a__    (       snake_case				,						unittest.TestCase				,						snake_case      ):
			"""simple docstring"""







			__lowerCamelCase										=							True
			__lowerCamelCase										=							(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			__lowerCamelCase										=							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


			def   UpperCamelCase		(     self							)	->    List[str]:
							'''simple docstring'''






							A__          =      FlaxBlenderbotSmallModelTester(self							)


			def   UpperCamelCase		(     self							)	->    Dict:
							'''simple docstring'''






							A__     ,					A__          =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowercase   ,   lowercase   ,   lowercase							)


			def   UpperCamelCase		(     self							)	->    Union[str, Any]:
							'''simple docstring'''






							A__     ,					A__          =      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(lowercase   ,   lowercase   ,   lowercase							)


			def   UpperCamelCase		(     self							)	->    Any:
							'''simple docstring'''






							A__     ,					A__          =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__							):
															A__          =      self._prepare_for_class(lowercase   ,   lowercase							)
															A__          =      model_class(lowercase							)

															@jax.jit
															def encode_jitted(lowercase   ,   lowercase=None   ,   **lowercase							):
																			return model.encode(input_ids=lowercase   ,   attention_mask=lowercase							)

															with self.subTest("JIT Enabled"							):
																			A__          =      encode_jitted(**lowercase							).to_tuple()

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

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


			def   UpperCamelCase		(     self							)	->    Any:
							'''simple docstring'''






							A__     ,					A__          =      self.model_tester.prepare_config_and_inputs_for_common()

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

															A__          =      {
															    "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(lowercase   ,   lowercase   ,   lowercase							):
																			return model.decode(
																			    decoder_input_ids=lowercase   ,   decoder_attention_mask=lowercase   ,   encoder_outputs=lowercase   ,   )

															with self.subTest("JIT Enabled"							):
																			A__          =      decode_jitted(**lowercase							).to_tuple()

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

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


			@slow
			def   UpperCamelCase		(     self							)	->    Optional[int]:
							'''simple docstring'''






							for model_class_name in self.all_model_classes:
											A__          =      model_class_name.from_pretrained("facebook/blenderbot_small-90M"							)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											A__          =      np.ones((1, 1)							) * model.config.eos_token_id
											A__          =      model(lowercase							)
											self.assertIsNotNone(lowercase							)





'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

							if tensor.shape == tf.TensorShape(UpperCamelCase_ ):
														return dynamic

							A_			    =		tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_ )]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														A_			    =		[1] * inputs.shape.rank
														A_			    =		shape_list(UpperCamelCase_ )[axis]
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = "input_ids" ):
							tf.debugging.assert_less(
							    UpperCamelCase_						,  tf.cast(UpperCamelCase_						,  dtype=tensor.dtype )						,  message=(
							        f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_ )}) must be smaller than the embedding "
							        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
							    )						,  )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
														    f"bytes: {bad_attributes}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





