




import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(__snake_case				)
							__lowercase      =    flatten_dict(__snake_case				)
							return flax_params







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Optional[Any]:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(__snake_case				,       __snake_case				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(__snake_case				,       __snake_case				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       __snake_case				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       __snake_case				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			int=False				,       UpperCamelCase__							:			Optional[Any]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(__snake_case				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=__snake_case				)

							__lowercase      =    PixaStructForConditionalGeneration(__snake_case				)

							__lowercase      =    rename_and_convert_flax_params(__snake_case				)
							model.load_state_dict(__snake_case				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=__snake_case				,       tokenizer=__snake_case				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(__snake_case				,       exist_ok=__snake_case				)

							model.save_pretrained(__snake_case				)
							processor.save_pretrained(__snake_case				)

							print('''Model saved in {}'''.format(__snake_case				)				)


if __name__ == "__main__":
			UpperCAmelCase__     :   List[str]				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__     :   Optional[int]				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)












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
			UpperCAmelCase__				     = "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 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Dict=None				,       UpperCamelCase__							:			Optional[int]=None				,       UpperCamelCase__							:			int=None				,       UpperCamelCase__							:			Union[str, Any]=None				,       UpperCamelCase__							:			Optional[Any]=None				,       UpperCamelCase__							:			List[str]=None				,       )    -> int:
							'''simple docstring'''


							if attention_mask is None:
														__lowercase      =    np.where(input_ids != config.pad_token_id				,       1				,       0				)
							if decoder_attention_mask is None:
														__lowercase      =    np.where(decoder_input_ids != config.pad_token_id				,       1				,       0				)
							if head_mask is None:
														__lowercase      =    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														__lowercase      =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														__lowercase      =    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 :     Dict				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Union[str, Any]=13				,							lowerCamelCase__ :     Union[str, Any]=7				,							lowerCamelCase__ :     Dict=True				,							lowerCamelCase__ :     int=False				,							lowerCamelCase__ :     Any=99				,							lowerCamelCase__ :     str=16				,							lowerCamelCase__ :     Union[str, Any]=2				,							lowerCamelCase__ :     Union[str, Any]=4				,							lowerCamelCase__ :     Tuple=4				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     Any=32				,							lowerCamelCase__ :     List[str]=2				,							lowerCamelCase__ :     Optional[Any]=1				,							lowerCamelCase__ :     List[Any]=0				,							lowerCamelCase__ :     int=0.0_2				,							)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     int				)    ->						Any:
													"""simple docstring"""


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

													__lowercase      =    shift_tokens_right(lowerCamelCase__				,							1				,							2				)

													__lowercase      =    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__				,							)
													__lowercase      =    prepare_blenderbot_inputs_dict(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													return config, inputs_dict






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    20
													__lowercase      =    model_class_name(lowerCamelCase__				)

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

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

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

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

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

													__lowercase      =    model.decode(lowerCamelCase__				,							lowerCamelCase__				)

													__lowercase      =    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				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    20
													__lowercase      =    model_class_name(lowerCamelCase__				)

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

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

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

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

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

													__lowercase      =    model.decode(lowerCamelCase__				,							lowerCamelCase__				,							decoder_attention_mask=lowerCamelCase__				)

													__lowercase      =    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_	:       List[Any]       	=		99






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    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				,							)

													__lowercase      =    input_ids.shape[0]
													__lowercase      =    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 :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase     ,     __lowercase     ,     __lowercase      =    self._get_config_and_data()
													__lowercase      =    FlaxBlenderbotForConditionalGeneration(lowerCamelCase__				)
													__lowercase      =    lm_model(input_ids=lowerCamelCase__				)
													__lowercase      =    (batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs['''logits'''].shape				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,							dtype=np.intaa				)
													__lowercase      =    shift_tokens_right(lowerCamelCase__				,							1				,							2				)
													__lowercase      =    np.equal(lowerCamelCase__				,							1				).astype(np.floataa				).sum()
													__lowercase      =    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 a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase       ,						__SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[Any]       	=		True
						UpperCamelCase_	:       List[str]       	=		(
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase_	:       Union[str, Any]       	=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    FlaxBlenderbotModelTester(self				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						List[str]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    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     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    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     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__				):
																											__lowercase      =    self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)
																											__lowercase      =    model_class(lowerCamelCase__				)

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

																											with self.subTest('''JIT Enabled'''				):
																																		__lowercase      =    encode_jitted(**lowerCamelCase__				).to_tuple()

																											with self.subTest('''JIT Disabled'''				):
																																		with jax.disable_jit():
																																									__lowercase      =    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     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

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

																											__lowercase      =    {
																											    '''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__ :     List[str]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				):
																																		return model.decode(
																																		    decoder_input_ids=lowerCamelCase__				,							decoder_attention_mask=lowerCamelCase__				,							encoder_outputs=lowerCamelCase__				,							)

																											with self.subTest('''JIT Enabled'''				):
																																		__lowercase      =    decode_jitted(**lowerCamelCase__				).to_tuple()

																											with self.subTest('''JIT Disabled'''				):
																																		with jax.disable_jit():
																																									__lowercase      =    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     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													for model_class_name in self.all_model_classes:
																				__lowercase      =    model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
																				# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																				__lowercase      =    np.ones((1, 1)				) * model.config.eos_token_id
																				__lowercase      =    model(lowerCamelCase__				)
																				self.assertIsNotNone(lowerCamelCase__				)






						@unittest.skipUnless(jax_device != '''cpu'''				,							'''3B test too slow on CPU.'''				)
						@slow
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
													__lowercase      =    {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

													__lowercase      =    ['''Sam''']
													__lowercase      =    tokenizer(lowerCamelCase__				,							return_tensors='''jax'''				)

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

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






