






"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCamelCase_							      = get_logger(__name__)


lowerCamelCase_							      = Path(__file__).parent / '''model_card_template.md'''
lowerCamelCase_							      = uuida().hex
lowerCamelCase_							      = os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase_							      = os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase_							      = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''

def 							snake_case (  A__ = None		):
			UpperCAmelCase_	:					List[Any]     	=   F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += F"""; torch/{_torch_version}"""
			if is_flax_available():
						ua += F"""; jax/{_jax_version}"""
						ua += F"""; flax/{_flax_version}"""
			if is_onnx_available():
						ua += F"""; onnxruntime/{_onnxruntime_version}"""
			# CI will set this value to True
			if os.environ.get("DIFFUSERS_IS_CI"      ,""		).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(A__      ,A__		):
						ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()		)
			elif isinstance(A__      ,A__		):
						ua += "; " + user_agent
			return ua

def 							snake_case (  A__      ,A__ = None      ,A__ = None		):
			if token is None:
						UpperCAmelCase_	:					str     	=   HfFolder.get_token()
			if organization is None:
						UpperCAmelCase_	:					Optional[Any]     	=   whoami(A__		)["name"]
						return F"""{username}/{model_id}"""
			else:
						return F"""{organization}/{model_id}"""

def 							snake_case (  A__      ,A__		):
			if not is_jinja_available():
						raise ValueError(
						    "Modelcard rendering is based on Jinja templates."
						    " Please make sure to have `jinja` installed before using `create_model_card`."
						    " To install it, please run `pip install Jinja2`."		)

			if hasattr(A__      ,"local_rank"		) and args.local_rank not in [-1, 0]:
						return

			UpperCAmelCase_	:					Tuple     	=   args.hub_token if hasattr(A__      ,"hub_token"		) else None
			UpperCAmelCase_	:					Union[str, Any]     	=   get_full_repo_name(A__      ,token=A__		)

			UpperCAmelCase_	:					Tuple     	=   ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language="en"      ,license="apache-2.0"      ,library_name="diffusers"      ,tags=[]      ,datasets=args.dataset_name      ,metrics=[]      ,)      ,template_path=A__      ,model_name=A__      ,repo_name=A__      ,dataset_name=args.dataset_name if hasattr(A__      ,"dataset_name"		) else None      ,learning_rate=args.learning_rate      ,train_batch_size=args.train_batch_size      ,eval_batch_size=args.eval_batch_size      ,gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(A__      ,"gradient_accumulation_steps"		) else None
			    )      ,adam_betaa=args.adam_betaa if hasattr(A__      ,"adam_beta1"		) else None      ,adam_betaa=args.adam_betaa if hasattr(A__      ,"adam_beta2"		) else None      ,adam_weight_decay=args.adam_weight_decay if hasattr(A__      ,"adam_weight_decay"		) else None      ,adam_epsilon=args.adam_epsilon if hasattr(A__      ,"adam_epsilon"		) else None      ,lr_scheduler=args.lr_scheduler if hasattr(A__      ,"lr_scheduler"		) else None      ,lr_warmup_steps=args.lr_warmup_steps if hasattr(A__      ,"lr_warmup_steps"		) else None      ,ema_inv_gamma=args.ema_inv_gamma if hasattr(A__      ,"ema_inv_gamma"		) else None      ,ema_power=args.ema_power if hasattr(A__      ,"ema_power"		) else None      ,ema_max_decay=args.ema_max_decay if hasattr(A__      ,"ema_max_decay"		) else None      ,mixed_precision=args.mixed_precision      ,)

			UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(args.output_dir      ,"README.md"		)
			model_card.save(A__		)

def 							snake_case (  A__      ,A__ = None		):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			UpperCAmelCase_	:					List[str]     	=   str(Path(A__		).as_posix()		)
			UpperCAmelCase_	:					Tuple     	=   re.search(r"snapshots/([^/]+)/"      ,A__		)
			if search is None:
						return None
			UpperCAmelCase_	:					List[str]     	=   search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(A__		) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCamelCase_							      = os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCamelCase_							      = os.path.join(hf_cache_home, '''diffusers''')

def 							snake_case (  A__ = None      ,A__ = None		):
			if new_cache_dir is None:
						UpperCAmelCase_	:					int     	=   DIFFUSERS_CACHE
			if old_cache_dir is None:
						UpperCAmelCase_	:					Dict     	=   old_diffusers_cache

			UpperCAmelCase_	:					Union[str, Any]     	=   Path(A__		).expanduser()
			UpperCAmelCase_	:					Optional[int]     	=   Path(A__		).expanduser()
			for old_blob_path in old_cache_dir.glob("**/blobs/*"		):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									UpperCAmelCase_	:					Union[str, Any]     	=   new_cache_dir / old_blob_path.relative_to(A__		)
									new_blob_path.parent.mkdir(parents=A__      ,exist_ok=A__		)
									os.replace(A__      ,A__		)
									try:
												os.symlink(A__      ,A__		)
									except OSError:
												logger.warning(
												    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."		)
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCamelCase_							      = os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
			lowerCamelCase_							      = 0
else:
			with open(cache_version_file) as f:
						try:
									lowerCamelCase_							      = int(f.read())
						except ValueError:
									lowerCamelCase_							      = 0

if cache_version < 1:
			lowerCamelCase_							      = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
			if old_cache_is_not_empty:
						logger.warning(
						    '''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
						    '''existing cached models. This is a one-time operation, you can interrupt it or run it '''
						    '''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
						)
						try:
									move_cache()
						except Exception as e:
									lowerCamelCase_							      = '''\n'''.join(traceback.format_tb(e.__traceback__))
									logger.error(
									    f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
									    '''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
									    '''message and we will do our best to help.'''
									)

if cache_version < 1:
			try:
						os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
						with open(cache_version_file, '''w''') as f:
									f.write('''1''')
			except Exception:
						logger.warning(
						    f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
						    '''the directory exists and can be written to.'''
						)

def 							snake_case (  A__      ,A__ = None		):
			if variant is not None:
						UpperCAmelCase_	:					Union[str, Any]     	=   weights_name.split("."		)
						UpperCAmelCase_	:					Union[str, Any]     	=   splits[:-1] + [variant] + splits[-1:]
						UpperCAmelCase_	:					Optional[Any]     	=   ".".join(A__		)

			return weights_name




def 							snake_case (  A__      ,*,
    A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__=None      ,):
			UpperCAmelCase_	:					str     	=   str(A__		)
			if os.path.isfile(A__		):
						return pretrained_model_name_or_path
			elif os.path.isdir(A__		):
						if os.path.isfile(os.path.join(A__      ,A__		)		):
									# Load from a PyTorch checkpoint
									UpperCAmelCase_	:					Dict     	=   os.path.join(A__      ,A__		)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(A__      ,A__      ,A__		)		):
									UpperCAmelCase_	:					str     	=   os.path.join(A__      ,A__      ,A__		)
									return model_file
						else:
									raise EnvironmentError(
									    F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""		)
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(A__		).base_version		) >= version.parse("0.20.0"		)
						):
									try:
												UpperCAmelCase_	:					List[str]     	=   hf_hub_download(
												    A__      ,filename=_add_variant(A__      ,A__		)      ,cache_dir=A__      ,force_download=A__      ,proxies=A__      ,resume_download=A__      ,local_files_only=A__      ,use_auth_token=A__      ,user_agent=A__      ,subfolder=A__      ,revision=revision or commit_hash      ,)
												warnings.warn(
												    F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""      ,A__      ,)
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(A__      ,A__		)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(A__      ,A__		)}' so that the correct variant file can be added."""      ,A__      ,)
						try:
									# 2. Load model file as usual
									UpperCAmelCase_	:					str     	=   hf_hub_download(
									    A__      ,filename=A__      ,cache_dir=A__      ,force_download=A__      ,proxies=A__      ,resume_download=A__      ,local_files_only=A__      ,use_auth_token=A__      ,user_agent=A__      ,subfolder=A__      ,revision=revision or commit_hash      ,)
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
									    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
									    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
									    "login`."		)
						except RevisionNotFoundError:
									raise EnvironmentError(
									    F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
									    "this model name. Check the model page at "
									    F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""		)
						except EntryNotFoundError:
									raise EnvironmentError(
									    F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""		)
						except HTTPError as err:
									raise EnvironmentError(
									    F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""		)
						except ValueError:
									raise EnvironmentError(
									    F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
									    F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
									    F""" directory containing a file named {weights_name} or"""
									    " \nCheckout your internet connection or see how to run the library in"
									    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."		)
						except EnvironmentError:
									raise EnvironmentError(
									    F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
									    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
									    F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
									    F"""containing a file named {weights_name}"""		)





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 )





