



"""simple docstring"""


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
				snake_case_				     =       """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  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	):
						if attention_mask is None:
												UpperCAmelCase             = np.where(input_ids != config.pad_token_id		,	1		,	0						)
						if decoder_attention_mask is None:
												UpperCAmelCase             = np.where(decoder_input_ids != config.pad_token_id		,	1		,	0						)
						if head_mask is None:
												UpperCAmelCase             = np.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												UpperCAmelCase             = np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												UpperCAmelCase             = 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 :Optional[int]   ,  lowercase_ :Optional[int]   ,  lowercase_ :List[str]=13   ,  lowercase_ :Optional[int]=7   ,  lowercase_ :Dict=True   ,  lowercase_ :Union[str, Any]=False   ,  lowercase_ :Optional[int]=99   ,  lowercase_ :Dict=16   ,  lowercase_ :List[Any]=2   ,  lowercase_ :str=4   ,  lowercase_ :Any=4   ,  lowercase_ :str="gelu"   ,  lowercase_ :Optional[int]=0.1   ,  lowercase_ :int=0.1   ,  lowercase_ :Tuple=32   ,  lowercase_ :Tuple=2   ,  lowercase_ :List[str]=1   ,  lowercase_ :Dict=0   ,  lowercase_ :Optional[int]=0.02   ,  )				->			List[str]:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size
												UpperCAmelCase             = hidden_act
												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = max_position_embeddings
												UpperCAmelCase             = eos_token_id
												UpperCAmelCase             = pad_token_id
												UpperCAmelCase             = bos_token_id
												UpperCAmelCase             = initializer_range






						def 	UpperCAmelCase__     (					self :int					)				->			Dict:
												UpperCAmelCase             = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,  self.vocab_size					)   ,  3   ,  self.vocab_size					)
												UpperCAmelCase             = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,  dtype=np.intaa					))   ,  -1					)

												UpperCAmelCase             = shift_tokens_right(lowercase_   ,  1   ,  2					)

												UpperCAmelCase             = 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=lowercase_   ,  )
												UpperCAmelCase             = prepare_blenderbot_inputs_dict(lowercase_   ,  lowercase_   ,  lowercase_					)
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :str					)				->			Any:
												UpperCAmelCase      ,    UpperCAmelCase             = self.prepare_config_and_inputs()
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :int   ,  lowercase_ :int   ,  lowercase_ :str   ,  lowercase_ :int					)				->			Any:
												UpperCAmelCase             = 20
												UpperCAmelCase             = model_class_name(lowercase_					)

												UpperCAmelCase             = model.encode(inputs_dict['input_ids']					)

												UpperCAmelCase      ,    UpperCAmelCase             = (
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												UpperCAmelCase             = model.init_cache(decoder_input_ids.shape[0]   ,  lowercase_   ,  lowercase_					)
												UpperCAmelCase             = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,  dtype='i4'					)

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

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

												UpperCAmelCase             = model.decode(lowercase_   ,  lowercase_					)

												UpperCAmelCase             = 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 :int   ,  lowercase_ :Any   ,  lowercase_ :List[str]   ,  lowercase_ :Any					)				->			Dict:
												UpperCAmelCase             = 20
												UpperCAmelCase             = model_class_name(lowercase_					)

												UpperCAmelCase             = model.encode(inputs_dict['input_ids']					)

												UpperCAmelCase      ,    UpperCAmelCase             = (
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

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

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

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

												UpperCAmelCase             = model.decode(lowercase_   ,  lowercase_   ,  decoder_attention_mask=lowercase_					)

												UpperCAmelCase             = 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"""
						__UpperCamelCase						=							99






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			Any:
												UpperCAmelCase             = 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   ,  )

												UpperCAmelCase             = input_ids.shape[0]
												UpperCAmelCase             = 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 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			Any:
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = self._get_config_and_data()
												UpperCAmelCase             = FlaxBlenderbotForConditionalGeneration(lowercase_					)
												UpperCAmelCase             = lm_model(input_ids=lowercase_					)
												UpperCAmelCase             = (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['logits'].shape   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Optional[int]:
												UpperCAmelCase             = 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   ,  )
												UpperCAmelCase             = FlaxBlenderbotForConditionalGeneration(lowercase_					)
												UpperCAmelCase             = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,  dtype=np.intaa					)
												UpperCAmelCase             = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,  dtype=np.intaa					)
												UpperCAmelCase             = lm_model(input_ids=lowercase_   ,  decoder_input_ids=lowercase_					)
												UpperCAmelCase             = (*summary.shape, config.vocab_size)
												self.assertEqual(outputs['logits'].shape   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :str					)				->			List[Any]:
												UpperCAmelCase             = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,  dtype=np.intaa					)
												UpperCAmelCase             = shift_tokens_right(lowercase_   ,  1   ,  2					)
												UpperCAmelCase             = np.equal(lowercase_   ,  1					).astype(np.floataa					).sum()
												UpperCAmelCase             = 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_			(     SCREAMING_SNAKE_CASE_	,   unittest.TestCase	,   SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							True
						__UpperCamelCase						=							(
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						__UpperCamelCase						=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[int]:
												UpperCAmelCase             = FlaxBlenderbotModelTester(self					)






						def 	UpperCAmelCase__     (					self :Dict					)				->			Dict:
												UpperCAmelCase      ,    UpperCAmelCase             = 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 :Tuple					)				->			Dict:
												UpperCAmelCase      ,    UpperCAmelCase             = 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 :int					)				->			Optional[Any]:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__					):
																								UpperCAmelCase             = self._prepare_for_class(lowercase_   ,  lowercase_					)
																								UpperCAmelCase             = model_class(lowercase_					)

																								@jax.jit
																								def encode_jitted(lowercase_ :Dict   ,  lowercase_ :Any=None   ,  **lowercase_ :Optional[int]					):
																														return model.encode(input_ids=lowercase_   ,  attention_mask=lowercase_					)

																								with self.subTest('JIT Enabled'					):
																														UpperCAmelCase             = encode_jitted(**lowercase_					).to_tuple()

																								with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																				UpperCAmelCase             = 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 :str					)				->			Tuple:
												UpperCAmelCase      ,    UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__					):
																								UpperCAmelCase             = model_class(lowercase_					)
																								UpperCAmelCase             = model.encode(inputs_dict['input_ids']   ,  inputs_dict['attention_mask']					)

																								UpperCAmelCase             = {
																								    '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_ :str   ,  lowercase_ :str   ,  lowercase_ :Union[str, Any]					):
																														return model.decode(
																														    decoder_input_ids=lowercase_   ,  decoder_attention_mask=lowercase_   ,  encoder_outputs=lowercase_   ,  )

																								with self.subTest('JIT Enabled'					):
																														UpperCAmelCase             = decode_jitted(**lowercase_					).to_tuple()

																								with self.subTest('JIT Disabled'					):
																														with jax.disable_jit():
																																				UpperCAmelCase             = 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 :List[Any]					)				->			Optional[Any]:
												for model_class_name in self.all_model_classes:
																		UpperCAmelCase             = model_class_name.from_pretrained('facebook/blenderbot-400M-distill'					)
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		UpperCAmelCase             = np.ones((1, 1)					) * model.config.eos_token_id
																		UpperCAmelCase             = model(lowercase_					)
																		self.assertIsNotNone(lowercase_					)






						@unittest.skipUnless(jax_device != 'cpu'   ,  '3B test too slow on CPU.'					)
						@slow
						def 	UpperCAmelCase__     (					self :Union[str, Any]					)				->			List[Any]:
												UpperCAmelCase             = {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
												UpperCAmelCase             = {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

												UpperCAmelCase             = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'   ,  from_pt=lowercase_					)
												UpperCAmelCase             = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'					)

												UpperCAmelCase             = ['Sam']
												UpperCAmelCase             = tokenizer(lowercase_   ,  return_tensors='jax'					)

												UpperCAmelCase             = model.generate(**lowercase_   ,  **lowercase_					)
												UpperCAmelCase             = 'Sam is a great name. It means "sun" in Gaelic.'

												UpperCAmelCase             = tokenizer.batch_decode(lowercase_   ,  **lowercase_					)
												assert generated_txt[0].strip() == tgt_text







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_lowercase :  Optional[int]    							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



